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Special Issue "Wearable Sensors & Gait"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Wearables".

Deadline for manuscript submissions: closed (31 January 2022) | Viewed by 17820

Special Issue Editors

Prof. Felipe García-Pinillos
E-Mail Website
Guest Editor
Department of Physical Education and Sport, Faculty of Sport Sciences, University of Granada, Granada, Spain
Interests: training; sport biomechanics; sport technology; endurance performance; endurance running
Prof. Luis Enrique Roche-Seruendo
E-Mail Website
Guest Editor
Universidad San Jorge, Campus Universitario, Autov A23 km 299, 50830, Villanueva de Gállego Zaragoza, Spain
Interests: sport biomechanics; sport technology; gait biomechanics; running biomechanics
Dr. Diego Jaén-Carrillo
E-Mail Website
Guest Editor
Universidad San Jorge, Campus Universitario, Autov A23 km 299, 50830, Villanueva de Gállego Zaragoza, Spain
Interests: port biomechanics; endurance; performance; running; sport technology; training

Special Issue Information

Dear Colleagues,

Gait analysis has been traditionally conducted in laboratory settings and thereby has requested specific conditions and expensive equipment. The emergence of wearable sensors solves the lack of ecology for these measurements and offers a more economical and easy to use option to perform gait analysis. Lately, wearable sensors have allowed the quantification of performance and workload by providing mechanical and physiological parameters and their popularity has grown exponentially. In this context, more and more wearable sensors are commercially available and, when applied to gait analysis (either walking or running), these devices are able to provide both kinetic and kinematic variables improving consequently the feasibility and testing time of such assessments and, therefore, becoming a real alternative for clinicians, researchers and sport practitioners.

The incremental growth in big data, cloud computing and artificial intelligent make these sensors suitable to connect gait biomechanics with real life and real time analysis. All these benefits broaden the possibilities, among others, to provide real-time biofeedback while walking and running, or to integrate sensors with cloud platforms or mobile apps to improve health and/or performance.

This Special Issue encourages authors to submit their research and contributions about the use and application of wearable sensors for gait assessment and analysis.

The main topics for this issue include, but not limited to:

- Validity analysis of novel wearable sensors for human locomotion.

- Reliability analysis of wearable sensor.

- New applications and uses of metrics provided by wearable sensors in training, competition and injury management settings.

- Novel technologies applied to gait analysis.

- State of the art for wearable devices.

-Algorithms, integrations with other platforms or software, signal processing, and bigdata obtained by wearable sensors.

Prof. Felipe García-Pinillos
Prof. Luis Enrique Roche-Seruendo
Dr. Diego Jaén-Carrillo
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. Sensors 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.

Published Papers (15 papers)

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Research

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Article
Test-Retest Reliability of the MotionMetrix Software for the Analysis of Walking and Running Gait Parameters
Sensors 2022, 22(9), 3201; https://doi.org/10.3390/s22093201 - 21 Apr 2022
Viewed by 343
Abstract
The use of markerless motion capture systems is becoming more popular for walking and running analysis given their user-friendliness and their time efficiency but in some cases their validity is uncertain. Here, the test-retest reliability of the MotionMetrix software combined with the use [...] Read more.
The use of markerless motion capture systems is becoming more popular for walking and running analysis given their user-friendliness and their time efficiency but in some cases their validity is uncertain. Here, the test-retest reliability of the MotionMetrix software combined with the use of Kinect sensors is tested with 24 healthy volunteers for walking (at 5 km·h−1) and running (at 10 and 15 km·h−1) gait analysis in two different trials. All the parameters given by the MotionMetrix software for both walking and running gait analysis are tested in terms of reliability. No significant differences (p > 0.05) were found for walking gait parameters between both trials except for the phases of loading response and double support, and the spatiotemporal parameters of step length and step frequency. Additionally, all the parameters exhibit acceptable reliability (CV < 10%) but step width (CV > 10%). When analyzing running gait, although the parameters here tested exhibited different reliability values at 10 km·h−1, the system provided reliable measurements for most of the kinematic and kinetic parameters (CV < 10%) when running at 15 km·h−1. Overall, the results obtained show that, although some variables must be interpreted with caution, the Kinect + MotionMetrix system may be useful for walking and running gait analysis. Nevertheless, the validity still needs to be determined against a gold standard system to fully trust this technology and software combination. Full article
(This article belongs to the Special Issue Wearable Sensors & Gait)
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Article
Clinical–Functional Evaluation and Test–Retest Reliability of the G-WALK Sensor in Subjects with Bimalleolar Ankle Fractures 6 Months after Surgery
Sensors 2022, 22(8), 3050; https://doi.org/10.3390/s22083050 - 15 Apr 2022
Viewed by 358
Abstract
Ankle fractures can cause significant functional impairment in the short and long term. In recent years, gait analysis using inertial sensors has gained special relevance as a reliable measurement system. This study aimed to evaluate the differences in spatiotemporal gait parameters and clinical–functional [...] Read more.
Ankle fractures can cause significant functional impairment in the short and long term. In recent years, gait analysis using inertial sensors has gained special relevance as a reliable measurement system. This study aimed to evaluate the differences in spatiotemporal gait parameters and clinical–functional measurements in patients with bimalleolar ankle fracture and healthy subjects, to study the correlation between the different variables, and to analyze the test–retest reliability of a single inertial sensor in our study population. Twenty-two subjects with bimalleolar ankle fracture six months after surgery and eleven healthy subjects were included in the study. Spatiotemporal parameters were analyzed with the G-WALK sensor. Functional scales and clinical measures were collected beforehand. In the ankle fracture group, the main differences were obtained in bilateral parameters (effect size: 0.61 ≤ d ≤ 0.80). Between-group differences were found in cadence, speed, stride length, and stride time (effect size: 1.61 ≤ d ≤ 1.82). Correlation was moderate (0.436 < r < 0.554) between spatiotemporal parameters and clinical–functional measures, explaining up to 46% of gait performance. Test–retest reliability scores were high to excellent (0.84 ≤ ICC ≤ 0.98), with the worst results in the gait phases. Our study population presents evident clinical–functional impairments 6 months after surgery. The G-WALK can be considered a reliable tool for clinical use in this population. Full article
(This article belongs to the Special Issue Wearable Sensors & Gait)
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Article
Temporal Synergies Detection in Gait Cyclograms Using Wearable Technology
Sensors 2022, 22(7), 2728; https://doi.org/10.3390/s22072728 - 02 Apr 2022
Cited by 1 | Viewed by 452
Abstract
The human gait can be described as the synergistic activity of all individual components of the sensory–motor system. The central nervous system (CNS) develops synergies to execute endpoint motion by coordinating muscle activity to reflect the global goals of the endpoint trajectory. This [...] Read more.
The human gait can be described as the synergistic activity of all individual components of the sensory–motor system. The central nervous system (CNS) develops synergies to execute endpoint motion by coordinating muscle activity to reflect the global goals of the endpoint trajectory. This paper proposes a new method for assessing temporal dynamic synergies. Principal component analysis (PCA) has been applied on the signals acquired by wearable sensors (inertial measurement units, IMU and ground reaction force sensors, GRF mounted on feet) to detect temporal synergies in the space of two-dimensional PCA cyclograms. The temporal synergy results for different gait speeds in healthy subjects and stroke patients before and after the therapy were compared. The hypothesis of invariant temporal synergies at different gait velocities was statistically confirmed, without the need to record and analyze muscle activity. A significant difference in temporal synergies was noticed in hemiplegic gait compared to healthy gait. Finally, the proposed PCA-based cyclogram method provided the therapy follow-up information about paretic leg gait in stroke patients that was not available by observing conventional parameters, such as temporal and symmetry gait measures. Full article
(This article belongs to the Special Issue Wearable Sensors & Gait)
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Article
Step Length Estimation Using the RSSI Method in Walking and Jogging Scenarios
Sensors 2022, 22(4), 1640; https://doi.org/10.3390/s22041640 - 19 Feb 2022
Viewed by 400
Abstract
In this paper, human step length was estimated based on wireless channel properties and the received signal strength indicator (RSSI) method. Path loss between two ankles of the person under test was converted from the RSSI, which was measured using our developed wearable [...] Read more.
In this paper, human step length was estimated based on wireless channel properties and the received signal strength indicator (RSSI) method. Path loss between two ankles of the person under test was converted from the RSSI, which was measured using our developed wearable transceivers with embedded micro-controllers in four scenarios, namely indoor walking, outdoor walking, indoor jogging, and outdoor jogging. For brevity, we call it on-ankle path loss. The histogram of the on-ankle path loss showed clearly that there were two humps, where the second hump was closely related to the maximum path loss, which, in turn, corresponded to the step length. This histogram can be well approximated by a two-term Gaussian fitting curve model. Based on the histogram of the experimental data and the two-term Gaussian fitting curve, we propose a novel filtering technique to filter out the path loss outliers, which helps set up the upper and lower thresholds of the path loss values used for the step length estimation. In particular, the upper threshold was found to be on the right side of the second Gaussian hump, and its value was a function of the mean value and the standard deviation of the second Gaussian hump. Meanwhile, the lower threshold lied on the left side of the second hump and was determined at the point where the survival rate of the measured data fell to 0.68, i.e., the cumulative distribution function (CDF) approached 0.32. The experimental data showed that the proposed filtering technique resulted in high accuracy in step length estimation with errors of only 10.15 mm for the indoor walking, 4.40 mm for the indoor jogging, 4.81 mm for the outdoor walking, and 10.84 mm for the outdoor jogging scenarios, respectively. Full article
(This article belongs to the Special Issue Wearable Sensors & Gait)
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Article
Using a Portable Gait Rhythmogram to Examine the Effect of Music Therapy on Parkinson’s Disease-Related Gait Disturbance
Sensors 2021, 21(24), 8321; https://doi.org/10.3390/s21248321 - 13 Dec 2021
Viewed by 818
Abstract
External cues improve walking by evoking internal rhythm formation related to gait in the brain in patients with Parkinson’s disease (PD). This study examined the usefulness of using a portable gait rhythmogram (PGR) in music therapy on PD-related gait disturbance. A total of [...] Read more.
External cues improve walking by evoking internal rhythm formation related to gait in the brain in patients with Parkinson’s disease (PD). This study examined the usefulness of using a portable gait rhythmogram (PGR) in music therapy on PD-related gait disturbance. A total of 19 subjects with PD who exhibited gait disturbance were evaluated for gait speed and step length during a 10 m straight walking task. Moreover, acceleration, cadence, and trajectory of the center of the body were estimated using a PGR. Walking tasks were created while incorporating music intervention that gradually increased in tempo from 90 to 120 beats per minute (BPM). We then evaluated whether immediate improvement in gait could be recognized even without music after walking tasks by comparing pre- (pre-MT) and post-music therapy (post-MT) values. Post-MT gait showed significant improvement in acceleration, gait speed, cadence, and step length. During transitions throughout the walking tasks, acceleration, gait speed, cadence, and step length gradually increased in tasks with music. With regard to the trajectory of the center of the body, we recognized a reduction in post-MT medio-lateral amplitude. Music therapy immediately improved gait disturbance in patients with PD, and the effectiveness was objectively shown using PGR. Full article
(This article belongs to the Special Issue Wearable Sensors & Gait)
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Article
Electromyography, Stiffness and Kinematics of Resisted Sprint Training in the Specialized SKILLRUN® Treadmill Using Different Load Conditions in Rugby Players
Sensors 2021, 21(22), 7482; https://doi.org/10.3390/s21227482 - 10 Nov 2021
Viewed by 682
Abstract
This study’s aim was to analyze muscle activation and kinematics of sled-pushing and resisted-parachute sprinting with three load conditions on an instrumentalized SKILLRUN® treadmill. Nine male amateur rugby union players (21.3 ± 4.3 years, 75.8 ± 10.2 kg, 176.6 ± 8.8 cm) [...] Read more.
This study’s aim was to analyze muscle activation and kinematics of sled-pushing and resisted-parachute sprinting with three load conditions on an instrumentalized SKILLRUN® treadmill. Nine male amateur rugby union players (21.3 ± 4.3 years, 75.8 ± 10.2 kg, 176.6 ± 8.8 cm) performed a sled-push session consisting of three 15-m repetitions at 20%, 55% and 90% body mas and another resisted-parachute session using three different parachute sizes (XS, XL and 3XL). Sprinting kinematics and muscle activity of three lower-limb muscles (biceps femoris (BF), vastus lateralis (VL) and gastrocnemius medialis (GM)) were measured. A repeated-measures analysis of variance (RM-ANOVA) showed that higher loads during the sled-push increased (VL) (p ≤ 0.001) and (GM) (p ≤ 0.001) but not (BF) (p = 0.278) activity. Furthermore, it caused significant changes in sprinting kinematics, stiffness and joint angles. Resisted-parachute sprinting did not change kinematics or muscle activation, despite producing a significant overload (i.e., speed loss). In conclusion, increased sled-push loading caused disruptions in sprinting technique and altered lower-limb muscle activation patterns as opposed to the resisted-parachute. These findings might help practitioners determine the more adequate resisted sprint exercise and load according to the training objective (e.g., power production or speed performance). Full article
(This article belongs to the Special Issue Wearable Sensors & Gait)
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Article
Orientation-Invariant Spatio-Temporal Gait Analysis Using Foot-Worn Inertial Sensors
Sensors 2021, 21(11), 3940; https://doi.org/10.3390/s21113940 - 07 Jun 2021
Cited by 2 | Viewed by 1502
Abstract
Inertial sensors can potentially assist clinical decision making in gait-related disorders. Methods for objective spatio-temporal gait analysis usually assume the careful alignment of the sensors on the body, so that sensor data can be evaluated using the body coordinate system. Some studies infer [...] Read more.
Inertial sensors can potentially assist clinical decision making in gait-related disorders. Methods for objective spatio-temporal gait analysis usually assume the careful alignment of the sensors on the body, so that sensor data can be evaluated using the body coordinate system. Some studies infer sensor orientation by exploring the cyclic characteristics of walking. In addition to being unrealistic to assume that the sensor can be aligned perfectly with the body, the robustness of gait analysis with respect to differences in sensor orientation has not yet been investigated—potentially hindering use in clinical settings. To address this gap in the literature, we introduce an orientation-invariant gait analysis approach and propose a method to quantitatively assess robustness to changes in sensor orientation. We validate our results in a group of young adults, using an optical motion capture system as reference. Overall, good agreement between systems is achieved considering an extensive set of gait metrics. Gait speed is evaluated with a relative error of 3.1±9.2 cm/s, but precision improves when turning strides are excluded from the analysis, resulting in a relative error of 3.4±6.9 cm/s. We demonstrate the invariance of our approach by simulating rotations of the sensor on the foot. Full article
(This article belongs to the Special Issue Wearable Sensors & Gait)
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Communication
Are the Assioma Favero Power Meter Pedals a Reliable Tool for Monitoring Cycling Power Output?
Sensors 2021, 21(8), 2789; https://doi.org/10.3390/s21082789 - 15 Apr 2021
Cited by 4 | Viewed by 1700
Abstract
This study aimed to examine the validity and reliability of the recently developed Assioma Favero pedals under laboratory cycling conditions. In total, 12 well-trained male cyclists and triathletes (VO2max = 65.7 ± 8.7 mL·kg−1·min−1) completed five cycling tests [...] Read more.
This study aimed to examine the validity and reliability of the recently developed Assioma Favero pedals under laboratory cycling conditions. In total, 12 well-trained male cyclists and triathletes (VO2max = 65.7 ± 8.7 mL·kg−1·min−1) completed five cycling tests including graded exercises tests (GXT) at different cadences (70–100 revolutions per minute, rpm), workloads (100–650 Watts, W), pedaling positions (seated and standing), vibration stress (20–40 Hz), and an 8-s maximal sprint. Tests were completed using a calibrated direct drive indoor trainer for the standing, seated, and vibration GXTs, and a friction belt cycle ergometer for the high-workload step protocol. Power output (PO) and cadence were collected from three different brand, new pedal units against the gold-standard SRM crankset. The three units of the Assioma Favero exhibited very high within-test reliability and an extremely high agreement between 100 and 250 W, compared to the gold standard (Standard Error of Measurement, SEM from 2.3–6.4 W). Greater PO produced a significant underestimating trend (p < 0.05, Effect size, ES ≥ 0.22), with pedals showing systematically lower PO than SRM (1–3%) but producing low bias for all GXT tests and conditions (1.5–7.4 W). Furthermore, vibrations ≥ 30 Hz significantly increased the differences up to 4% (p < 0.05, ES ≥ 0.24), whereas peak and mean PO differed importantly between devices during the sprints (p < 0.03, ES ≥ 0.39). These results demonstrate that the Assioma Favero power meter pedals provide trustworthy PO readings from 100 to 650 W, in either seated or standing positions, with vibrations between 20 and 40 Hz at cadences of 70, 85, and 100 rpm, or even at a free chosen cadence. Full article
(This article belongs to the Special Issue Wearable Sensors & Gait)
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Article
The Relationship between VO2max, Power Management, and Increased Running Speed: Towards Gait Pattern Recognition through Clustering Analysis
Sensors 2021, 21(7), 2422; https://doi.org/10.3390/s21072422 - 01 Apr 2021
Cited by 1 | Viewed by 1866
Abstract
Triathlon has become increasingly popular in recent years. In this discipline, maximum oxygen consumption (VO2max) is considered the gold standard for determining competition cardiovascular capacity. However, the emergence of wearable sensors (as Stryd) has drastically changed training and races, allowing for [...] Read more.
Triathlon has become increasingly popular in recent years. In this discipline, maximum oxygen consumption (VO2max) is considered the gold standard for determining competition cardiovascular capacity. However, the emergence of wearable sensors (as Stryd) has drastically changed training and races, allowing for the more precise evaluation of athletes and study of many more potential determining variables. Thus, in order to discover factors associated with improved running efficiency, we studied which variables are correlated with increased speed. We then developed a methodology to identify associated running patterns that could allow each individual athlete to improve their performance. To achieve this, we developed a correlation matrix, implemented regression models, and created a heat map using hierarchical cluster analysis. This highlighted relationships between running patterns in groups of young triathlon athletes and several different variables. Among the most important conclusions, we found that high VO2max did not seem to be significantly correlated with faster speed. However, faster individuals did have higher power per kg, horizontal power, stride length, and running effectiveness, and lower ground contact time and form power ratio. VO2max appeared to strongly correlate with power per kg and this seemed to indicate that to run faster, athletes must also correctly manage their power. Full article
(This article belongs to the Special Issue Wearable Sensors & Gait)
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Article
The Use of Infrared Thermography to Develop and Assess a Wearable Sock and Monitor Foot Temperature in Diabetic Subjects
Sensors 2021, 21(5), 1821; https://doi.org/10.3390/s21051821 - 05 Mar 2021
Cited by 2 | Viewed by 1123
Abstract
One important health problem that could affect diabetics is diabetic foot syndrome, as risk of ulceration, neuropathy, ischemia and infection. Unnoticed minor injuries, subsequent infection and ulceration may end in a foot amputation. Preliminary studies have shown a relationship between increased skin temperature [...] Read more.
One important health problem that could affect diabetics is diabetic foot syndrome, as risk of ulceration, neuropathy, ischemia and infection. Unnoticed minor injuries, subsequent infection and ulceration may end in a foot amputation. Preliminary studies have shown a relationship between increased skin temperature and asymmetries between the same regions of both feet. In the preulceration phase, to develop a smart device able to control the temperature of these types of patients to avoid this risk might be very useful. A statistical analysis has been carried out with a sample of foot temperature data obtained from 93 individuals, of whom 44 are diabetics and 49 nondiabetics and among them 43% are men and 57% are women. Data obtained with a thermographic camera has been successful in providing a set of regions of interest, where the temperature could influence the individual, and the behavior of several variables that could affect these subjects provides a mathematical model. Finally, an in-depth analysis of existing sensors situated in those positions, namely, heel, medial midfoot, first metatarsal head, fifth metatarsal head, and first toe has allowed for the development of a smart sock to store temperatures obtained every few minutes in a mobile device. Full article
(This article belongs to the Special Issue Wearable Sensors & Gait)
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Article
Estimating Functional Threshold Power in Endurance Running from Shorter Time Trials Using a 6-Axis Inertial Measurement Sensor
Sensors 2021, 21(2), 582; https://doi.org/10.3390/s21020582 - 15 Jan 2021
Cited by 3 | Viewed by 1153
Abstract
Wearable technology has allowed for the real-time assessment of mechanical work employed in several sporting activities. Through novel power metrics, Functional Threshold Power have shown a reliable indicator of training intensities. This study aims to determine the relationship between mean power output (MPO) [...] Read more.
Wearable technology has allowed for the real-time assessment of mechanical work employed in several sporting activities. Through novel power metrics, Functional Threshold Power have shown a reliable indicator of training intensities. This study aims to determine the relationship between mean power output (MPO) values obtained during three submaximal running time trials (i.e., 10 min, 20 min, and 30 min) and the functional threshold power (FTP). Twenty-two recreationally trained male endurance runners completed four submaximal running time trials of 10, 20, 30, and 60 min, trying to cover the longest possible distance on a motorized treadmill. Absolute MPO (W), normalized MPO (W/kg) and standard deviation (SD) were calculated for each time trial with a power meter device attached to the shoelaces. All simplified FTP trials analyzed (i.e., FTP10, FTP20, and FTP30) showed a significant association with the calculated FTP (p < 0.001) for both MPO and normalized MPO, whereas stronger correlations were found with longer time trials. Individual correction factors (ICF% = FTP60/FTPn) of ~90% for FTP10, ~94% for FTP20, and ~96% for FTP30 were obtained. The present study procures important practical applications for coaches and athletes as it provides a more accurate estimation of FTP in endurance running through less fatiguing, reproducible tests. Full article
(This article belongs to the Special Issue Wearable Sensors & Gait)
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Article
Foot Strike Angle Prediction and Pattern Classification Using LoadsolTM Wearable Sensors: A Comparison of Machine Learning Techniques
Sensors 2020, 20(23), 6737; https://doi.org/10.3390/s20236737 - 25 Nov 2020
Cited by 5 | Viewed by 1143
Abstract
The foot strike pattern performed during running is an important variable for runners, performance practitioners, and industry specialists. Versatile, wearable sensors may provide foot strike information while encouraging the collection of diverse information during ecological running. The purpose of the current study was [...] Read more.
The foot strike pattern performed during running is an important variable for runners, performance practitioners, and industry specialists. Versatile, wearable sensors may provide foot strike information while encouraging the collection of diverse information during ecological running. The purpose of the current study was to predict foot strike angle and classify foot strike pattern from LoadsolTM wearable pressure insoles using three machine learning techniques (multiple linear regression―MR, conditional inference tree―TREE, and random forest―FRST). Model performance was assessed using three-dimensional kinematics as a ground-truth measure. The prediction-model accuracy was similar for the regression, inference tree, and random forest models (RMSE: MR = 5.16°, TREE = 4.85°, FRST = 3.65°; MAPE: MR = 0.32°, TREE = 0.45°, FRST = 0.33°), though the regression and random forest models boasted lower maximum precision (13.75° and 14.3°, respectively) than the inference tree (19.02°). The classification performance was above 90% for all models (MR = 90.4%, TREE = 93.9%, and FRST = 94.1%). There was an increased tendency to misclassify mid foot strike patterns in all models, which may be improved with the inclusion of more mid foot steps during model training. Ultimately, wearable pressure insoles in combination with simple machine learning techniques can be used to predict and classify a runner’s foot strike with sufficient accuracy. Full article
(This article belongs to the Special Issue Wearable Sensors & Gait)
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Article
Wearable Sensors Detect Differences between the Sexes in Lower Limb Electromyographic Activity and Pelvis 3D Kinematics during Running
Sensors 2020, 20(22), 6478; https://doi.org/10.3390/s20226478 - 12 Nov 2020
Cited by 1 | Viewed by 1588
Abstract
Each year, 50% of runners suffer from injuries. Consequently, more studies are being published about running biomechanics; these studies identify factors that can help prevent injuries. Scientific evidence suggests that recreational runners should use personalized biomechanical training plans, not only to improve their [...] Read more.
Each year, 50% of runners suffer from injuries. Consequently, more studies are being published about running biomechanics; these studies identify factors that can help prevent injuries. Scientific evidence suggests that recreational runners should use personalized biomechanical training plans, not only to improve their performance, but also to prevent injuries caused by the inability of amateur athletes to tolerate increased loads, and/or because of poor form. This study provides an overview of the different normative patterns of lower limb muscle activation and articular ranges of the pelvis during running, at self-selected speeds, in men and women. Methods: 38 healthy runners aged 18 to 49 years were included in this work. We examined eight muscles by applying two wearable superficial electromyography sensors and an inertial sensor for three-dimensional (3D) pelvis kinematics. Results: the largest differences were obtained for gluteus maximus activation in the first double float phase (p = 0.013) and second stance phase (p = 0.003), as well as in the gluteus medius in the second stance phase (p = 0.028). In both cases, the activation distribution was more homogeneous in men and presented significantly lower values than those obtained for women. In addition, there was a significantly higher percentage of total vastus medialis activation in women throughout the running cycle with the median (25th–75th percentile) for women being 12.50% (9.25–14) and 10% (9–12) for men. Women also had a greater range of pelvis rotation during running at self-selected speeds (p = 0.011). Conclusions: understanding the differences between men and women, in terms of muscle activation and pelvic kinematic values, could be especially useful to allow health professionals detect athletes who may be at risk of injury. Full article
(This article belongs to the Special Issue Wearable Sensors & Gait)
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Article
A Random Forest Machine Learning Framework to Reduce Running Injuries in Young Triathletes
Sensors 2020, 20(21), 6388; https://doi.org/10.3390/s20216388 - 09 Nov 2020
Cited by 2 | Viewed by 2177
Abstract
Background: The running segment of a triathlon produces 70% of the lower limb injuries. Previous research has shown a clear association between kinematic patterns and specific injuries during running. Methods: After completing a seven-month gait retraining program, a questionnaire was used to assess [...] Read more.
Background: The running segment of a triathlon produces 70% of the lower limb injuries. Previous research has shown a clear association between kinematic patterns and specific injuries during running. Methods: After completing a seven-month gait retraining program, a questionnaire was used to assess 19 triathletes for the incidence of injuries. They were also biomechanically analyzed at the beginning and end of the program while running at a speed of 90% of their maximum aerobic speed (MAS) using surface sensor dynamic electromyography and kinematic analysis. We used classification tree (random forest) techniques from the field of artificial intelligence to identify linear and non-linear relationships between different biomechanical patterns and injuries to identify which styles best prevent injuries. Results: Fewer injuries occurred after completing the program, with athletes showing less pelvic fall and greater activation in gluteus medius during the first phase of the float phase, with increased trunk extension, knee flexion, and decreased ankle dorsiflexion during the initial contact with the ground. Conclusions: The triathletes who had suffered the most injuries ran with increased pelvic drop and less activation in gluteus medius during the first phase of the float phase. Contralateral pelvic drop seems to be an important variable in the incidence of injuries in young triathletes. Full article
(This article belongs to the Special Issue Wearable Sensors & Gait)
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Systematic Review
Is This the Real Life, or Is This Just Laboratory? A Scoping Review of IMU-Based Running Gait Analysis
Sensors 2022, 22(5), 1722; https://doi.org/10.3390/s22051722 - 23 Feb 2022
Cited by 1 | Viewed by 952
Abstract
Inertial measurement units (IMUs) can be used to monitor running biomechanics in real-world settings, but IMUs are often used within a laboratory. The purpose of this scoping review was to describe how IMUs are used to record running biomechanics in both laboratory and [...] Read more.
Inertial measurement units (IMUs) can be used to monitor running biomechanics in real-world settings, but IMUs are often used within a laboratory. The purpose of this scoping review was to describe how IMUs are used to record running biomechanics in both laboratory and real-world conditions. We included peer-reviewed journal articles that used IMUs to assess gait quality during running. We extracted data on running conditions (indoor/outdoor, surface, speed, and distance), device type and location, metrics, participants, and purpose and study design. A total of 231 studies were included. Most (72%) studies were conducted indoors; and in 67% of all studies, the analyzed distance was only one step or stride or <200 m. The most common device type and location combination was a triaxial accelerometer on the shank (18% of device and location combinations). The most common analyzed metric was vertical/axial magnitude, which was reported in 64% of all studies. Most studies (56%) included recreational runners. For the past 20 years, studies using IMUs to record running biomechanics have mainly been conducted indoors, on a treadmill, at prescribed speeds, and over small distances. We suggest that future studies should move out of the lab to less controlled and more real-world environments. Full article
(This article belongs to the Special Issue Wearable Sensors & Gait)
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