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Inertial Sensing of Human Movement and Physiological Function

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

Deadline for manuscript submissions: closed (31 July 2021) | Viewed by 2429

Special Issue Editor


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Guest Editor
Electrical & Electronic Engineering, School of Engineering, NUI Galway, University Road, H91 HX31 Galway, Ireland
Interests: design of medical devices; human movement sensing; the application of neuromuscular electrical stimulation; connected health; usability considerations in the design of medical devices for home healthcare

Special Issue Information

Dear Colleagues,

Since the development of low-cost, commercial MEMS inertial sensors thirty years ago, there has been a rapid growth of research and development in the application of these sensors in health and wellness and sport and exercise.

This growth has been further driven in the last ten years by the incorporation of inertial sensors in smartphones and more recently by their incorporation in smartwatches. Whereas the primary application of inertial sensors in these domains is in human movement (e.g., step counting, falls detection, and lap counting in swimming), the range of human movement applications of inertial sensors is growing with new applications emerging, such as posture in the workplace, real-time range-of-motion assessment in rehabilitation, etc. There are also new applications of inertial sensors in the indirect measurement of elements of human physiological function (predicted cardiac demands) and this trend is set to expand and develop.

More and more inertial sensor data are being processed and analyzed using machine learning and artificial intelligence techniques with the growth of data and high-speed communications and these techniques are enhancing performance, making new applications possible.

With telemedicine being fast-tracked worldwide due to the COVID-19 pandemic, more use of inertial sensing in a telemedicine/connected health context is occurring as a means to autonomously track different aspects of human movement and physiological function in the context of assessing the effectiveness of clinician prescribed therapeutic programs. 

This Special Issue invites articles featuring original research, as well as review papers covering the full gamut of the application of inertial sensing in human movement and physiological function.

Prof. Dr. Gearóid ÓLaighin
Guest Editor

Manuscript Submission Information

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Keywords

  • Inertial sensing
  • Human movement
  • Human function
  • Motion analysis
  • Gait analysis
  • Movement analysis

Published Papers (1 paper)

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Research

15 pages, 1798 KiB  
Article
Identification of Movements and Postures Using Wearable Sensors for Implementation in a Bi-Hormonal Artificial Pancreas System
by Ben Sawaryn, Michel Klaassen, Bert-Jan van Beijnum, Hans Zwart and Peter H. Veltink
Sensors 2021, 21(17), 5954; https://doi.org/10.3390/s21175954 - 05 Sep 2021
Cited by 2 | Viewed by 1869
Abstract
Background: Closed loop bi-hormonal artificial pancreas systems, such as the artificial pancreas (AP™) developed by Inreda Diabetic B.V., control blood glucose levels of type 1 diabetes mellitus patients via closed loop regulation. As the AP™ currently does not classify postures and movements to [...] Read more.
Background: Closed loop bi-hormonal artificial pancreas systems, such as the artificial pancreas (AP™) developed by Inreda Diabetic B.V., control blood glucose levels of type 1 diabetes mellitus patients via closed loop regulation. As the AP™ currently does not classify postures and movements to estimate metabolic energy consumption to correct hormone administration levels, considerable improvements to the system can be made. Therefore, this research aimed to investigate the possibility to use the current system to identify several postures and movements. Methods: seven healthy participants took part in an experiment where sequences of postures and movements were performed to train and assess a computationally sparing algorithm. Results: Using accelerometers, one on the hip and two on the abdomen, user-specific models achieved classification accuracies of 86.5% using only the hip sensor and 87.3% when including the abdomen sensors. With additional accelerometers on the sternum and upper leg for identification, 90.0% of the classified postures and movements were correct. Conclusions: The current hardware configuration of the AP™ poses no limitation to the identification of postures and movements. If future research shows that identification can still be done accurately in a daily life setting, this algorithm may be an improvement for the AP™ to sense physical activity. Full article
(This article belongs to the Special Issue Inertial Sensing of Human Movement and Physiological Function)
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