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Sensors Technologies and Applications for Physical Activity Monitoring
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Sensors Technologies and Applications for Physical Activity Monitoring

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

Deadline for manuscript submissions: closed (2 January 2024) | Viewed by 3064

Special Issue Editor

Dr. Jungyoon Kim

E-Mail Website
Guest Editor
Department of Computer Science, Kent State University, Kent, OH 44242, USA
Interests: smart health and wellbeing; real-time cardiovascular disease; stress monitoring; physiological sensor design; intelligent analytics for decision supports; environmental monitoring and assessment; air quality monitoring; ubiquitous computing; embedded system design; energy efficient processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This special issue focuses on the advancements in sensor technologies and their applications for physical activity monitoring. The integration of sensors into wearable devices, smartphones, and specialized monitoring systems has revolutionized the way we track and analyze physical activity, leading to improved health and well-being outcomes. This issue aims to explore the latest developments, challenges, and potential solutions in the field of sensor technologies for physical activity monitoring, providing insights into the diverse range of sensors, their applications, and their impact on promoting an active lifestyle. This special issue contributes to the field of sensors by exploring the latest innovations and applications specific to physical activity monitoring. It covers various types of sensors used in wearable devices, smartphones, and specialized monitoring systems, emphasizing their integration, data analytics, and the impact on health and well-being outcomes. By bringing together research on sensor technologies for physical activity monitoring, this special issue provides valuable insights into the advancements in the field and promotes the dissemination of knowledge to researchers, practitioners, and industry professionals in the sensor community.

Dr. Jungyoon Kim
Guest Editor

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

  • sensing technologies
  • physical activity monitoring
  • wearable devices
  • health tracking
  • sensor fusion
  • data analytics
  • activity recognition
  • well-being assessment
  • IoT system

Published Papers (3 papers)

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Research

24 pages, 8534 KiB  
Article
Inertial Measuring System to Evaluate Gait Parameters and Dynamic Alignments for Lower-Limb Amputation Subjects
by Shao-Li Han, Meng-Lin Cai and Min-Chun Pan
Sensors 2024, 24(5), 1519; https://doi.org/10.3390/s24051519 - 26 Feb 2024
Viewed by 771
Abstract
The study aims to construct an inertial measuring system for the application of amputee subjects wearing a prosthesis. A new computation scheme to process inertial data by installing seven wireless inertial sensors on the lower limbs was implemented and validated by comparing it [...] Read more.
The study aims to construct an inertial measuring system for the application of amputee subjects wearing a prosthesis. A new computation scheme to process inertial data by installing seven wireless inertial sensors on the lower limbs was implemented and validated by comparing it with an optical motion capture system. We applied this system to amputees to verify its performance for gait analysis. The gait parameters are evaluated to objectively assess the amputees’ prosthesis-wearing status. The Madgwick algorithm was used in the study to correct the angular velocity deviation using acceleration data and convert it to quaternion. Further, the zero-velocity update method was applied to reconstruct patients’ walking trajectories. The combination of computed walking trajectory with pelvic and lower limb joint motion enables sketching the details of motion via a stickman that helps visualize and animate the walk and gait of a test subject. Five participants with above-knee (n = 2) and below-knee (n = 3) amputations were recruited for gait analysis. Kinematic parameters were evaluated during a walking test to assess joint alignment and overall gait characteristics. Our findings support the feasibility of employing simple algorithms to achieve accurate and precise joint angle estimation and gait parameters based on wireless inertial sensor data. Full article
(This article belongs to the Special Issue Sensors Technologies and Applications for Physical Activity Monitoring)
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23 pages, 35782 KiB  
Article
Data-Driven Approach for Upper Limb Fatigue Estimation Based on Wearable Sensors
by Sophia Otálora, Marcelo E. V. Segatto, Maxwell E. Monteiro, Marcela Múnera, Camilo A. R. Díaz and Carlos A. Cifuentes
Sensors 2023, 23(22), 9291; https://doi.org/10.3390/s23229291 - 20 Nov 2023
Viewed by 1351
Abstract
Muscle fatigue is defined as a reduced ability to maintain maximal strength during voluntary contraction. It is associated with musculoskeletal disorders that affect workers performing repetitive activities, affecting their performance and well-being. Although electromyography remains the gold standard for measuring muscle fatigue, its [...] Read more.
Muscle fatigue is defined as a reduced ability to maintain maximal strength during voluntary contraction. It is associated with musculoskeletal disorders that affect workers performing repetitive activities, affecting their performance and well-being. Although electromyography remains the gold standard for measuring muscle fatigue, its limitations in long-term work motivate the use of wearable devices. This article proposes a computational model for estimating muscle fatigue using wearable and non-invasive devices, such as Optical Fiber Sensors (OFSs) and Inertial Measurement Units (IMUs) along the subjective Borg scale. Electromyography (EMG) sensors are used to observe their importance in estimating muscle fatigue and comparing performance in different sensor combinations. This study involves 30 subjects performing a repetitive lifting activity with their dominant arm until reaching muscle fatigue. Muscle activity, elbow angles, and angular and linear velocities, among others, are measured to extract multiple features. Different machine learning algorithms obtain a model that estimates three fatigue states (low, moderate and high). Results showed that between the machine learning classifiers, the LightGBM presented an accuracy of 96.2% in the classification task using all of the sensors with 33 features and 95.4% using only OFS and IMU sensors with 13 features. This demonstrates that elbow angles, wrist velocities, acceleration variations, and compensatory neck movements are essential for estimating muscle fatigue. In conclusion, the resulting model can be used to estimate fatigue during heavy lifting in work environments, having the potential to monitor and prevent muscle fatigue during long working shifts. Full article
(This article belongs to the Special Issue Sensors Technologies and Applications for Physical Activity Monitoring)
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13 pages, 1822 KiB  
Article
A Markov Chain Model for Determining the Optimal Time to Move Pregnant Cows to Individual Calving Pens
by Cho Nilar Phyo, Pyke Tin and Thi Thi Zin
Sensors 2023, 23(19), 8141; https://doi.org/10.3390/s23198141 - 28 Sep 2023
Viewed by 590
Abstract
The use of individual calving pens in modern farming is widely recognized as a good practice for promoting good animal welfare during parturition. However, determining the optimal time to move a pregnant cow to a calving pen can be a management challenge. Moving [...] Read more.
The use of individual calving pens in modern farming is widely recognized as a good practice for promoting good animal welfare during parturition. However, determining the optimal time to move a pregnant cow to a calving pen can be a management challenge. Moving cows too early may result in prolonged occupancy of the pen, while moving them too late may increase the risk of calving complications and production-related diseases. In this paper, a simple random walk type Markov Chain Model to predict the optimal time for moving periparturient cows to individual calving pens was proposed. Behavior changes such as lying time, standing time, and rumination time were analyzed using a video monitoring system, and we formulated these changes as the states of a Markov Chain with an absorbing barrier. The model showed that the first time entering an absorbing state was the optimal time for a pregnant cow to be moved to a calving pen. The proposed method was validated through a series of experiments in a real-life dairy farm, showing promising results with high accuracy. Full article
(This article belongs to the Special Issue Sensors Technologies and Applications for Physical Activity Monitoring)
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