Submit to Applied Sciences Review for Applied Sciences Propose a Special Issue

Journal Browser

Movement Analysis for Health and Biometrics

Special Issue Editors
Special Issue Information
Keywords
Published Papers

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Biosciences and Bioengineering".

Deadline for manuscript submissions: closed (31 October 2022) | Viewed by 23464

Share This Special Issue

Special Issue Editors

Dr. Antonio Parziale

E-Mail Website
Guest Editor

Department of Electrical and Information Engineering and Applied Mathematics (DIEM), Università di Salerno, 84084 Salerno, Italy
Interests: movement analysis; handwriting analysis; automatic signature and writer verification; artificial intelligence; pattern recognition; neurodegenerative disorders; computational neuroscience; systems neuroscience; neurocomputational models; neuromusculoskeletal models; neurorobotics
Special Issues, Collections and Topics in MDPI journals

Dr. Rosa Senatore

E-Mail Website
Guest Editor

Department of Electrical and Information Engineering and Applied Mathematics (DIEM), University of Salerno, Salerno, Italy
Interests: e-health; explainable artificial intelligence; Parkinson disease; machine learning; evolutionary computation; neurocomputational models and pattern recognition
Special Issues, Collections and Topics in MDPI journals

Dr. Nicole Dalia Cilia

E-Mail Website
Guest Editor

Department of Electrical and Information Engineering, University of Cassino and Southern Lazio, Cassino, Italy
Interests: artificial intelligence; embodied cognition; eye-tracking analysis; machine learning; deep learning; neurodegenerative disorders; neurodevelopmental disorders

Special Issue Information

Dear Colleagues,

Movement is being investigated in biomechanics, neuroscience, psychology, and artificial intelligence. The progress that has been made in modeling and analyzing movement is contributing to both the understanding of human movement and the development of new applications. In particular, the availability of low cost and pervasive devices for recording movements (wearable devices, smartphones, tablets, cameras, etc.), together with machine learning methods for the quantitative and automatic analysis of movement, has put forward the development of systems for user authentication, medical diagnosis, and rehabilitation monitoring. A leading example is the use of artificial intelligence methods for the analysis of complex movements such as handwriting and gait, which is improving our knowledge of the mechanisms underlying human movement and, at the same time, enriching the fields of e-health and e-security with new applications. Another successful example is the design of control systems for prosthetic devices that exploit knowledge about movement execution and artificial intelligence methods.

This Special Issue aims to highlight how movement analysis and new technologies are innovating the fields of health and biometrics and contributing to human movement understanding.

We invite researchers to contribute with original works and qualified reviews related to this Special Issue.

Dr. Antonio Parziale
Dr. Rosa Senatore
Dr. Nicole Dalia Cilia
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

gait analysis
handwriting analysis
pose estimation
speech analysis
eye-tracking analysis
neurodegenerative disorder diagnosis and prognosis
assessment of movement disorders
assessment of neurodevelopmental disorders
characterization of fine motor problems
user authentication
signature verification
writer verification
movement-based biometric systems
wearable devices
biomechanical models
assistive robots for rehabilitation
prosthetic devices
brain–computer interfaces

Published Papers (12 papers)

Download All Papers

Editorial

Jump to: Research, Other

5 pages, 185 KiB

Open AccessEditorial

Movement Analysis for Health and Biometrics

by Antonio Parziale, Rosa Senatore and Nicole Dalia Cilia

Appl. Sci. 2023, 13(11), 6683; https://doi.org/10.3390/app13116683 - 31 May 2023

Viewed by 1046

Abstract

The analysis of human movement provides important insights in several fields, such as biomechanics, neuroscience, psychology, medicine, and Artificial Intelligence (AI) [...] Full article

(This article belongs to the Special Issue Movement Analysis for Health and Biometrics)

Research

Jump to: Editorial, Other

24 pages, 1214 KiB

Open AccessArticle

Distinctive Handwriting Signs in Early Parkinson’s Disease

by Rosa Senatore, Angelo Marcelli, Rosa De Micco, Alessandro Tessitore and Hans-Leo Teulings

Appl. Sci. 2022, 12(23), 12338; https://doi.org/10.3390/app122312338 - 02 Dec 2022

Cited by 1 | Viewed by 1261

Abstract

Background: The analysis of handwriting movements to quantify motor and cognitive impairments in neurodegenerative diseases is increasingly attracting interest. Non-invasive and quick-to-administer tools using handwriting movement analysis can be used in early screening of Parkinson’s disease (PD) and maybe in the diagnosis of other neurodegenerative disease. Theaim of this work is to identify the distinctive signs characterizing handwriting in the early stage of PD, in order to provide a diagnostic tool for the early detection of the disease. Compared to previous studies, here, we analyzed handwriting movements of patients on which the disease affects the contralateral side with respect to the one used for writing. Methods: We collected and analyzed a set of handwriting samples by PD patients and healthy subjects. Participants were asked to follow a novel protocol, containing handwriting patterns of various levels of complexity, using both familiar and unfamiliar movements. Results: We found that the signs characterizing the early stage of PD differ from those appearing in later stages. Our work provides evidence that early detection of PD, even when the disease affects mainly the contralateral side with respect to the one used for writing, could be achieved by analyzing specific features measured during the execution of specific handwriting tasks. Eventually, we found that patients’ performance benefits from the execution of handwriting in specific conditions. Conclusions: The analysis provides the guidelines for the design of a diagnostic tool for the early detection of PD and some suggestions for reducing motor impairments in PD patients. Full article

(This article belongs to the Special Issue Movement Analysis for Health and Biometrics)

► Show Figures

Figure 1

19 pages, 3516 KiB

Open AccessArticle

Selection of Kinematic and Temporal Input Parameters to Define a Novel Upper Body Index Indicator for the Evaluation of Upper Limb Pathology

by Agata Guzik-Kopyto, Katarzyna Nowakowska-Lipiec, Mikołaj Krysiak, Katarzyna Jochymczyk-Woźniak, Jacek Jurkojć, Piotr Wodarski, Marek Gzik and Robert Michnik

Appl. Sci. 2022, 12(22), 11634; https://doi.org/10.3390/app122211634 - 16 Nov 2022

Cited by 4 | Viewed by 1379

Abstract

Purpose: This work aimed to develop a novel indicator of upper limb manipulative movements. A principal component analysis (PCA) algorithm was applied to kinematic measurements of movements of the upper limbs performed during an everyday activity. Methods: Kinematics of the upper limb while drinking from a mug were investigated using the commercially available Xsens MVN BIOMECH inertial sensor-based motion capture system. The study group consisted of 20 male patients who had previously suffered an ischaemic stroke, whilst the reference group consisted of 16 males with no disorders of their motor organs. Based on kinematic data obtained, a set of 30 temporal and kinematic parameters were defined. From this, 16 parameters were selected for the determination of a novel indicator, the Upper Body Index (UBI), which served the purpose of assessing manipulative movements of upper limbs. Selection of the 16 parameters considered the percentage distribution of the parameters beyond the standard, the differences in mean values between the reference group and the study group, and parameter variability. Results: Analysis of kinematics allowed for the identification and selection of the parameters used in the development of the new index. This included 2 temporal parameters and 14 kinematic parameters, with the minimum and maximum angles of the upper limb joints, motion ranges in the joints, and parameters connected with movement of the spine recorded. These parameters were used to assess motion in the shoulder and elbow joints, in all possible planes, as well as spine movement. The values of the UBI indicator were as follows: in the case of the reference group: 13.67 ± 2.40 for the dominant limb, 13.71 ± 3.36 for the non-dominant limb; in the case of the stroke patient group: 130.86 ± 75.07 for the dominant limb, 155.58 ± 170.76 for the non-dominant limb. Conclusions: The developed UBI made it possible to discover deviations from the standard performance of upper limb movements. Therefore, the index may be applicable to the analysis of any sequence of movements carried out by the upper limb. Full article

(This article belongs to the Special Issue Movement Analysis for Health and Biometrics)

► Show Figures

Figure 1

12 pages, 2732 KiB

Open AccessArticle

Analysis of Healthcare Push and Pull Task via JACK: Predicted Joint Accuracy during Full-Body Simulation

by Xiaoxu Ji, Davide Piovesan, Maria Arenas and He Liu

Appl. Sci. 2022, 12(13), 6450; https://doi.org/10.3390/app12136450 - 25 Jun 2022

Cited by 6 | Viewed by 1396

Abstract

The posture accuracy of full-body dynamic simulation has been successfully evaluated in JACK Siemens software via analyzing two common push and pull tasks. The difference in joint angles between the actual and predicted human movement directly results in the strength of force exposed on the lumbar spine. In this study, the individual factors, such as body height, body weight, trunk and hip flexion, shoulder movement, and muscle strength between genders, have shown a strong association with the adopted postures and exposed spinal forces during task performance. To provide robust ergonomics analysis, these individual variables should be adequately considered in software design for the long-term goal of injury prevention in diverse occupational workplaces. Full article

(This article belongs to the Special Issue Movement Analysis for Health and Biometrics)

► Show Figures

Figure 1

12 pages, 1824 KiB

Open AccessArticle

A Comparison of Turning Kinematics at Different Amplitudes during Standing Turns between Older and Younger Adults

by Fuengfa Khobkhun, Mark Hollands and Jim Richards

Appl. Sci. 2022, 12(11), 5474; https://doi.org/10.3390/app12115474 - 28 May 2022

Cited by 2 | Viewed by 1253

Abstract

It is well-established that processes involving changing direction or turning in which either or both standing and walking turns are utilized involve coordination of the whole-body and stepping characteristics. However, the turn context and whole-body coordination have not been fully explored during different turning amplitudes. For these reasons, this present study aimed to determine the effects of turning amplitude on whole-body coordination. The findings from this study can be utilized to inform the rationale behind fall prevention factors and to help design an exercise strategy to address issues related to amplitude of turning in older adults. Twenty healthy older and twenty healthy younger adults were asked to complete standing turns on level ground using three randomly selected amplitudes, 90°, 135° and 180°, at their self-selected turn speed. Turning kinematics and stepping variables were recorded using Inertial Measurement Units. Analysis of the data was carried out using Mixed Model Analysis of Variance with two factors (2 groups × 3 turning amplitudes) and further post hoc pairwise analysis to examine differences between factors. There were significant interaction effects (p < 0.05) between the groups and turning amplitudes for step duration and turn speed. Further analysis using Repeated Measure Analysis of Variance tests determined a main effect of amplitude on step duration and turn speed within each group. Furthermore, post hoc pairwise comparisons revealed that the step duration and turn speed increased significantly (p < 0.001) with all increases in turning amplitude in both groups. In addition, significant main effects for group and amplitudes were seen for onset latency of movement for the head, thorax, pelvis, and feet, and for peak head–thorax and peak head–pelvis angular separations and stepping characteristics, which all increased with turn amplitude and showed differences between groups. These results suggest that large amplitude turns result in a change in turning and stepping kinematics. Therefore, when assessing the turning characteristics of older adults or those in frail populations, the turning amplitude should be taken into account during turning, and could be gradually increased to challenge motor control as part of exercise falls prevention strategies. Full article

(This article belongs to the Special Issue Movement Analysis for Health and Biometrics)

► Show Figures

Figure 1

21 pages, 7012 KiB

Open AccessArticle

Multi-Output Sequential Deep Learning Model for Athlete Force Prediction on a Treadmill Using 3D Markers

by Milton Osiel Candela-Leal, Erick Adrián Gutiérrez-Flores, Gerardo Presbítero-Espinosa, Akshay Sujatha-Ravindran, Ricardo Ambrocio Ramírez-Mendoza, Jorge de Jesús Lozoya-Santos and Mauricio Adolfo Ramírez-Moreno

Appl. Sci. 2022, 12(11), 5424; https://doi.org/10.3390/app12115424 - 27 May 2022

Cited by 5 | Viewed by 1760

Abstract

Reliable and innovative methods for estimating forces are critical aspects of biomechanical sports research. Using them, athletes can improve their performance and technique and reduce the possibility of fractures and other injuries. For this purpose, throughout this project, we proceeded to research the use of video in biomechanics. To refine this method, we propose an RNN trained on a biomechanical dataset of regular runners that measures both kinematics and kinetics. The model will allow analyzing, extracting, and drawing conclusions about continuous variable predictions through the body. It marks different anatomical and reflective points (96 in total, 32 per dimension) that will allow the prediction of forces (N) in three dimensions (

F_{x}

F_{y}

F_{z}

), measured on a treadmill with a force plate at different velocities (2.5 m/s, 3.5 m/s, 4.5 m/s). In order to obtain the best model, a grid search of different parameters that combined various types of layers (Simple, GRU, LSTM), loss functions (MAE, MSE, MSLE), and sampling techniques (down-sampling, up-sampling) helped obtain the best performing model (LSTM, MSE, down-sampling) achieved an average coefficient of determination of 0.68, although when excluding

F_{z}

it reached 0.92. Full article

(This article belongs to the Special Issue Movement Analysis for Health and Biometrics)

► Show Figures

Figure 1

8 pages, 259 KiB

Open AccessArticle

The Acute Influence of Whole-Body Cryotherapy on Electromyographic Signals and Jumping Tasks

by Mateusz Kowal, Ewa Gieysztor, Anna Kołcz, Anna Pecuch, Wojciech Borowicz, Robert Dymarek and Małgorzata Paprocka-Borowicz

Appl. Sci. 2022, 12(10), 5020; https://doi.org/10.3390/app12105020 - 16 May 2022

Cited by 1 | Viewed by 1493

Abstract

Whole-body cryotherapy (WBC) is a popular treatment in prevention as well as post-injury therapy. The parameter used to assess the risk of injury is the ability of the human body to absorb and recover energy (elasticity). Therefore, this study aimed to assess the impact of whole-body cryotherapy (WBC) at 1 and 3 min intervals on the bioelectric activity of lower-limb muscles and countermovement jumps (CMJs) using trained subjects. A total of 24 individuals participated in the study. The mean age of the study group was 27.9 ± 7.9 years, mean body weight was 77.9 ± 8.8 kg, and mean body height was equal to 181 ± 6 cm. The training routine included 2–4 training sessions per week that lasted for at least 2 h at a time (mainly football). Along with the surface electromyography (sEMG) test of the rectus femoris, the BTS G-Sensor inertia measurement device was applied. After three minutes of WBC, a 6% difference in take-off force was noted, with a 7% (p < 0.04) decrease in elasticity. In the bioelectrical activity of the rectus femoris after MVC normalization, differences (p < 0.05) were noted 3 min after WBC. In this conducted study, a reduction in flexibility of the lower-limb muscle groups in the CMJ task was noted after 3 min of WBC. Full article

(This article belongs to the Special Issue Movement Analysis for Health and Biometrics)

21 pages, 1844 KiB

Open AccessArticle

A Hybrid Deep Residual Network for Efficient Transitional Activity Recognition Based on Wearable Sensors

by Sakorn Mekruksavanich, Narit Hnoohom and Anuchit Jitpattanakul

Appl. Sci. 2022, 12(10), 4988; https://doi.org/10.3390/app12104988 - 15 May 2022

Cited by 36 | Viewed by 2499

Abstract

Numerous learning-based techniques for effective human behavior identification have emerged in recent years. These techniques focus only on fundamental human activities, excluding transitional activities due to their infrequent occurrence and short period. Nevertheless, postural transitions play a critical role in implementing a system for recognizing human activity and cannot be ignored. This study aims to present a hybrid deep residual model for transitional activity recognition utilizing signal data from wearable sensors. The developed model enhances the ResNet model with hybrid Squeeze-and-Excitation (SE) residual blocks combining a Bidirectional Gated Recurrent Unit (BiGRU) to extract deep spatio-temporal features hierarchically, and to distinguish transitional activities efficiently. To evaluate recognition performance, the experiments are conducted on two public benchmark datasets (HAPT and MobiAct v2.0). The proposed hybrid approach achieved classification accuracies of 98.03% and 98.92% for the HAPT and MobiAct v2.0 datasets, respectively. Moreover, the outcomes show that the proposed method is superior to the state-of-the-art methods in terms of overall accuracy. To analyze the improvement, we have investigated the effects of combining SE modules and BiGRUs into the deep residual network. The findings indicates that the SE module is efficient in improving transitional activity recognition. Full article

(This article belongs to the Special Issue Movement Analysis for Health and Biometrics)

► Show Figures

Figure 1

14 pages, 638 KiB

Open AccessArticle

An Activity Recognition Framework for Continuous Monitoring of Non-Steady-State Locomotion of Individuals with Parkinson’s Disease

by Mahdieh Kazemimoghadam and Nicholas P. Fey

Appl. Sci. 2022, 12(9), 4682; https://doi.org/10.3390/app12094682 - 06 May 2022

Cited by 5 | Viewed by 1335

Abstract

Fundamental knowledge in activity recognition of individuals with motor disorders such as Parkinson’s disease (PD) has been primarily limited to detection of steady-state/static tasks (e.g., sitting, standing, walking). To date, identification of non-steady-state locomotion on uneven terrains (stairs, ramps) has not received much attention. Furthermore, previous research has mainly relied on data from a large number of body locations which could adversely affect user convenience and system performance. Here, individuals with mild stages of PD and healthy subjects performed non-steady-state circuit trials comprising stairs, ramp, and changes of direction. An offline analysis using a linear discriminant analysis (LDA) classifier and a Long-Short Term Memory (LSTM) neural network was performed for task recognition. The performance of accelerographic and gyroscopic information from varied lower/upper-body segments were tested across a set of user-independent and user-dependent training paradigms. Comparing the F1 score of a given signal across classifiers showed improved performance using LSTM compared to LDA. Using LSTM, even a subset of information (e.g., feet data) in subject-independent training appeared to provide F1 score > 0.8. However, employing LDA was shown to be at the expense of being limited to using a subject-dependent training and/or biomechanical data from multiple body locations. The findings could inform a number of applications in the field of healthcare monitoring and developing advanced lower-limb assistive devices by providing insights into classification schemes capable of handling non-steady-state and unstructured locomotion in individuals with mild Parkinson’s disease. Full article

(This article belongs to the Special Issue Movement Analysis for Health and Biometrics)

► Show Figures

Figure 1

19 pages, 1090 KiB

Open AccessArticle

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

Appl. Sci. 2022, 12(8), 3981; https://doi.org/10.3390/app12083981 - 14 Apr 2022

Cited by 6 | Viewed by 3742

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. Full article

(This article belongs to the Special Issue Movement Analysis for Health and Biometrics)

► Show Figures

Figure 1

10 pages, 4417 KiB

Open AccessArticle

Assessing Movement Quality in Youth Footballers: The Relationship between Hip and Lower Limb Movement Screen and Functional Movement Screen

by Pawel Linek, Paul E. Muckelt, Damian Sikora, Nadine Booysen and Maria Stokes

Appl. Sci. 2021, 11(19), 9298; https://doi.org/10.3390/app11199298 - 07 Oct 2021

Cited by 1 | Viewed by 2549

Abstract

The Hip and Lower Limb Movement Screen (HLLMS) was developed to detect altered movement patterns and asymmetry specifically related to hip, pelvic, and lower limb movement control, as the other tools, such as the Functional Movement Screen (FMS), lacked focus on the hip and pelvic area. Both screening tools contain symmetrical and asymmetrical motor tasks which are based on observation of different aspects of each task performance. One motor task is in both screening tools. Therefore, they have some common features. The present study aimed to assess the relationship between the HLLMS and FMS performance in youth football players. The study included 41 elite male football (soccer) players (age: 15.6 ± 0.50 years), and the HLLMS and FMS scores were analyzed by assessing Spearman’s rank correlation. The FMS total score and the FMS_MOVE were moderately correlated with the HLLMS total score (R = −0.54; −0.53, respectively). The FMS rotatory stability task was moderately correlated with the HLLMS small knee bend with the trunk rotation task (R = −0.50). The FMS deep squat task was moderately correlated with the HLLMS deep squat task (R = −0.46). The FMS hurdle step was weakly correlated with two of the HLLMS tasks: standing hip flexion (R = −0.37) and hip abduction with external rotation (R = −0.34). There were no other relationships found (p > 0.05). Out of the seven FMS tasks, only one asymmetrical (trunk rotary stability) and one symmetrical (deep squat) task were moderately related to the newly developed HLLMS tool contributing moderate relationship between the FMS total score and the HLLMS total score. Other FMS tasks were weakly or unrelated with the HLLMS. These findings indicate that these two screening tools mainly assess different aspects of movement quality in healthy youth football players. Full article

(This article belongs to the Special Issue Movement Analysis for Health and Biometrics)

► Show Figures

Figure 1

Other

Jump to: Editorial, Research

20 pages, 4373 KiB

Open AccessBrief Report

Motion Analysis of Balance Pre and Post Sensorimotor Exercises to Enhance Elderly Mobility: A Case Study

by Ji Chen, Roni Romero and Lara A. Thompson

Appl. Sci. 2023, 13(2), 889; https://doi.org/10.3390/app13020889 - 09 Jan 2023

Cited by 3 | Viewed by 1603

Abstract

Quantitative assessment of movement using motion capture provides insights on mobility which are not evident from clinical evaluation. Here, in older individuals that were healthy or had suffered a stroke, we aimed to investigate their balance in terms of changes in body kinematics and muscle activity. Our research question involved determining the effects on post- compared to pre-sensorimotor training exercises on maintaining or improving balance. Our research hypothesis was that training would improve the gait and balance by increasing joint angles and extensor muscle activities in lower extremities and spatiotemporal measures of stroke and elderly people. This manuscript describes a motion capture-based evaluation protocol to assess joint angles and spatiotemporal parameters (cadence, step length and walking speed), as well as major extensor and flexor muscle activities. We also conducted a case study on a healthy older participant (male, age, 65) and an older participant with chronic stroke (female, age, 55). Both participants performed a walking task along a path with a rectangular shape which included tandem walking forward, right side stepping, tandem walking backward, left side stepping to the starting location. For the stroke participant, the training improved the task completion time by 19 s. Her impaired left leg had improved step length (by 0.197 m) and cadence (by 10 steps/min) when walking forward, and cadence (by 12 steps/min) when walking backward. The non-impaired right leg improved cadence when walking forward (by 15 steps/min) and backward (by 27 steps/min). The joint range of motion (ROM) did not change in most cases. However, the ROM of the hip joint increased significantly by 5.8 degrees (p = 0.019) on the left leg side whereas the ROMs of hip joint and knee joint increased significantly by 4.1 degrees (p = 0.046) and 8.1 degrees (p = 0.007) on the right leg side during backward walking. For the healthy participant, the significant changes were only found in his right knee joint ROM having increased by 4.2 degrees (p = 0.031) and in his left ankle joint ROM having increased by 5.5 degrees (p = 0.006) during the left side stepping. Full article

(This article belongs to the Special Issue Movement Analysis for Health and Biometrics)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Movement Analysis for Health and Biometrics

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Published Papers (12 papers)

Editorial

Research

Other

Further Information

Guidelines

MDPI Initiatives

Follow MDPI