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Sensor-Based Measurement of Human Motor Performance
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Sensor-Based Measurement of Human Motor Performance

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

Deadline for manuscript submissions: closed (10 December 2022) | Viewed by 21681

Special Issue Editors

Prof. Drew Smith

E-Mail Website
Guest Editor
Motion Analysis Research Center, Samuel Merritt University, Oakland, CA 94609, USA
Interests: posture; balance; gait; wearable technology
Prof. Dr. Del P. Wong

E-Mail Website
Guest Editor
College of Education, Psychology and Social Work, Flinders University, Adelaide, Australia
Interests: sport biomechanics; movement science; exercise in elderly
Special Issues, Collections and Topics in MDPI journals
Prof. Brian L. Davis

E-Mail Website
Guest Editor
Washkewicz College of Engineering, Cleveland State University, Cleveland, OH 44115, USA
Interests: biomechanics; biomedical engineering; biomedical sensors

Special Issue Information

Dear Colleagues,

The tools available for tracking three-dimensional human motor performance have expanded significantly in the past two decades from predominately optical-based technologies to include a wide range of approaches that include markerless video protocols, inertial monitoring units, accelerometry, and so on.

The focus of this Special Issue will be to showcase the latest applications of sensor-based technologies for analyzing human motor performance across a wide range of activities including, but not limited to, rehabilitation, gait, balance, activities of daily living, ergonomics, and sports.

Prof. Drew Smith
Prof. Del P. Wong
Prof. Brian L. Davis
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 2600 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

  • inertial monitoring units
  • markerless motion capture
  • accelerometry
  • biomedical sensors

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Published Papers (6 papers)

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Research

16 pages, 1913 KiB  
Article
Synthesising 2D Video from 3D Motion Data for Machine Learning Applications
by Marion Mundt, Henrike Oberlack, Molly Goldacre, Julia Powles, Johannes Funken, Corey Morris, Wolfgang Potthast and Jacqueline Alderson
Sensors 2022, 22(17), 6522; https://doi.org/10.3390/s22176522 - 29 Aug 2022
Cited by 5 | Viewed by 2946
Abstract
To increase the utility of legacy, gold-standard, three-dimensional (3D) motion capture datasets for computer vision-based machine learning applications, this study proposed and validated a method to synthesise two-dimensional (2D) video image frames from historic 3D motion data. We applied the video-based human pose [...] Read more.
To increase the utility of legacy, gold-standard, three-dimensional (3D) motion capture datasets for computer vision-based machine learning applications, this study proposed and validated a method to synthesise two-dimensional (2D) video image frames from historic 3D motion data. We applied the video-based human pose estimation model OpenPose to real (in situ) and synthesised 2D videos and compared anatomical landmark keypoint outputs, with trivial observed differences (2.11–3.49 mm). We further demonstrated the utility of the method in a downstream machine learning use-case in which we trained and then tested the validity of an artificial neural network (ANN) to estimate ground reaction forces (GRFs) using synthesised and real 2D videos. Training an ANN to estimate GRFs using eight OpenPose keypoints derived from synthesised 2D videos resulted in accurate waveform GRF estimations (r > 0.9; nRMSE < 14%). When compared with using the smaller number of real videos only, accuracy was improved by adding the synthetic views and enlarging the dataset. The results highlight the utility of the developed approach to enlarge small 2D video datasets, or to create 2D video images to accompany 3D motion capture datasets to make them accessible for machine learning applications. Full article
(This article belongs to the Special Issue Sensor-Based Measurement of Human Motor Performance)
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9 pages, 512 KiB  
Article
Electromyographic Comparison of an Abdominal Rise on a Ball with a Traditional Crunch
by Aleš Dolenec, Mojca Svetina and Vojko Strojnik
Sensors 2022, 22(5), 1979; https://doi.org/10.3390/s22051979 - 3 Mar 2022
Cited by 1 | Viewed by 3112
Abstract
We propose a new exercise, the abdominal rise on the ball, to replace the traditional crunch in exercise programs. The aim of this study is to compare the activity of the abdominal muscles when performing an ARB with the same activity when performing [...] Read more.
We propose a new exercise, the abdominal rise on the ball, to replace the traditional crunch in exercise programs. The aim of this study is to compare the activity of the abdominal muscles when performing an ARB with the same activity when performing a traditional crunch. Twenty healthy adults participated in the study. Surface electromyography (EMG) was recorded from the upper and lower rectus abdominis (URA, LRA), internal oblique (IO), external oblique (EO), transversus abdominis (TrA), and erector spinae (ES). EMG values were normalized to maximal voluntary isometric contraction. A paired t-test, nonparametric Wilcoxon test and correlation coefficient were used for statistical analysis. The normalized EMG values of EO, TrA and ES, were statistically significantly higher during the abdominal rise on the ball compared to the traditional crunch, while URA, LRA and IO were significantly lower during the abdominal rise on the ball compared to the traditional crunch. TrA, EO and IO are sufficiently activated during an abdominal rise on a ball, so the exercise could be deemed effective for strengthening these muscles. Full article
(This article belongs to the Special Issue Sensor-Based Measurement of Human Motor Performance)
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11 pages, 1680 KiB  
Communication
Development of an Inexpensive Harnessing System Allowing Independent Gardening for Balance Training for Mobility Impaired Individuals
by McPherson Newell, Ann Reinthal, Debbie Espy and Beth Ekelman
Sensors 2021, 21(16), 5610; https://doi.org/10.3390/s21165610 - 20 Aug 2021
Viewed by 1945
Abstract
Balance is key to independent mobility, and poor balance leads to a risk of falling and subsequent injury that can cause self-restriction of activity for older adults. Balance and mobility can be improved through training programs, but many programs are not intensive or [...] Read more.
Balance is key to independent mobility, and poor balance leads to a risk of falling and subsequent injury that can cause self-restriction of activity for older adults. Balance and mobility can be improved through training programs, but many programs are not intensive or engaging enough to sufficiently improve balance while maintaining adherence. As an alternative to traditional balance training, harnessed gardening sessions were conducted in an urban greenhouse as an example of a community activity through which balance and mobility can be trained and/or maintained. An inexpensive multidirectional harness system was developed that can be used as an assistive or rehabilitative device in community, private, and senior center gardens to allow balance or mobility-impaired adults to participate in programming. Two wearable sensor systems were used to measure responses to the system: the Polhemus G4 system measured gardeners’ positions and center of mass relative to the base of support, and ActiGraph activity monitors measured the frequency and intensity of arm movements in garden as compared to home environments. The harnessed gardening system provides a safe environment for intense movement activity and can be used as a rehabilitation device along with wearable sensor systems to monitor ongoing changes. Full article
(This article belongs to the Special Issue Sensor-Based Measurement of Human Motor Performance)
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22 pages, 11924 KiB  
Article
NE-Motion: Visual Analysis of Stroke Patients Using Motion Sensor Networks
by Rodrigo Colnago Contreras, Avinash Parnandi, Bruno Gomes Coelho, Claudio Silva, Heidi Schambra and Luis Gustavo Nonato
Sensors 2021, 21(13), 4482; https://doi.org/10.3390/s21134482 - 30 Jun 2021
Cited by 2 | Viewed by 2737
Abstract
A large number of stroke survivors suffer from a significant decrease in upper extremity (UE) function, requiring rehabilitation therapy to boost recovery of UE motion. Assessing the efficacy of treatment strategies is a challenging problem in this context, and is typically accomplished by [...] Read more.
A large number of stroke survivors suffer from a significant decrease in upper extremity (UE) function, requiring rehabilitation therapy to boost recovery of UE motion. Assessing the efficacy of treatment strategies is a challenging problem in this context, and is typically accomplished by observing the performance of patients during their execution of daily activities. A more detailed assessment of UE impairment can be undertaken with a clinical bedside test, the UE Fugl–Meyer Assessment, but it fails to examine compensatory movements of functioning body segments that are used to bypass impairment. In this work, we use a graph learning method to build a visualization tool tailored to support the analysis of stroke patients. Called NE-Motion, or Network Environment for Motion Capture Data Analysis, the proposed analytic tool handles a set of time series captured by motion sensors worn by patients so as to enable visual analytic resources to identify abnormalities in movement patterns. Developed in close collaboration with domain experts, NE-Motion is capable of uncovering important phenomena, such as compensation while revealing differences between stroke patients and healthy individuals. The effectiveness of NE-Motion is shown in two case studies designed to analyze particular patients and to compare groups of subjects. Full article
(This article belongs to the Special Issue Sensor-Based Measurement of Human Motor Performance)
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11 pages, 1711 KiB  
Article
Measuring Gait Velocity and Stride Length with an Ultrawide Bandwidth Local Positioning System and an Inertial Measurement Unit
by Pratham Singh, Michael Esposito, Zach Barrons, Christian A. Clermont, John Wannop and Darren Stefanyshyn
Sensors 2021, 21(9), 2896; https://doi.org/10.3390/s21092896 - 21 Apr 2021
Cited by 10 | Viewed by 3121
Abstract
One possible modality to profile gait speed and stride length includes using wearable technologies. Wearable technology using global positioning system (GPS) receivers may not be a feasible means to measure gait speed. An alternative may include a local positioning system (LPS). Considering that [...] Read more.
One possible modality to profile gait speed and stride length includes using wearable technologies. Wearable technology using global positioning system (GPS) receivers may not be a feasible means to measure gait speed. An alternative may include a local positioning system (LPS). Considering that LPS wearables are not good at determining gait events such as heel strikes, applying sensor fusion with an inertial measurement unit (IMU) may be beneficial. Speed and stride length determined from an ultrawide bandwidth LPS equipped with an IMU were compared to video motion capture (i.e., the “gold standard”) as the criterion standard. Ninety participants performed trials at three self-selected walk, run and sprint speeds. After processing location, speed and acceleration data from the measurement systems, speed between the last five meters and stride length in the last stride of the trial were analyzed. Small biases and strong positive intraclass correlations (0.9–1.0) between the LPS and “the gold standard” were found. The significance of the study is that the LPS can be a valid method to determine speed and stride length. Variability of speed and stride length can be reduced when exploring data processing methods that can better extract speed and stride length measurements. Full article
(This article belongs to the Special Issue Sensor-Based Measurement of Human Motor Performance)
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16 pages, 29010 KiB  
Article
A Data-Driven Approach to Predict Fatigue in Exercise Based on Motion Data from Wearable Sensors or Force Plate
by Yanran Jiang, Vincent Hernandez, Gentiane Venture, Dana Kulić and Bernard K. Chen
Sensors 2021, 21(4), 1499; https://doi.org/10.3390/s21041499 - 22 Feb 2021
Cited by 32 | Viewed by 6245
Abstract
Fatigue increases the risk of injury during sports training and rehabilitation. Early detection of fatigue during exercises would help adapt the training in order to prevent over-training and injury. This study lays the foundation for a data-driven model to automatically predict the onset [...] Read more.
Fatigue increases the risk of injury during sports training and rehabilitation. Early detection of fatigue during exercises would help adapt the training in order to prevent over-training and injury. This study lays the foundation for a data-driven model to automatically predict the onset of fatigue and quantify consequent fatigue changes using a force plate (FP) or inertial measurement units (IMUs). The force plate and body-worn IMUs were used to capture movements associated with exercises (squats, high knee jacks, and corkscrew toe-touch) to estimate participant-specific fatigue levels in a continuous fashion using random forest (RF) regression and convolutional neural network (CNN) based regression models. Analysis of unseen data showed high correlation (up to 89%, 93%, and 94% for the squat, jack, and corkscrew exercises, respectively) between the predicted fatigue levels and self-reported fatigue levels. Predictions using force plate data achieved similar performance as those with IMU data; the best results in both cases were achieved with a convolutional neural network. The displacement of the center of pressure (COP) was found to be correlated with fatigue compared to other commonly used features of the force plate. Bland–Altman analysis also confirmed that the predicted fatigue levels were close to the true values. These results contribute to the field of human motion recognition by proposing a deep neural network model that can detect fairly small changes of motion data in a continuous process and quantify the movement. Based on the successful findings with three different exercises, the general nature of the methodology is potentially applicable to a variety of other forms of exercises, thereby contributing to the future adaptation of exercise programs and prevention of over-training and injury as a result of excessive fatigue. Full article
(This article belongs to the Special Issue Sensor-Based Measurement of Human Motor Performance)
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