Next Article in Journal
Data-Driven Personalized Learning Path Planning Based on Cognitive Diagnostic Assessments in MOOCs
Next Article in Special Issue
An Activity Recognition Framework for Continuous Monitoring of Non-Steady-State Locomotion of Individuals with Parkinson’s Disease
Previous Article in Journal
Design and Development of a Management System for Energy Microgrids Using Linear Programming
Previous Article in Special Issue
Assessing Movement Quality in Youth Footballers: The Relationship between Hip and Lower Limb Movement Screen and Functional Movement Screen
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Applied Sciences
Volume 12
Issue 8
10.3390/app12083981
applsci-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Survey on Video-Based Biomechanics and Biometry Tools for Fracture and Injury Assessment in Sports

by
Vanessa E. Ortiz-Padilla
†,
Mauricio A. Ramírez-Moreno
†,
Gerardo Presbítero-Espinosa
†,
Ricardo A. Ramírez-Mendoza
and
Jorge de J. Lozoya-Santos
*
Mechatronics Department, School of Engineering and Sciences, Tecnologico de Monterrey, Av. Eugenio Garza Sada 2501 Sur, Tecnológico, Monterrey 64849, Mexico
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Appl. Sci. 2022, 12(8), 3981; https://doi.org/10.3390/app12083981
Submission received: 18 February 2022 / Revised: 2 April 2022 / Accepted: 4 April 2022 / Published: 14 April 2022
(This article belongs to the Special Issue Movement Analysis for Health and Biometrics)
Browse Figures
Versions Notes

Abstract

:
This work presents a survey literature review on biomechanics, specifically aimed at the study of existent biomechanical tools through video analysis, in order to identify opportunities for researchers in the field, and discuss future proposals and perspectives. Scientific literature (journal papers and conference proceedings) in the field of video-based biomechanics published after 2010 were selected and discussed. The most common application of the study of biomechanics using this technique is sports, where the most reported applications are american football, soccer, basketball, baseball, jumping, among others. These techniques have also been studied in a less proportion, in ergonomy, and injury prevention. From the revised literature, it is clear that biomechanics studies mainly focus on the analysis of angles, speed or acceleration, however, not many studies explore the dynamical forces in the joints. The development of video-based biomechanic tools for force analysis could provide methods for assessment and prediction of biomechanical force associated risks such as injuries and fractures. Therefore, it is convenient to start exploring this field. A few case studies are reported, where force estimation is performed via manual tracking in different scenarios. This demonstration is carried out using conventional manual tracking, however, the inclusion of similar methods in an automated manner could help in the development of intelligent healthcare, force prediction tools for athletes and/or elderly population. Future trends and challenges in this field are also discussed, where data availability and artificial intelligence models will be key to proposing new and more reliable methods for biomechanical analysis.

1. Introduction

1.1. Biomechanics and Biometry

Biomechanics is the field where the movements of a living body are studied from a mathematical and physics perspective for different purposes, such as ergonomy, healthcare, sports, among others [1]. Research in biomechanics in the sports science field has allowed the development of important applications for improvement in healthcare and human performance [2]. Meanwhile, Biometry refers to the development and implementation of statistical and mathematical methods for data analysis solutions to problems in the biological sciences field [3]. Biomechanics and biometry are similar in the sense that they analyze biological information with mathematical methods to generate a solution. The aforementioned areas have allowed to develop biomechanical tools for digital joint tracking [4], joint-range associated injury prevention and rehabilitation [5], athlete’s technique assessment and correction [6], among others.
For several years, biomechanical studies have assessed the existing risk of developing fractures and injuries generated from high-performance sports activities. Among the types of fractures formed in sports activities, the formation of stress fractures stands out. A stress fracture is defined as a “partial or complete fracture of bone due to its inability to withstand non-visible stress applied in a rhythmic, repeated, sub-threshold manner” [7]. There is not a precisely defined mechanism for the initiation of stress fractures, although it is known that when surrounding muscles become fatigued, they concentrate excessive forces over the zones surrounding the bone, generating microcracks.

1.2. Risk of Fractures and Injuries in Sports

When microcrack formation exceeds bone remodeling, these accumulate and grow towards threshold lengths, which cause the formation of macro fractures consisting of measures approximating 1 mm, to provoke the final rupture through the bone cortex. In high-intensity sports, fatigue injuries compose 20% of the total injuries [8], while in the military, they can be as high as 31% [9]. These fractures locate mainly in the tibia and fibula in long-distance runners [10].
Tibia is the most frequently fractured bone during sports activities, after sustaining five to six times the body weight [11]. The sports where this type of fracture occurs more commonly are classical ballet, aerobics, tennis, basketball, football, volleyball. After tibial shaft (40.3%), the cases of fractures in athletes due to stress include rib (15.8%), metatarsal bone (9.7%), ulnar olecranon (8.2%), pubic bone (5.6%), fibular shaft (4.6%), tibial medial malleolus (4.1%), and other types of bones (11.8%) [12]. In general, ref. [13] reported metatarsal fatigue fractures to consist of 38% of all the bone stress injuries and even 58% in the military.
Furthermore, studies have reported measurement and prevention of the development of stress fractures due to sports activities. Some of these include the analysis of using shock-absorbing insoles, basketball shoes, training modifications, and calcium supplements [14].
In soccer, the metatarsal bone has to be studied, and its risk for developing fatigue injuries. In this regard, refs. [15,16] affirmed these types of fractures included almost 78% of all stress fractures that occurred in a European League from 2000 to 2009, involving refracturing in 27% of the cases, and even not full recovery in 14% of them. Hence, this type of injury can lead to high risks of a career-ending decision [17].
Moreover, several studies exist regarding the involvement of stresses in the fifth metatarsal region towards developing fatigue fractures. Among these, one of the first studies in football involving repetitive loading is presented in [18]. In this work, athletes developed fractures in the fifth metatarsal while performing kicking and cutting movements. Other studies include: plantar pressure influences the risk of injuries [19]; specific action against static motions for detecting discrepancies in lateral foot loadings [20]; plantar forces at the lateral forefoot between healthy and fractured players [21]; more tendency to fracture for the case of a non-preferred foot [22]; explanations regarding static plantar pressure measurements to be not efficient in assessing fatigue injury risks [23]; specific regions within the foot showing higher pressures in the non-preferred feet of football players [24]; longer fifth metatarsal and higher medial longitudinal arch in the case of fractured against healthy players [25], among other studies.
In [26], authors reported analysis regarding the use of deterministic models to study biomechanics to improve movement performance and injury risk reduction. This type of models help to determine the relationship between a movement outcome measurement and the biomechanical parameters involved in such measure. Typically, block diagrams and equations are used to understand the relationship of variables, and provide its numerical representation respectively [26]. Another work makes use of video processing software, with a front and lateral view analysis in stroke to highlight errors towards proper instruction and minimize injury risk [27].
A study focused on the shoulder biomechanics in swimming [28] concluded supraspinatus tendinopathy was the most common cause of shoulder pain, suggesting that a proper evaluation involves the entire kinetic chain. Another relevant study regarding the inclusion of strategies for the prevention and treatment focusing on swimmers’ shoulders is presented in [29]. Other research studies involving swimming biomechanics analysis are included in Table 1.
Given the aforementioned context, it is clear that smart tools for fracture prevention in sports would benefit greatly the field of sports sciences. The same tool could have important impact in elderly healthcare [66]. A plausible solution for this method is the combination of artificial intelligence (AI) and biomechanics. The combination of AI and video-based biomechanics tools could foster the development of intelligent methods for fracture prevention in athletes and/or for the elderly, by predicting risk of fracture via video assessment of body movements [67].
In this study, we focused on the analysis of scientific literature which describes video-based biomechanics tools in different sports. A categorization (see Table 1) was performed on the selected literature based on the sport, software and analysis used, which can be provide researchers in the field a notion of the current state of art. Special attention was given to sports with high-risk of development of fractures and injuries such as football (soccer), and swimming.
Difficulties and challenges for the development of the present work included the absence of existing studies towards the accurate prediction modalities of applied stresses in focused in specific bones and joints with tendencies for fracturing by fatigue. As far as the authors of this work know, this is the first time that it has been shown that it is possible to measure the concentration of stresses in determined body joints from videos. Work was carried out on the establishment of a relatively simple tracking modality to apply, where, from videos on body movements it will be possible to establish variables of considerable importance such as force, acceleration, and position, towards the correct estimation of stresses generated in areas fracture tendency in bones and joints.
This paper opens the possibility of developing determined stress calculations on specific areas where fatigue fractures are possible, such as joints and bones. The case studies presented in this paper show that it is possible to estimate forces, acceleration, and positions by the analysis of body movements through video recordings of athletes in various disciplines. These estimates will allow shortly to establish prediction models for fatigue injuries towards the prevention of fatigue fractures in sports in which specific fatigue fractures are common. A simple modality for tracking movements performed by athletes is reported and will allow soon to estimate the conditions of physical motions towards the correct implementation of training routines, minimizing the possibility of developing fatigue fractures.
Section 2 presents applications and uses in different fields for video-based biomechanics (movement, injury prevention, sports and ergonomy). Section 3 shows three examples (case studies) of the proposed video assessment; then in Section 4 a roadmap of the current worldwide state of the field, and a perspective of future opportunities respectively is presented. Finally, Section 5 closes with the conclusions of the paper.

1.3. Video-Based Biomechanics

Biomechanical analysis can be performed by either video and/or kinematic sensors. In video analysis, 3D and 2D are typically found in the literature, as well as depth detection images [5,68,69]. In these types of procedures, in some cases, optical markers are used to detect different body parts and thus detect their position in real time in a given coordinate frame [70]. Some of the most used sensors in the literature to represent human kinematics are: electromagnetic, acoustic, and inertial sensors, Kinect platform (RGB, depth sensors, IR, accelerometers, and microphones), as well as mobile applications [70]. While different sensors provide useful information, video analysis is typically preferred by non-specialized customers because the outputs of the models, expressed as angles or coordinates which complement the videos, are easier to understand [6].
Video analysis for biomechanical purposes has been increasing in recent years. Depending on the complexity of the desired model, this type of analysis can be performed manually, by manually selecting the positions of relevant body parts at different video frames, or automatically, by involving image recognition/processing algorithms to perform this procedure in an automated manner [71]. Although manual digitizing is not often considered as a difficult process, when analyzing large videos with several frames, it becomes a time-consuming task which could be solved in a more efficient way by an algorithm. Other more simple video analysis have been reported in the literature to assess concussions in Australian football [72] and bicycle accidents [73], however also require manual supervision and thus, are time-consuming.
3D Video analysis involving multiple cameras allow to perform three-dimensional reconstructions of the environment, thus obtaining three-dimensional position, speed, and acceleration measurements in human models. 3D video analysis is usually referred to in biomechanics as the gold standard due to its precision and degree of information provided [6]. However, expensive, and specialized software and equipment are needed to perform this type of analysis. Even more so if the analysis is performed using optical markers.
Biomechanical analysis through 2D video offers a relatively simple and low-cost solution to analyze human motion, as it can be easily performed using cellphones, and low-cost cameras. Hence, making biomechanical analysis feasible to general users outside laboratory-like environments, which makes it appealing for risk assessment and training in sports, medical and industry fields. The downside of video analysis is that proper setup about the angle and environmental lighting of the films need to be considered to obtain reliable measurements. However, many studies in the literature have reported successful biomechanical analyses using 2D video protocols, such as race walking, vertical jump squats, baseball pitching, martial arts [6,68,70,74]. Some of the studies also compare the performance of 2D vs 3D video analysis and report no significant differences between both when using proper filming setups [5,74,75].
Marker-less image processing algorithms offer a solution for automated biomechanical assessment for low-cost video analysis. Some of the reported algorithms include image processing techniques such as model reconstruction from multiple views using silhouette recognition algorithms [71] and 2D stick-like models of the human body based on polynomial regression [76].

1.4. Software

Different softwares are available for biomechanics research. This section describes briefly few softwares commonly used in this field, while a more detailed description can be found in Table 2. An open source tool, Biomechanical ToolKit, based in Python language for biomechanical analysis is presented in [77]. Conventional softwares for Biomechanical analysis include Vicon [78], Kinovea [79], Coach’e eye, Dartfish [4], Tracker [80], and SkillSpector [81]. OpenSim allows its users to create and analyze dynamic simulations of movement [82]. A Matlab package for biomechanical analysis has also been described, such as the BoB (Biomechanics of Bodies) [83]. It is based on m-coding, so it is specific to the Matlab environment. FeBio allows to implement finite element analysis for biomechanics on rigid solids [84].

2. Discipline and Uses

2.1. Movement Studies

Biomechanical analyses have been used in various areas of study, including the study of body movement in postures of specific actions [85], body coordination [86], speed or impact force on diverse regions of the body, among others. The applications of such studies can help to understand better the human body and its movement from a clinical point of view; for example, when a person is subjected to different forces or circumstances, either while walking [87], jogging, running, or notable differences in various diseases or disabilities. It can also be applied in sports, to carry out an extensive analysis of sports techniques [88] and the consideration of certain factors such as weight or height to test the accurate effectiveness at a competitive level [89], or for the design of sports equipment that can meet the true needs of athletes.

2.2. Injury Prevention

Biomechanical analysis can also be applied to injury prevention, where biomechanical studies are carried out to identify actions and movements that stress certain areas of the body, and are shown affected by factors such as torque, impact force, coordination, among others, and how this can contribute to the development of physical damage and possible injury [90]. It is also used for the study of diverse diseases and further exploration for a proper and fuller understanding of said disease [91]. In addition, through the previously mentioned analyses, alternative processes for the diagnosis and management of these diseases and injuries can be found.

2.3. Sports

The analysis of biomechanics using video in sports is very well documented in the literature. The study of different biomechanical features in several sports have been reported in the literature. In Table 1, a list of studies are presented, describing the analyzed sport, software and analysis used. Among the most common sports are: Australian Football, American Football, Soccer, Volleyball, Swimming and Taekwondo. High-jump, Baseball, Basketball, Gaellic Sports, Rugby, Alpine Skiing, Snowboarding and Rugby are also included, although in less quantity.

2.4. Ergonomy

Biomechanical analyses have also been implemented in the ergonomics field. Ergonomics is an important factor to consider in different daily life activities, such as load handling [92], laptop carrying [93] and use, backpack carrying [94] and office work [95]. It is important for employers and companies to ensure their workers’ safety and comfort during specific tasks. In manual labor, injuries associated to tasks such as load handling can represent important direct and indirect medical costs to companies around the world [92]. A biomechanical analysis of spinal forces during load handling was implemented in [92] to calculate the safest and most comfortable position to perform load handling, by measuring EMG signals in muscles in the back and neck.
The use of laptop in no-desk settings has also been reported as uncomfortable in the literature, and some authors agree that ergonomy needs to be taken into account in the design of such devices [93]. Specifically, during the COVID pandemic, a great amount of people were forced to work from home; some without appropriate office equipment, which increases this ergonomics problem. Back pain problems have also been linked to prolonged standing or sitting while working. A recent study analyzed the use of a sitting-standing approach where participants were able to alternate between sitting and standing while performing specific tasks [95]. Discomfort levels and biomechanical parameters were obtained and compared between prolonged sitting or standing and the sitting-standing approach. The results showed lower back discomfort levels when using the sitting-standing approach, while productivity was not affected.
Among the revised literature, different methods, applications, scopes of analysis and challenges in the field of biometry and biomechanical analysis of body movement were identified; all of which were organized in the taxonomy presented in Figure 1.

3. Case Studies

It can be observed in Table 1 that among the reported studies in the literature when it comes to the analysis of biomechanical parameters in sports using video; swimming, taekwondo and football soccer are among the most common. In this work, three case studies were selected to analyze specific movements and techniques of such sports due to their presence in sports biomechanics studies.
A manual joint tracking approach was used, and an analysis of kinematic parameters was implemented on the video-based data analysis system SkillSpector. SkillSpector software was used because it allows to manually digitize positions of human joints in videoframes, and to perform a pixel-coordinate transformation to obtain the real-world positions of each digitized joint using the Direct Linear Transform (DLT) after a calibration process [96]. In each case study, joints of interest were manually selected in SkillSpector (digitized) in a set of frames where athletes performed a given technique from each Sports Calculations on desired variables were carried out using movement, speed, and acceleration analysis with visual representations, such as graphs of measurements of linear and angular kinematics of specific points (joints or body part), as well as animations of the tracked movements in a 3D space with coordinates based on the calibration of each specific case.
From a wide variety of videos available in the web (mainly Youtube) for biomechanical analysis of movements, one swimming video was selected, and analysed. For the analysis of taekwondo and football soccer, videos were personally recorded by the authors with the help of external participants.

3.1. Swimming

The swimming video was selected to show the movements of a male swimmer, extended horizontally while swimming in “front crawl” style, from a perpendicular point of view. Manual digitization of 10 points was performed: Left (L) toe, L ankle, L knee, L hip, L shoulder, L elbow, L wrist, L finger, chin, and forehead; and since this case of study was obtained from an online video, the following assumptions were made. A height of 1.5 m, and a weight of 78 kg were assumed. The approximated area of the hand was estimated as A h a n d = 0.017161256 m2 [97], and the drag coefficient was considered as C d = 1.8262 . In this case study, we were interested in estimating instantaneous force and torque (in a specific videoframe), as well as the average swimming power. Due to the water imposing drag forces onto the swimmer, the equations proposed in [98] were exploited to improve the force estimation made by SkillSpector that doesn’t take into account drag forces.
The velocity of the hand in the x axis was estimated via the manual digitization in SkillSpector. A specific frame (∼2.5 s) of Figure 2 was selected to estimate and observe the generated force, when the swimmer was pushing the water with the arm flexed (close to finishing the stroke). At that point, the estimated force ( F f r e e s w i m ) measured at the finger, despite the active drag related to free swimming was calculated using the following formula [98]:
F f r e e s w i m = 1 2 ρ V f r e e s w i m 2 A h a n d C d
where ρ is the density of water of 997 kg m 3 , and considering the instantaneous velocity at 2.5 s (see Figure 2), V f r e e s w i m 2 = ( 2.3 m / s ) 2 , the estimated average force is F f r e e s w i m = 82.65 N .
The maximum distance from the normal position to the hand was obtained with another graph from SkillSpector, subtracting the distance in the y axis of the shoulder minus the distance in the indicated position of the hand.
The distance was 0.63 m in the position where the arm was practically extended and perpendicular to the body, so for the torque in the shoulder we multiplied the force with the distance:
τ = F f r e e s w i m * d = 82.65 N ( 0.63 m ) = 52.07 Nm
Such results indicate that the dimensions of the estimated torque are consistent with the ones obtained in different biomechanical studies of athletes during crawl swimming, which have reported values of maximal torques of 69 and 91 and Nm [99].
For the calculations that we found on the clinical method [98], we wanted to obtain the average swimming power, which was given by the following equation:
S w i m m i n g P o w e r = ( T o w i n g F o r c e ) ( S w i m m i n g V e l o c i t y )
The average swimming velocity was obtained from the video we are analyzing, which indicates that the athlete swam 50 m (length of an olympic pool) in 38.8 s, and therefore, an average velocity ( V s w i m ) of 1.2886 m/s was considered.
For the towing force, we calculated the resistance in the whole body of swimming, with the sum of the resistance of wave R w a v e and the resistance due to friction R f r i c t i o n a l :
R w a v e = 0.5 ρ V s w i m 2 A w e t t e d C w , R f r i c t i o n a l = 0.5 ρ V s w i m 2 A w e t t e d C f
The coefficient of wave C w for a streamlined body is approximately 0.04 and the area wetted for the body is:
A w e t t e d = 0.20247 H 0.725 W 0.425 = 1.935 m 2 ,
where H is the height of the participant, and W is the weight, which brings us to R w a v e = 63.22 N.
For the frictional coefficient C f the calculations were:
C f = 0.75 ( l o g l o g ( R e ) − 2 ) 2 = 4.6875 * 10 − 3 ,
using a Reynolds number (Re) estimated as 106 for human swimming, therefore:
R f r i c t i o n a l = 0.5 ( 997 kg m 3 ) ( 1.2886 m / s ) 2 ( 1.9351 ) ( 4.6875 × 10 − 3 ) = 7.508 N
And the total sum of the two, which will give us the towing force
R t o t a l = 63.22 N + 7.508 N = 70.728 N
And finally, the swimming power would be calculated as
S w i m m i n g P o w e r = ( 70.728 N ) ( 1.2886 m / s ) = 91.13 W
Figure 2 shows the position, velocity, and estimated force at the left finger of the swimmer while doing a front crawl exercise for three seconds. Pictures of the swimmer are also shown to represent the different phases of movement.
The position graph in Figure 2, shows the tracking of the L finger drawing water during the stroke in crawl, in a starting position, arm aligned with the rest of the body next to the head, and the lowest point as the arm goes to a second position, completely perpendicular position to the athlete’s shoulder at 2.4 s and returns to the horizontal axis of the body.
Velocity started with a negative value of −0.1 m/s and got to its highest point of velocity just a few milliseconds after the lowest position shown in the graphic above, reaching approximately 2.3 m/s at 2.45 s, and progressively lowering the velocity as the hand came back to a third position, arm aligned and close to the body. The applied force began in 0 N as no movement was performed yet, and the value elevated as the stroke began and the hand drew water, with a maximum force of approximately 100 N applied in the moment just before the arm reaches its second position, decreasing as the stroke ends up in the third position.

3.2. Taekwondo

A taekwondo demonstration was performed; in this study case, the dynamical force at the ankle of an athlete during a taewkondo kick was explored. For this scenario, a personally recorded video was used.
In this video, a young female athlete performs a series of punches, stances, and kicks. However, we focused on a particular roundhouse kick with her left leg. A total of 342 frames were manually digitized, and 18 points were used per frame digitization from both left and right (R) sides: (LR toes, LR ankles, LR knees, LR hips, LR shoulder, LR elbows, LR wrists, LR fingers, chin, and forehead). The height and weight of the athlete were of 1.45 m and 35 kg respectively. Figure 3 shows the position, acceleration and estimated force at the left ankle of the athlete, during the roundhouse kick, as well as two manually digitized frames of the video. Here, force was obtained by multiplying the mass times the acceleration estimated by SkillSpector ( F → = m a → ).
In Figure 3, it can be observed that, prior to the execution of the kick, the position (y-axis) remains at ground level, close to 0 m; then, the position constantly increases, representing the upward movement of the kick, reaching 1.6 m in the y-axis. Regarding the acceleration, similarly, it starts approximately at 0 m/s2, representing no movement; the acceleration begins to increase after 0.4 s, reaches a peak acceleration of 65 m/s2 at 0.85 s, and then decreases by the final stages of the kick. By analyzing the Force experimented in the athlete’s left ankle, it can be observed that low forces are presented in the initial stage, intermediate forces (500–1000 N) are observed in different moments (0.6 s, 1.18 s), and a maximum force of approximately 2200 N at the 0.85 s mark, where the kick reaches its highest acceleration.
The obtained maximal force of 2.2 kN for a 35 kg participant during a roundhouse kick lies within a consistent range, when compared to reports of average forces in the foot during roundhouse kicks up to 5.5 kN for 60 kg practitioners [100].

3.3. Football Soccer

For this particular demonstration, a participant was filmed instead of using an online video. In this case, a young (18 years) male participant was recruited. The height and weight of the participant at the moment of the experiment were 1.72 m and 65.5 kg, respectively.
The video was filmed at 50 fps using a Huawei Mate 20 Lite mobile phone (Huawei, Monterrey, Mexico). The procedure involved a lateral view of a soccer ball kick with the dominant leg of the participant. Before the kick, the ball remained in a fixed position, at approximately 0.7 m from the participant. The participant proceeded to reach the soccer ball and kick it using an average force level. Figure 4 shows the position, acceleration, and estimated force at the left ankle of the participant during the kick, as well as two manually digitized frames of the video. Hence, manual digitization entailed forty-six frames (1 s) for this analysis, with 18 points: (LR toes, LR ankles, LR knees, LR hips, LR shoulder, LR elbows, LR wrists, LR fingers, chin, and forehead) per frame. Force was obtained by multiplying the mass times the acceleration estimated by SkillSpector ( F → = m a → ).
As shown in Figure 4, Position tracking in the vertical orientation displays the raising of the foot from ground level until reaching its maximum height at the instance of 0.48 s. Subsequently, the foot lowered towards the ground level with higher velocity, as shown during the lapses 0.49 towards 0.8 s.
Regarding acceleration, the movement began with a positive value close to 50 m/s2, which decreased to allow the foot to raise. The acceleration reached a maximum negative acceleration level surpassing the region of −100 m/s2 and increasing again until reaching a level over 100 m/s2 at the moment of maximum height of the foot. After this moment, acceleration decreased again until getting the foot in touch with the ball, at 0.72 s.
In the case of the applied force, the movement began at approximately 4 kN, reaching a minimum value of 0 N at 0.17 s and increasing steeply towards the raising of the foot. The motion achieved a maximum force of 10 kN at 0.32 s to permit the foot to reach its top height, lowering until reaching a value close to the 3 kN at 0.42 s and rising again to a maximum of 10 kN at 0.5 s. The force decreased again while the foot lowered its height, reaching a minimum of 0 N at 0.68 s and rising when impacting the ball towards 4050 N. Similar studies in the literature have been reported maximal forces during in-step kicking in football soccer of 8.5 kN [101].

4. Discussion

Whereas the results discussed are based on numerical calculations, which can be useful for coaches to a certain extent, a more contextual interpretation can further improve the athelete’s performance. It is here where the role of the biomechanical analysts take importance, as they can interpret the estimations, and translate them from the science into the sports performance domain in order to achieve the goals in mind [102]. In this context, the analyst can use methods considered in the proposed tool for performance forecasting, and data-based decision-making in training [102].
The presented case studies were selected due to the the feasibility of the existence of videos in various media. In future studies, a more extensive search for web videos could facilitate the application of the methods presented for other sports. Other biomechanical studies have been considered by tracking in alternative sports, such as American football [35], rugby [39], baseball [6], basketball [33], Gaelic field [37], snowboard [41], skiing [40], among others. However, such mentioned studies are limited to the analysis of the efforts generated on sensors when stepping or making direct contact, or by consensus decision by experts in the area in case.

4.1. Worlwide Roadmap in Biomechanics

A 30 year-trend study of biomechanics literature is provided in [103], focusing on how the experimental design and practices of biomechanics studies has (or not) changed in the last 30 years. Interesting findings are provided. This report notes that there have not been any significant changes in the sample sizes, groups of participants across the years. Linear increases in the following trends were observed: number of studies including group randomization, number of independent variables, number of trials per participant, number of papers including recommendations and limitations, usage of parametric and non-parametric statistics, while a decrease of the usage of simple correlations, descriptive statistics and number of dependent variables was observed. Although the use of more complex statistical methods has increased over the years, not much progress has been attained concerning the sample sizes of the studies. Authors remark the importance of including sample size estimation in biomechanical studies in order to improve future research in the field.
A survey was implemented across different biomechanics research groups across the world to identify opinions and needs regarding current biomechanics sensor devices. The survey was implemented via online to members of the International Society of Biomechanics in Sports (ISBS), and researchers were asked about their main areas of expertise, main features or variables to study, main devices used, and their desires for an optimal measurement device [104]. The study was able to identify the most frequent research area (biomechanics in sports), features (sports performance) and devices (force platform) and unveiled that little research has focused on the simulation and modelling of internal forces. It was also unveiled that most groups agree on a more integrated device, able to measure multiple features in non-laboratory like settings to provide ecological validity to their research. Also, the need of open-source software tools for biomechanics analysis, compatible with different systems was observed from the results of the survey [104].

4.2. Perspectives

Some of the trends in biomechanics analysis is the implementation of Big Data approaches. A very detailed review on different machine learning and big data implementations on biomechanics of human gait is provided in [105]. This work remarks the benefits of performing such techniques, such as multivariate analysis of complex variables, automatic relevant feature selection, obtaining insight on different populations and accounting for inter-subject variability. The 3D GAIT system was developed by a research group in the University of Calgary, as an automatic biomechanical data collection system that can be used by different research groups, and therefore contribute to Big Data approaches in biomechanics [105]. This system allows to obtain high-dimensional (74 variables) measurements of gait cycles at 200 Hz, and then all the data from each recording is transferred to a research database with demographic information (e.g., mass, height, age, etc.). All this massive amount of data can be used to obtain high-quality models after implementing carefully designed machine or deep learning methodologies.
Other authors have pointed out that artificial intelligence and the Big Data approaches are offering emergent solutions with the proposal of large scale data analysis, such as the development of automated pattern recognition for injury identification in joints [106], and individualized sport teaching between coaches and students, using the historical information available from the users [107]. In a similar context, the transmission of such data will need powerful methods to be transferred in real-time and perform analysis; hence, another important technology that will take importance in Sports Biomechanics research is that of Internet of Things (IoT) to alleviate this challenge [108].

5. Conclusions

Concerning existing studies where tracking modalities were applied in video sports activities, as far as we know, this is the first time that tracking procedures are used based on existing videos from sources of immediate access such as YouTube. Thus, making possible the estimation of position, force, and speed. We confirm that these procedures will have considerable application opportunities in future studies where video motion tracking is essential for calculating moving physical variables.
As indicated previously, the studies to estimate mechanical properties in movements carried out by high-performance sports activities were limited to the use of sensors that calculated the force applied through the direct impact of the limb. The tracking proposed in this work opens up new possibilities through the specific trace of forces and speeds through movements carried out in actual physical activity conditions.
Additionally, future use of deep learning techniques will allow us to estimate movements and calculate the forces applied by physical activities by default. New options will be available for data acquisition to clarify trends of fractures and fatigue injuries caused by repetitive movements determined as extreme for in-vivo conditions. It will then be possible to develop methods for the prediction and prevention of these types of fractures, to be applied clinically in the proper training of high-performance exercise, and towards the prevention of fatigue fractures in bones with osteoporosis.
The case studies discussed in this paper have shown that in the future the prevention of fatigue fractures through the estimation of concentrated stresses on body regions via video analysis will be possible. The collected literature allows us to clarify that, up to our knowledge, there are no studies on the concentration of stresses in specific regions of the anatomy in athletes through tracking modalities from existing videos in the media through the use of specialized software. We estimate that in future studies under the application of deep learning modalities for the appropriate tracking of body movements, it will be possible to validate specific tracking towards the establishment of optimal exercise routines in which the minimization of the development of injuries due to fatigue will be possible.

Author Contributions

Conceptualization, M.A.R.-M., G.P.-E. and J.d.J.L.-S.; methodology, V.E.O.-P., M.A.R.-M. and G.P.-E.; validation, M.A.R.-M. and G.P.-E.; formal analysis, V.E.O.-P., M.A.R.-M. and G.P.-E.; investigation, V.E.O.-P., M.A.R.-M. and G.P.-E.; resources, M.A.R.-M., G.P.-E. and J.d.J.L.-S.; data curation, V.E.O.-P., M.A.R.-M. and G.P.-E.; writing—original draft preparation, V.E.O.-P., M.A.R.-M. and G.P.-E.; writing—review and editing, J.d.J.L.-S. and R.A.R.-M.; visualization, V.E.O.-P., M.A.R.-M. and G.P.-E. and J.d.J.L.-S.; supervision, J.d.J.L.-S.; project administration, J.d.J.L.-S. and R.A.R.-M.; funding acquisition, J.d.J.L.-S. and R.A.R.-M. All authors have read and agreed to the published version of the manuscript.

Funding

The APC of this publication was funded by Tecnologico de Monterrey.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Acknowledgments

Authors would like to thank Tecnologico de Monterrey for the support in the development of this project.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Yoshida, Y.; Nishimura, T.; Jokinen, K. Biomechanics for understanding movements in daily activities. In Proceedings of the LREC 2018 Workshop Language and Body in Real Life, Miyazaki, Japan, 9–11 May 2018. [Google Scholar]
  2. Singh, G.K. Biomechanics of Human Movement in Occupational Tasks with Ergonomic Considerations. Ph.D. Thesis, Deemed University, Tamilnadu, India, 2014. [Google Scholar]
  3. Dafoulas, G.A.; Maia, C.C.; Clarke, J.S.; Ali, A.; Augusto, J. Investigating the role of biometrics in education—The use of sensor data in collaborative learning. In Proceedings of the MCCSIS 2018—Multi Conference on Computer Science and Information Systems, Madrid, Spain, 17–19 July 2018; pp. 115–123. [Google Scholar]
  4. Albert, J.A.; Owolabi, V.; Gebel, A.; Brahms, C.M.; Granacher, U.; Arnrich, B. Evaluation of the pose tracking performance of the azure kinect and kinect v2 for gait analysis in comparison with a gold standard: A pilot study. Sensors 2020, 20, 5104. [Google Scholar] [CrossRef] [PubMed]
  5. Fernández-Baena, A.; Susín, A.; Lligadas, X. Biomechanical validation of upper-body and lower-body joint movements of kinect motion capture data for rehabilitation treatments. In Proceedings of the 2012 4th International Conference on Intelligent Networking and Collaborative Systems, Bucharest, Romania, 19–21 September 2012; pp. 656–661. [Google Scholar] [CrossRef] [Green Version]
  6. De Froda, S.F.; Thigpen, C.A.; Kriz, P.K. Two-dimensional video analysis of youth and adolescent pitching biomechanics: A tool for the common athlete. Curr. Sports Med. Rep. 2016, 15, 350–358. [Google Scholar] [CrossRef] [PubMed]
  7. Guten, G.N. Running Injuries; W. B. Saunders: Philadelphia, PA, USA, 1997. [Google Scholar]
  8. Fredericson, M.; Jennings, F.; Beaulieu, C.; Matheson, G.O. Stress fractures in athletes. Top. Magn. Reson. Imaging 2006, 17, 309–325. [Google Scholar] [CrossRef] [PubMed]
  9. Baggaley, M.; Esposito, M.; Xu, C.; Unnikrishnan, G.; Reifman, J.; Edwards, W.B. Effects of load carriage on biomechanical variables associated with tibial stress fractures in running. Gait Posture 2020, 77, 190–194. [Google Scholar] [CrossRef]
  10. Garrett, W.E. Principles and Practice of Orthopaedic Sports Medicine; Lippincott Williams & Wilkins: Philadelphia, PA, USA, 2000. [Google Scholar]
  11. Irani, M. Principles and Practice of Primary Care Sports Medicine; Garrett, W.E., Jr., Kirkendall, D.T., Squire, D.L., Eds.; Lippincott Williams & Wilkins: Philadelphia, PA, USA, 2001; p. 679. ISBN 0-7817-2956-4. [Google Scholar]
  12. Iwamoto, J.; Takeda, T. Stress fractures in athletes: Review of 196 cases. J. Orthop. Sci. 2003, 8, 273–278. [Google Scholar] [CrossRef]
  13. Troy, K.L.; Davis, I.S.; Tenforde, A.S. A Narrative Review of Metatarsal Bone Stress Injury in Athletic Populations: Etiology, Biomechanics, and Management. PM&R 2020, 13, 1281–1290. [Google Scholar]
  14. Bennell, K.; Brukner, P. Preventing and managing stress fractures in athletes. Phys. Ther. Sport 2005, 6, 171–180. [Google Scholar] [CrossRef]
  15. Boutefnouchet, T.; Budair, B.; Backshayesh, P.; Ali, S.A. Metatarsal fractures: A review and current concepts. Trauma 2014, 16, 147–163. [Google Scholar] [CrossRef]
  16. Miller, D.; Marsland, D.; Jones, M.; Calder, J. Early return to playing professional football following fixation of 5th metatarsal stress fractures may lead to delayed union but does not increase the risk of long-term non-union. Knee Surg. Sports Traumatol. Arthrosc. 2019, 27, 2796–2801. [Google Scholar] [CrossRef]
  17. Ekstrand, J.; van Dijk, C.N. Fifth metatarsal fractures among male professional footballers: A potential career-ending disease. Br. J. Sports Med. 2013, 47, 754–758. [Google Scholar] [CrossRef]
  18. Lawrence, S.J.; Botte, M.J. Jones’ fractures and related fractures of the proximal fifth metatarsal. Foot Ankle 1993, 14, 358–365. [Google Scholar] [CrossRef]
  19. Kuzuyama, M.; Perrier, J.; Kusaki, Y.; Sato, K.; Yamaura, I.; Tsuchiya, A. Characteristics of plantar pressure distribution in elite male soccer players with or without history of proximal fifth metatarsal fracture: A case-control study. J. Phys. Ther. Sci. 2019, 31, 530–535. [Google Scholar] [CrossRef] [Green Version]
  20. Thomson, A.; Akenhead, R.; Whiteley, R.; D’Hooghe, P.; Van Alsenoy, K.; Bleakley, C. Fifth metatarsal stress fracture in elite male football players: An on-field analysis of plantar loading. BMJ Open Sport Exerc. Med. 2018, 4, e000377. [Google Scholar] [CrossRef] [Green Version]
  21. Sims, E.; Hardaker, W.; Queen, R. Gender differences in plantar loading during three soccer-specific tasks. Br. J. Sports Med. 2008, 42, 272–277. [Google Scholar] [CrossRef] [Green Version]
  22. Fujitaka, K.; Taniguchi, A.; Isomoto, S.; Kumai, T.; Otuki, S.; Okubo, M.; Tanaka, Y. Pathogenesis of fifth metatarsal fractures in college soccer players. Orthop. J. Sports Med. 2015, 3, 2325967115603654. [Google Scholar] [CrossRef] [Green Version]
  23. Matsuda, S.; Fukubayashi, T.; Hirose, N. Characteristics of the foot static alignment and the plantar pressure associated with fifth metatarsal stress fracture history in male soccer players: A case-control study. Sports Med.-Open 2017, 3, 1–7. [Google Scholar] [CrossRef]
  24. Azevedo, R.R.; da Rocha, E.S.; Franco, P.S.; Carpes, F.P. Plantar pressure asymmetry and risk of stress injuries in the foot of young soccer players. Phys. Ther. Sport 2017, 24, 39–43. [Google Scholar] [CrossRef]
  25. Fujitaka, K.; Tanaka, Y.; Taniguchi, A.; Ogawa, M.; Isomoto, S.; Otuki, S.; Okubo, M. Pathoanatomy of the Jones fracture in male university soccer players. Am. J. Sports Med. 2020, 48, 424–431. [Google Scholar] [CrossRef]
  26. Chow, J.W.; Knudson, D.V. Use of deterministic models in sports and exercise biomechanics research. Sports Biomech. 2011, 10, 219–233. [Google Scholar] [CrossRef]
  27. Virag, B.; Hibberd, E.E.; Oyama, S.; Padua, D.A.; Myers, J.B. Prevalence of freestyle biomechanical errors in elite competitive swimmers. Sports Health 2014, 6, 218–224. [Google Scholar] [CrossRef] [Green Version]
  28. Heinlein, S.A.; Cosgarea, A.J. Biomechanical considerations in the competitive swimmer’s shoulder. Sports Health 2010, 2, 519–525. [Google Scholar] [CrossRef] [Green Version]
  29. Tovin, B.J. Prevention and treatment of swimmer’s shoulder. N. Am. J. Sports Phys. Ther. NAJSPT 2006, 1, 166. [Google Scholar]
  30. De la Fuente, C.; Ramirez-Campillo, R.; Gallardo-Fuentes, F.; Alvarez, C.; Bustamante, C.; Henríquez, H.; Carpes, F.P. Pattern analysis of a complete Achilles tendon rupture suffered during high jump preparation in an official national-level athletic competition. Sports Biomech. 2019, 21, 312–322. [Google Scholar] [CrossRef]
  31. McIntosh, A.S.; Willmott, C.; Patton, D.A.; Mitra, B.; Brennan, J.H.; Dimech-Betancourt, B.; Howard, T.S.; Rosenfeld, J.V. An assessment of the utility and functionality of wearable head impact sensors in Australian Football. J. Sci. Med. Sport 2019, 22, 784–789. [Google Scholar] [CrossRef]
  32. Reyes, J.; Mitra, B.; McIntosh, A.; Clifton, P.; Makdissi, M.; Nguyen, J.V.; Harcourt, P.; Howard, T.S.; Cameron, P.A.; Rosenfeld, J.V.; et al. An Investigation of Factors Associated With Head Impact Exposure in Professional Male and Female Australian Football Players. Am. J. Sports Med. 2020, 48, 1485–1495. [Google Scholar] [CrossRef]
  33. Lemme, N.J.; Li, N.Y.; Kleiner, J.E.; Tan, S.; DeFroda, S.F.; Owens, B.D. Epidemiology and video analysis of Achilles tendon ruptures in the National Basketball Association. Am. J. Sports Med. 2019, 47, 2360–2366. [Google Scholar] [CrossRef]
  34. Clark, J.M.; Adanty, K.; Post, A.; Hoshizaki, T.B.; Clissold, J.; McGoldrick, A.; Hill, J.; Annaidh, A.N.; Gilchrist, M.D. Proposed injury thresholds for concussion in equestrian sports. J. Sci. Med. Sport 2020, 23, 222–236. [Google Scholar] [CrossRef] [Green Version]
  35. Joodaki, H.; Bailey, A.; Lessley, D.; Funk, J.; Sherwood, C.; Crandall, J. Relative motion between the helmet and the head in football impact test. J. Biomech. Eng. 2019, 141, 081006. [Google Scholar] [CrossRef] [PubMed]
  36. Rowson, B.; Duma, S.M. A review of on-field investigations into the biomechanics of concussion in football and translation to head injury mitigation strategies. Ann. Biomed. Eng. 2020, 48, 2734–2750. [Google Scholar] [CrossRef] [PubMed]
  37. Fox, D.; O’Malley, E.; Blake, C. Normative data for the Functional Movement Screen™ in male Gaelic field sports. Phys. Ther. Sport 2014, 15, 194–199. [Google Scholar] [CrossRef] [PubMed]
  38. Wilcox, B.J.; Machan, J.T.; Beckwith, J.G.; Greenwald, R.M.; Burmeister, E.; Crisco, J.J. Head-impact mechanisms in men’s and women’s collegiate ice hockey. J. Athl. Train. 2014, 49, 514–520. [Google Scholar] [CrossRef] [Green Version]
  39. Preatoni, E.; Cazzola, D.; Stokes, K.; England, M.; Trewartha, G. Pre-binding prior to full engagement improves loading conditions for front-row players in contested R ugby U nion scrums. Scand. J. Med. Sci. Sport. 2016, 26, 1398–1407. [Google Scholar] [CrossRef]
  40. Bere, T.; Flørenes, T.W.; Krosshaug, T.; Koga, H.; Nordsletten, L.; Irving, C.; Muller, E.; Reid, R.C.; Senner, V.; Bahr, R. Mechanisms of anterior cruciate ligament injury in World Cup alpine skiing: A systematic video analysis of 20 cases. Am. J. Sports Med. 2011, 39, 1421–1429. [Google Scholar] [CrossRef]
  41. Bakken, A.; Bere, T.; Bahr, R.; Kristianslund, E.; Nordsletten, L. Mechanisms of injuries in World Cup Snowboard Cross: A systematic video analysis of 19 cases. Br. J. Sports Med. 2011, 45, 1315–1322. [Google Scholar] [CrossRef]
  42. Della Villa, F.; Buckthorpe, M.; Grassi, A.; Nabiuzzi, A.; Tosarelli, F.; Zaffagnini, S.; Della Villa, S. Systematic video analysis of ACL injuries in professional male football (soccer): Injury mechanisms, situational patterns and biomechanics study on 134 consecutive cases. Br. J. Sports Med. 2020, 54, 1423–1432. [Google Scholar] [CrossRef]
  43. Grassi, A.; Tosarelli, F.; Agostinone, P.; Macchiarola, L.; Zaffagnini, S.; Della Villa, F. Rapid Posterior Tibial Reduction After Noncontact Anterior Cruciate Ligament Rupture: Mechanism Description From a Video Analysis. Sports Health 2020, 12, 462–469. [Google Scholar] [CrossRef]
  44. Schmitt, K.U.; Schlittler, M.; Boesiger, P. Biomechanical loading of the hip during side jumps by soccer goalkeepers. J. Sports Sci. 2010, 28, 53–59. [Google Scholar] [CrossRef]
  45. Waldén, M.; Krosshaug, T.; Bjørneboe, J.; Andersen, T.E.; Faul, O.; Hägglund, M. Three distinct mechanisms predominate in non-contact anterior cruciate ligament injuries in male professional football players: A systematic video analysis of 39 cases. Br. J. Sports Med. 2015, 49, 1452–1460. [Google Scholar] [CrossRef] [Green Version]
  46. Parsons, J.L.; Alexander, M.J. Modifying spike jump landing biomechanics in female adolescent volleyball athletes using video and verbal feedback. J. Strength Cond. Res. 2012, 26, 1076–1084. [Google Scholar] [CrossRef] [Green Version]
  47. You, D.Z.; Tomlinson, M.; Borschneck, G.; Borschneck, A.; MacDonald, M.; Deluzio, K.; Borschneck, D. The Effect of Ankle Brace Use on a 3-Step Volleyball Spike Jump Height. Arthrosc. Sports Med. Rehabil. 2020, 2, e461–e467. [Google Scholar] [CrossRef]
  48. Van Trigt, B.; Schallig, W.; Van der Graaff, E.; Hoozemans, M.J.; Veeger, D. Knee angle and stride length in association with ball speed in youth baseball pitchers. Sports 2018, 6, 51. [Google Scholar] [CrossRef] [Green Version]
  49. Reiser, M.; Zentgraf, K.; Kindermann, S.; Künzell, S. An approach to quantify the float effect of float serves in indoor and beach volleyball. Front. Sports Act. Living 2020, 2, 559277. [Google Scholar] [CrossRef]
  50. Wild, J.J.; Bezodis, I.N.; North, J.S.; Bezodis, N.E. Differences in step characteristics and linear kinematics between rugby players and sprinters during initial sprint acceleration. Eur. J. Sport Sci. 2018, 18, 1327–1337. [Google Scholar] [CrossRef]
  51. Linthorne, N.P.; Patel, D.S. Optimum projection angle for attaining maximum distance in a soccer punt kick. J. Sports Sci. Med. 2011, 10, 203. [Google Scholar]
  52. Barkwell, G.E.; Dickey, J.P. Backstroke start performance: The impact of using the Omega OBL2 backstroke ledge. Sports Biomech. 2018, 17, 429–441. [Google Scholar] [CrossRef]
  53. Callaway, A.J. Measuring kinematic variables in front crawl swimming using accelerometers: A validation study. Sensors 2015, 15, 11363–11386. [Google Scholar] [CrossRef] [Green Version]
  54. De Jesus, K.; De Jesus, K.; Medeiros, A.I.; Gonçalves, P.; Figueiredo, P.; Fernandes, R.J.; Vilas-Boas, J.P. Neuromuscular activity of upper and lower limbs during two backstroke swimming start variants. J. Sports Sci. Med. 2015, 14, 591. [Google Scholar]
  55. De Jesus, K.; Sanders, R.; de Jesus, K.; Ribeiro, J.; Figueiredo, P.; Vilas-Boas, J.P.; Fernandes, R.J. The effect of intensity on 3-dimensional kinematics and coordination in front-crawl swimming. Int. J. Sports Physiol. Perform. 2016, 11, 768–775. [Google Scholar] [CrossRef]
  56. Ikeda, Y.; Ichikawa, H.; Nara, R.; Baba, Y.; Shimoyama, Y.; Kubo, Y. Functional role of the front and back legs during a track start with special reference to an inverted pendulum model in college swimmers. J. Appl. Biomech. 2016, 32, 462–468. [Google Scholar] [CrossRef]
  57. Mooney, R.; Corley, G.; Godfrey, A.; Quinlan, L.R.; ÓLaighin, G. Inertial sensor technology for elite swimming performance analysis: A systematic review. Sensors 2016, 16, 18. [Google Scholar] [CrossRef] [Green Version]
  58. Osborough, C.; Daly, D.; Payton, C. Effect of swim speed on leg-to-arm coordination in unilateral arm amputee front crawl swimmers. J. Sports Sci. 2015, 33, 1523–1531. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  59. Papic, C.; Sanders, R.H.; Naemi, R.; Elipot, M.; Andersen, J. Improving data acquisition speed and accuracy in sport using neural networks. J. Sports Sci. 2020, 39, 513–522. [Google Scholar] [CrossRef] [PubMed]
  60. Hamidi Rad, M.; Gremeaux, V.; Dadashi, F.; Aminian, K. A Novel Macro-Micro Approach for Swimming Analysis in Main Swimming Techniques Using IMU Sensors. Front. Bioeng. Biotechnol. 2021, 8, 1511. [Google Scholar] [CrossRef] [PubMed]
  61. Schnitzler, C.; Seifert, L.; Chollet, D.; Toussaint, H. Effect of aerobic training on inter-arm coordination in highly trained swimmers. Hum. Mov. Sci. 2014, 33, 43–53. [Google Scholar] [CrossRef]
  62. Diniz, R.; Del Vecchio, F.B.; Schaun, G.Z.; Oliveira, H.B.; Portella, E.G.; da Silva, E.S.; Formalioni, A.; Campelo, P.C.; Peyré-Tartaruga, L.A.; Pinto, S.S. Kinematic comparison of the roundhouse kick between taekwondo, karate, and muaythai. J. Strength Cond. Res. 2021, 35, 198–204. [Google Scholar] [CrossRef]
  63. Gutiérrez-Santiago, A.; Pereira-Rodríguez, R.; Prieto-Lage, I. Detection of the technical and tactical motion of the scorable movements in taekwondo. Physiol. Behav. 2020, 217, 112813. [Google Scholar] [CrossRef]
  64. Kim, J.W.; Kwon, M.S.; Yenuga, S.S.; Kwon, Y.H. The effects of target distance on pivot hip, trunk, pelvis, and kicking leg kinematics in Taekwondo roundhouse kicks. Sports Biomech. 2010, 9, 98–114. [Google Scholar] [CrossRef]
  65. Maloney, M.A.; Renshaw, I.; Headrick, J.; Martin, D.T.; Farrow, D. Taekwondo fighting in training does not simulate the affective and cognitive demands of competition: Implications for behavior and transfer. Front. Psychol. 2018, 9, 25. [Google Scholar] [CrossRef] [Green Version]
  66. Pradhan, A.; Kuruganti, U.; Chester, V. Biomechanical parameters and clinical assessment scores for identifying elderly fallers based on balance and dynamic tasks. IEEE Access 2020, 8, 193532–193543. [Google Scholar] [CrossRef]
  67. Lloyd, D. The future of in-field sports biomechanics: Wearables plus modelling compute real-time in vivo tissue loading to prevent and repair musculoskeletal injuries. Sports Biomech. 2021, 1–29. [Google Scholar] [CrossRef]
  68. Dingenen, B.; Malfait, B.; Nijs, S.; Peers, K.H.; Vereecken, S.; Verschueren, S.M.; Staes, F.F. Can two-dimensional video analysis during single-leg drop vertical jumps help identify non-contact knee injury risk? A one-year prospective study. Clin. Biomech. 2015, 30, 781–787. [Google Scholar] [CrossRef]
  69. Shere, M.; Kim, H.; Hilton, A. 3D Human Pose Estimation From Multi Person Stereo 360° Scenes. In Proceedings of the CVPR Workshops 2019, Long Beach, CA, USA, 16–20 June 2019. [Google Scholar]
  70. Polak, E.; Kulasa, J.; VencesBrito, A.; Castro, M.A.; Fernandes, O. Motion analysis systems as optimization training tools in combat sports and martial arts. Rev. Artes Marciales Asiáticas 2016, 10, 105. [Google Scholar] [CrossRef] [Green Version]
  71. Ceseracciu, E.; Sawacha, Z.; Fantozzi, S.; Cortesi, M.; Gatta, G.; Corazza, S.; Cobelli, C. Markerless analysis of front crawl swimming. J. Biomech. 2011, 44, 2236–2242. [Google Scholar] [CrossRef]
  72. Makdissi, M.; Davis, G. Using video analysis for concussion surveillance in Australian football. J. Sci. Med. Sport 2016, 19, 958–963. [Google Scholar] [CrossRef]
  73. Syed, S.H.; Willing, R.; Jenkyn, T.R.; Yazdani, A. Video analysis of the biomechanics of a bicycle accident resulting in significant facial fractures. J. Craniofacial Surg. 2013, 24, 2023–2029. [Google Scholar] [CrossRef]
  74. Hanley, B.; Tucker, C.B.; Bissas, A. Differences between motion capture and video analysis systems in calculating knee angles in elite-standard race walking. J. Sports Sci. 2018, 36, 1250–1255. [Google Scholar] [CrossRef]
  75. Schurr, S.A.; Marshall, A.N.; Resch, J.E.; Saliba, S.A. Two-dimensional video analysis is comparable to 3D motion capture in lower extremity movement assessment. Int. J. Sports Phys. Ther. 2017, 12, 163–172. [Google Scholar]
  76. Chan, C.K.; Loh, W.P.; Rahim, I.A. Human motion classification using 2D stick-model matching regression coefficients. Appl. Math. Comput. 2016, 283, 70–89. [Google Scholar] [CrossRef] [Green Version]
  77. Barre, A.; Armand, S. Biomechanical ToolKit: Open-source framework to visualize and process biomechanical data. Comput. Methods Programs Biomed. 2014, 114, 80–87. [Google Scholar] [CrossRef]
  78. Pfister, A.; West, A.M.; Bronner, S.; Noah, J.A. Comparative abilities of Microsoft Kinect and Vicon 3D motion capture for gait analysis. J. Med. Eng. Technol. 2014, 38, 274–280. [Google Scholar] [CrossRef]
  79. Valdivia, C.; Ortega, A.; Salazar, M.; Escobedo, J. Therapeutic Motion Analysis of Lower Limbs Using Kinovea. Int. J. Soft Comput. Eng. 2013, 3, 359–365. [Google Scholar]
  80. Ana, F.; Pedro, R. Use of Open-Source Technology to Teach Biomechanics. Ann. Univ. Oradea Fac. Phys. Educ. Sport. 2016, 26, 18–24. [Google Scholar]
  81. Moghaddami, A.; Gerek, Z.; Karimiasl, A.; Nozohouri, H. The Effect of Acute Dehydration and Rehydration on Biomechanical Parameters of Elite Wrestling Techniques. J. Sports Sci. 2016, 4, 93–101. [Google Scholar] [CrossRef] [Green Version]
  82. Delp, S.L.; Anderson, F.C.; Arnold, A.S.; Loan, P.; Habib, A.; John, C.T.; Guendelman, E.; Thelen, D.G. OpenSim: Open-source software to create and analyze dynamic simulations of movement. IEEE Trans. Biomed. Eng. 2007, 54, 1940–1950. [Google Scholar] [CrossRef] [Green Version]
  83. Shippen, J.; May, B. BoB—Biomechanics in MATLAB. In Proceedings of the 11th International Conference BIOMDOLE, Druskininkai, Lithuania, 20–22 October 2016; pp. 11–13. [Google Scholar] [CrossRef] [Green Version]
  84. Maas, S.A.; Ellis, B.J.; Ateshian, G.A.; Weiss, J.A. FEBio: Finite elements for biomechanics. J. Biomech. Eng. 2012, 134, 1–10. [Google Scholar] [CrossRef]
  85. Preuschl, E.; Hassmann, M.; Baca, A. A kinematic analysis of the jumping front-leg axe-kick in taekwondo. J. Sports Sci. Med. 2016, 15, 92–101. [Google Scholar]
  86. Li, X. 3-D image analysis and discussion on characteristics of taekwondo flank kick movement. RISTI—Revista Ibérica Sistemas Tecnologias Informação 2016, 2016, 170–178. [Google Scholar]
  87. Winiarski, S.; Rutkowska-Kucharska, A. Estimated ground reaction force in normal and pathological gait. Acta Bioeng. Biomech. 2009, 11, 53–60. [Google Scholar]
  88. Chen, T.L.W.; Wong, D.W.C.; Wang, Y.; Ren, S.; Yan, F.; Zhang, M. Biomechanics of fencing sport: A scoping review. PLoS ONE 2017, 12, e0171578. [Google Scholar] [CrossRef]
  89. Estevan, I.; Falco, C. Mechanical analysis of the roundhouse kick according to height and distance in taekwondo. Biol. Sport 2013, 30, 275–279. [Google Scholar] [CrossRef] [Green Version]
  90. Bencke, J.; Aagaard, P.; Zebis, M.K. Muscle activation during ACL injury risk movements in young female athletes: A narrative review. Front. Physiol. 2018, 9, 1–10. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  91. Weiss, K.; Whatman, C. Biomechanics Associated with Patellofemoral Pain and ACL Injuries in Sports. Sports Med. 2015, 45, 1325–1337. [Google Scholar] [CrossRef] [PubMed]
  92. Ning, X.; Zhou, J.; Dai, B.; Jaridi, M. The assessment of material handling strategies in dealing with sudden loading: The effects of load handling position on trunk biomechanics. Appl. Ergon. 2014, 45, 1399–1405. [Google Scholar] [CrossRef] [PubMed]
  93. Chedi, J.M.; Mustapha, R. Biomechanics, visual and carrying discomfort: The role of ergonomics among technology education professionals on the use of laptop in no-desk settings. J. Phys. Conf. Ser. 2020, 1456, 012014. [Google Scholar] [CrossRef] [Green Version]
  94. Suri, C.; Shojaei, I.; Bazrgari, B. Effects of School Backpacks on Spine Biomechanics During Daily Activities: A Narrative Review of Literature. Hum. Factors 2020, 62, 909–918. [Google Scholar] [CrossRef]
  95. Karakolis, T.; Barrett, J.; Callaghan, J.P. A comparison of trunk biomechanics, musculoskeletal discomfort and productivity during simulated sit-stand office work. Ergonomics 2016, 59, 1275–1287. [Google Scholar] [CrossRef]
  96. Ali, S.; Mina, A.; Mehdi, S.; Iman, S.M. C3D data based on 2-dimensional images from video camera. Ann. Biomed. Sci. Eng. 2021, 5, 1–5. [Google Scholar] [CrossRef]
  97. Agarwal, P.; Sahu, S. Determination of hand and palm area as a ratio of body surface area in Indian population. Indian J. Plast. Surg. 2010, 43, 49–53. [Google Scholar] [CrossRef]
  98. Webb, A.; Banks, J.; Phillips, C.; Hudson, D.; Taunton, D.; Turnock, S. Prediction of passive and active drag in swimming. Procedia Eng. 2011, 13, 133–140. [Google Scholar] [CrossRef] [Green Version]
  99. Hildebrand, F.; Schüler, A. Swimming propulsion and muscle force moments. Math. Comput. Model. Dyn. Syst. 2010, 16, 443–453. [Google Scholar] [CrossRef]
  100. O’Sullivan, D.; Chung, C.; Lee, K.; Kim, E.; Kang, S.; Kim, T.; Shin, I. Measurement and comparison of Taekwondo and Yongmudo turning kick impact force for two target heights. J. Sports Sci. Med. 2009, 8, 13–16. [Google Scholar]
  101. Ali, M.F.M.; Yaakub, N.; Haris, M.N.; Kamarudin, M.K.A.; Industry, F.; Sultan, U.; Abidin, Z.; Sultan, U.; Abidin, Z.; Campus, B. Modelling equation of knee force during instep kicking using dominant’s leg in football. J. Fundam. Appl. Sci. 2017, 9, 750–760. [Google Scholar] [CrossRef] [Green Version]
  102. Barbosa, T.M.; Barbosa, A.C.; Simbaña Escobar, D.; Mullen, G.J.; Cossor, J.M.; Hodierne, R.; Arellano, R.; Mason, B.R. The role of the biomechanics analyst in swimming training and competition analysis. Sports Biomech. 2021, 1–18. [Google Scholar] [CrossRef]
  103. Vagenas, G.; Palaiothodorou, D.; Knudson, D. Thirty-year Trends of Study Design and Statistics in Applied Sports and Exercise Biomechanics Research. Int. J. Exerc. Sci. 2018, 11, 239–259. [Google Scholar]
  104. Howard, R.M.; Conway, R.; Harrison, A.J. A survey of sensor devices: Use in sports biomechanics. Sports Biomech. 2016, 15, 450–461. [Google Scholar] [CrossRef] [Green Version]
  105. Phinyomark, A.; Petri, G.; Ibáñez-Marcelo, E.; Osis, S.T.; Ferber, R. Analysis of Big Data in Gait Biomechanics: Current Trends and Future Directions. J. Med. Biol. Eng. 2018, 38, 244–260. [Google Scholar] [CrossRef] [Green Version]
  106. Hollander, K. Biomechanics of Running—Implications for Running-Related Injuries and Future Areas for Research. Sports Med. 2019, 42, 53–54. [Google Scholar] [CrossRef]
  107. Muñoz, M.M.; Price, S.A. The Future is Bright for Evolutionary Morphology and Biomechanics in the Era of Big Data. Integr. Comp. Biol. 2019, 59, 599–603. [Google Scholar] [CrossRef]
  108. Peng, X.; Tang, L. Biomechanics analysis of real-time tennis batting images using Internet of Things and deep learning. J. Supercomput. 2021, 78, 5883–5902. [Google Scholar] [CrossRef]
Applsci 12 03981 g001 550
Figure 1. Taxonomy of the revised literature for methods, applications, scopes of analysis and challenges in Biometry and biomechanical analysis of body movement.
Figure 1. Taxonomy of the revised literature for methods, applications, scopes of analysis and challenges in Biometry and biomechanical analysis of body movement.
Applsci 12 03981 g001
Applsci 12 03981 g002 550
Figure 2. Temporal tracking of position, velocity and estimated force of the left finger of the swimmer during front crawl arm stroke. The insets at the top of the figure depict the sequence of movements that the swimmer was performing while completing one stroke with the left arm.
Figure 2. Temporal tracking of position, velocity and estimated force of the left finger of the swimmer during front crawl arm stroke. The insets at the top of the figure depict the sequence of movements that the swimmer was performing while completing one stroke with the left arm.
Applsci 12 03981 g002
Applsci 12 03981 g003 550
Figure 3. Temporal tracking of position, acceleration and estimated force of the left ankle of the taekwondoist during a high roundhouse kick. The insets at the top of the figure depict the sequence of movements that the taewkwondoist was performing; preparing stance, and high kick.
Figure 3. Temporal tracking of position, acceleration and estimated force of the left ankle of the taekwondoist during a high roundhouse kick. The insets at the top of the figure depict the sequence of movements that the taewkwondoist was performing; preparing stance, and high kick.
Applsci 12 03981 g003
Applsci 12 03981 g004 550
Figure 4. Temporal tracking of position, acceleration and estimated force of the left ankle of the volunteer during a football soccer kick. The insets at the top of the figure depict the sequence of movements that the volunteer was performing; before and after shooting.
Figure 4. Temporal tracking of position, acceleration and estimated force of the left ankle of the volunteer during a football soccer kick. The insets at the top of the figure depict the sequence of movements that the volunteer was performing; before and after shooting.
Applsci 12 03981 g004
Table
Table 1. Video-based biomechanics tools reported in the literature. For each specific study, the Sport, Method/Software and Analysis implemented is presented.
Table 1. Video-based biomechanics tools reported in the literature. For each specific study, the Sport, Method/Software and Analysis implemented is presented.
ReferenceSportMethod/SoftwareAnalysis
[30]High-jumpPixel segmentationBiomechanical analysis on risks and cases of Achilles tendon injury in high-jump attempts.
[31]Australian footballHead of an instrumented Hybrid III anthropomorphic test device (ATD),Head impact exposure and concussion risk under video analysis
[32]Australian footballX-PatchHead acceleration measurements and impacts by video analysis
[6]BaseballHigh-speed motion analysisTwo-dimensional biomechanical analysis use in assessing proper pitching techniques
[33]BasketballVideo analysis softwareRisk factors and causes of Achilles tendon (AT) ruptures in NBA players.
[34]Equestrian SportsKinovea 0.8.20; Head Impact Telemetry (HIT) SystemHelmet impact mechanics; concussion thresholds.
[35]American footballOptical motion capture system (Vicon, Los Angeles, CA)Helmet head protection. Kinematics among head and helmet.
[36]American footballHead impact sensorsReview on the concussion biomechanics and use of head impact sensors.
[37]Gaelic field sportsScoring Gray CookNormative reference values from the study of functional movement
[38]Ice HockeyHead Impact Telemetry (HIT) SystemIdentification of per-game frequencies of head impacts and concussions
[39]RugbyManual digitizing Vicon Motus V9, Vicon Motion Systems, USAPrevention for chronic injuries, towards safer engagement conditions
[40]Alpine SkiingConsensus decision by expertsInjury mechanisms descriptions of anterior cruciate ligament (ACL) injury during competition
[41]SnowboardConsensus decision by expertsSnowboard cross (SBX) descriptions of injury mechanisms
[42]FootballConsensus decision by expertsKinematics and frequency cases of ACL injuries in professional Italian football
[43]FootballKinovea software, consensus decision by expertsApproach for intra-articular lesions by late phases of ACL injury
[44]FootballForce plate Type 9285, Kistler Instruments, high-speed cameraInjury risk by hip loading under diving in goalkeepers
[45]FootballConsensus decision by expertsACL injury cases and contact mechanisms
[46]VolleyballDartfish motion analysis softwareImproving landing techniques to prevent ACL injuries
[47]VolleyballVisual3D tracking systemWearing ankle braces effect and tendency to injury development
[48]BaseballStalker pro radar gun (Stalker Radar, Plano, TX, USA)Biomechanics of the stride length and knee angle with high ball speeds
[49]Beach volleyballPeak Motus Software (Version 9, Vicon Motion Systems. Inc.)Biomechanics of float serve
[50]RugbyKinovea (v.0.8.15)Biomechanics of sprint initial steps
[51]Football soccerAriel Performance Analysis System (Ariel Dynamics, Trabuco Canyon, CA, USA)Measurement of soccer goalkeeper punt kick optimum projection angle
[52]SwimmingKinovea software (Version 0.8.15)Biomechanics on the optimal start performance in backstroke swimming
[53]SwimmingAccelerometer waterproof X6-2mini (Gulf Coast Data Concepts, Waveland, MA, USA); SensorsMultiple sensor systems for biomechanical analysis in front crawl swimming
[54]Swimming24 anatomical markers; linear transformation algorithmUse of differing handgrips in backstroke swimming start performance
[55]SwimmingAriel Dynamics Inc., San Diego, CA, USA
[56]SwimmingDigitizing system (FrameDIAS V, DKH, Inc., Itabashi-ku, Tokyo, Japan)Involvement of left of mass and flight distance in a proper track start
[57]SwimmingInertial sensorsAnalysis of the use of inertial sensors and detection algorithms in swimming phases
[58]SwimmingSIMI Motion 7.2 software (SIMI Reality Motion Systems GmbH, Unterschleißheim, Germany)Biomechanics of the leg to arm coordination in unilateral arm amputee swimmers
[59]Swimming“Cinalysis” software (Elipot et al., 2010)Use of software for auto-digitization and proper analysis of glide posture
[60]SwimmingWearable measurement system including six IMUs (Physilog R IV, GaitUp, CH)Motion phase approach for covering the analysis of full swimming training sessions
[61]SwimmingDartfish softwareEffect of aerobic training towards the increasing of swimming speed
[62]TaekwondoVicon Motion Capture System, Los Angeles, CA, USAKinematic characteristics for improving roundhouse kick technique performance
[63]TaekwondoLINCE v.1.2.1 softwareAnalysis of technical and tactical move patterns towards scoring during combat
[64]TaekwondoKwon3D XP Motion Analysis Suite (Version 4.1, Visol, Seoul, Korea)Target distance analysis during Taekwondo roundhouse kick movement
[65]TaekwondoKinovea, version 0.8.25Analysis on the levels of anxiety and mental challenge during fighting in competition
Table
Table 2. Softwares used for Video-based Biomechanics research and their distinctive features.
Table 2. Softwares used for Video-based Biomechanics research and their distinctive features.
SoftwareEase of AccessDistinctive Features
KinoveaOpen sourceUtilities to capture, slow down, compare, annotate and measure motion in videos.
DartfishMonthly subscriptionAvailable for PC and cell phone. Allows to annotate, compare videos, perform angle measurements.
SkillSpectorFree softwareManual digitization of joints and transformation to real coordinates using DLT transform. Can handle 2D and 3D movement analysis. Advanced analysis of linear and angular kinematic data, inertia calculation, 3D movement animations, and video calibration.
Coach’s EyePaid app, available on iOS, Android and WindowsInteractive and user-friendly GUI for sports assessment. Slowmotion and playback functions.
TrackerOpen source, continuously updatedAutomated tracking based on image recognition which can help in marker-less studies. Ample data analysis tools.
Biomechanical ToolkitOpen source, Python-basedSeveral online repositories, ease of communication with data formats from other softwares used in biomechanics research.
OpenSimOpen sourceDevelopment of models of musculoskeletal structures and generation of dynamic simulations.
ViconPaidHigh-tech software and algorithms. Multiple solutions for different needs of the research and athletes, both marker-less and with markers.
Ariel Performance Analysis SystemFree software3D motion analysis system. Automated biomechanical analysis from multiple, simultaneous video recordings. Can integrate information of EMG, force platforms and video.
Visual3DFree and paid optionsUsers can import motion and force data and perform computations, transformations, models and analysis with reports.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Ortiz-Padilla, V.E.; Ramírez-Moreno, M.A.; Presbítero-Espinosa, G.; Ramírez-Mendoza, R.A.; Lozoya-Santos, J.d.J. Survey on Video-Based Biomechanics and Biometry Tools for Fracture and Injury Assessment in Sports. Appl. Sci. 2022, 12, 3981. https://doi.org/10.3390/app12083981

AMA Style

Ortiz-Padilla VE, Ramírez-Moreno MA, Presbítero-Espinosa G, Ramírez-Mendoza RA, Lozoya-Santos JdJ. Survey on Video-Based Biomechanics and Biometry Tools for Fracture and Injury Assessment in Sports. Applied Sciences. 2022; 12(8):3981. https://doi.org/10.3390/app12083981

Chicago/Turabian Style

Ortiz-Padilla, Vanessa E., Mauricio A. Ramírez-Moreno, Gerardo Presbítero-Espinosa, Ricardo A. Ramírez-Mendoza, and Jorge de J. Lozoya-Santos. 2022. "Survey on Video-Based Biomechanics and Biometry Tools for Fracture and Injury Assessment in Sports" Applied Sciences 12, no. 8: 3981. https://doi.org/10.3390/app12083981

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop