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Wearable Sensors for Biomechanical Gait Analysis
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Wearable Sensors for Biomechanical Gait Analysis

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

Deadline for manuscript submissions: closed (28 May 2021) | Viewed by 12041

Special Issue Editors

Dr. Nizam Uddin Ahamed

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Guest Editor
Neuromuscular Research Laboratory, Department of Sports Medicine and Nutrition, University of Pittsburgh, Pittsburgh, PA 15203, USA
Interests: wearable technology; biomechanics; machine learning/artificial intelligence; biomedical signal processing; physiological signal (EMG; MMG)
Prof. Dr. Chris Connaboy

E-Mail Website
Guest Editor
Neuromuscular Research Laboratory, Department of Sports Medicine and Nutrition, University of Pittsburgh, Pittsburgh, PA 15203, USA
Interests: wearable sensors; gait biomechanics; motor control and motor learning
Prof. Dr. Qi Mi

E-Mail Website
Guest Editor
Neuromuscular Research Laboratory, Department of Sports Medicine and Nutrition, University of Pittsburgh, Pittsburgh, PA 15203, USA
Interests: data mining and machine learning; systems biology
Dr. Maria de Fátima Domingues

E-Mail Website
Guest Editor
Instituto de Telecomunicações and University of Aveiro, Campus Universitário de Santiago, 3810-193 Aveiro, Portugal
Interests: optical fiber sensors; fiber Bragg gratings; Fabry-Perot interformetric sensors; eHealth applications; gait analysis; wearable sensing devices
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recently, wearable sensors (like body-worn inertial sensors) are attracting substantial interest due to their potential to provide continuous, real-time functional information via dynamic, non-invasive measurements of biochemical markers in the gait cycle, such as kinetic and kinematic behaviors. These wearable sensors make it easy for researchers to collect gait biomechanics data in indoor (treadmill) and/or outdoor (natural, real-world) settings due to the many advantageous factors, such as small size, lightweight, easy to set up, portable, highly effective, and low cost. Using biomechanical gait data from multiple wearable wireless sensors (like fusing IMU and wrist-worn sensors) during various movement activities, paired with advanced signal processing and machine learning techniques can make clinical predictions and diagnostics tools to help track and treat musculoskeletal disorders, injuries, and performance assessment. This Special Issue of the Sensors journal entitled “Wearable Sensors for Biomechanical Gait Analysis” will focus on all aspects of research and development related to these areas. This Special Issue focuses on the development, validity, use, and applicability of wearable devices in biomechanical gait pattern identification. The broader aim is to collect high-quality papers from researchers around the world working in this area to make biomechanical gait monitoring more widespread and more effective using wearable technologies.

Dr. Nizam Uddin Ahamed
Prof. Dr. Chris Connaboy
Prof. Dr. Qi Mi
Dr. Maria de Fátima Domingues
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

  • Wearable sensor data
  • Fusing wearable sensors
  • Sensor-based signal processing in gait biomechanics
  • Wearable gait measurement
  • Biomechanical movement
  • Wearable and machine-learning approach
  • Inertial measurement unit (IMU)
  • Biomechanical gait analysis

Published Papers (3 papers)

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Research

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17 pages, 1982 KiB  
Article
A Framework for Maternal Physical Activities and Health Monitoring Using Wearable Sensors
by Farman Ullah, Asif Iqbal, Sumbul Iqbal, Daehan Kwak, Hafeez Anwar, Ajmal Khan, Rehmat Ullah, Huma Siddique and Kyung-Sup Kwak
Sensors 2021, 21(15), 4949; https://doi.org/10.3390/s21154949 - 21 Jul 2021
Cited by 11 | Viewed by 2916
Abstract
We propose a physical activity recognition and monitoring framework based on wearable sensors during maternity. A physical activity can either create or prevent health issues during a given stage of pregnancy depending on its intensity. Thus, it becomes very important to provide continuous [...] Read more.
We propose a physical activity recognition and monitoring framework based on wearable sensors during maternity. A physical activity can either create or prevent health issues during a given stage of pregnancy depending on its intensity. Thus, it becomes very important to provide continuous feedback by recognizing a physical activity and its intensity. However, such continuous monitoring is very challenging during the whole period of maternity. In addition, maintaining a record of each physical activity, and the time for which it was performed, is also a non-trivial task. We aim at such problems by first recognizing a physical activity via the data of wearable sensors that are put on various parts of body. We avoid the use of smartphones for such task due to the inconvenience caused by wearing it for activities such as “eating”. In our proposed framework, a module worn on body consists of three sensors: a 3-axis accelerometer, 3-axis gyroscope, and temperature sensor. The time-series data from these sensors are sent to a Raspberry-PI via Bluetooth Low Energy (BLE). Various statistical measures (features) of this data are then calculated and represented in features vectors. These feature vectors are then used to train a supervised machine learning algorithm called classifier for the recognition of physical activity from the sensors data. Based on such recognition, the proposed framework sends a message to the care-taker in case of unfavorable situation. We evaluated a number of well-known classifiers on various features developed from overlapped and non-overlapped window size of time-series data. Our novel dataset consists of 10 physical activities performed by 61 subjects at various stages of maternity. On the current dataset, we achieve the highest recognition rate of 89% which is encouraging for a monitoring and feedback system. Full article
(This article belongs to the Special Issue Wearable Sensors for Biomechanical Gait Analysis)
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13 pages, 3098 KiB  
Communication
EMG and Joint Angle-Based Machine Learning to Predict Future Joint Angles at the Knee
by Jordan Coker, Howard Chen, Mark C. Schall, Jr., Sean Gallagher and Michael Zabala
Sensors 2021, 21(11), 3622; https://doi.org/10.3390/s21113622 - 22 May 2021
Cited by 16 | Viewed by 4322
Abstract
Electromyography (EMG) is commonly used to measure electrical activity of the skeletal muscles. As exoskeleton technology advances, these signals may be used to predict human intent for control purposes. This study used an artificial neural network trained and tested with knee flexion angles [...] Read more.
Electromyography (EMG) is commonly used to measure electrical activity of the skeletal muscles. As exoskeleton technology advances, these signals may be used to predict human intent for control purposes. This study used an artificial neural network trained and tested with knee flexion angles and knee muscle EMG signals to predict knee flexion angles during gait at 50, 100, 150, and 200 ms into the future. The hypothesis of this study was that the algorithm’s prediction accuracy would only be affected by time into the future, not subject, gender or side, and that as time into the future increased, the prediction accuracy would decrease. A secondary hypothesis was that as the number of algorithm training trials increased, the prediction accuracy of the artificial neural network (ANN) would increase. The results of this study indicate that only time into the future affected the accuracy of knee flexion angle prediction (p < 0.001), whereby greater time resulted in reduced accuracy (0.68 to 4.62 degrees root mean square error (RMSE) from 50 to 200 ms). Additionally, increased number of training trials resulted in increased angle prediction accuracy. Full article
(This article belongs to the Special Issue Wearable Sensors for Biomechanical Gait Analysis)
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27 pages, 1098 KiB  
Systematic Review
The Contribution of Machine Learning in the Validation of Commercial Wearable Sensors for Gait Monitoring in Patients: A Systematic Review
by Théo Jourdan, Noëlie Debs and Carole Frindel
Sensors 2021, 21(14), 4808; https://doi.org/10.3390/s21144808 - 14 Jul 2021
Cited by 24 | Viewed by 3728
Abstract
Gait, balance, and coordination are important in the development of chronic disease, but the ability to accurately assess these in the daily lives of patients may be limited by traditional biased assessment tools. Wearable sensors offer the possibility of minimizing the main limitations [...] Read more.
Gait, balance, and coordination are important in the development of chronic disease, but the ability to accurately assess these in the daily lives of patients may be limited by traditional biased assessment tools. Wearable sensors offer the possibility of minimizing the main limitations of traditional assessment tools by generating quantitative data on a regular basis, which can greatly improve the home monitoring of patients. However, these commercial sensors must be validated in this context with rigorous validation methods. This scoping review summarizes the state-of-the-art between 2010 and 2020 in terms of the use of commercial wearable devices for gait monitoring in patients. For this specific period, 10 databases were searched and 564 records were retrieved from the associated search. This scoping review included 70 studies investigating one or more wearable sensors used to automatically track patient gait in the field. The majority of studies (95%) utilized accelerometers either by itself (N = 17 of 70) or embedded into a device (N = 57 of 70) and/or gyroscopes (51%) to automatically monitor gait via wearable sensors. All of the studies (N = 70) used one or more validation methods in which “ground truth” data were reported. Regarding the validation of wearable sensors, studies using machine learning have become more numerous since 2010, at 17% of included studies. This scoping review highlights the current state of the ability of commercial sensors to enhance traditional methods of gait assessment by passively monitoring gait in daily life, over long periods of time, and with minimal user interaction. Considering our review of the last 10 years in this field, machine learning approaches are algorithms to be considered for the future. These are in fact data-based approaches which, as long as the data collected are numerous, annotated, and representative, allow for the training of an effective model. In this context, commercial wearable sensors allowing for increased data collection and good patient adherence through efforts of miniaturization, energy consumption, and comfort will contribute to its future success. Full article
(This article belongs to the Special Issue Wearable Sensors for Biomechanical Gait Analysis)
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