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Wearable Sensor Systems for Human Activity Analysis

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 4268

Special Issue Editors


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Guest Editor
School of Electrical and Computer Engineering, Arts Media and Engineering, Arizona State University, Tempe, AZ 85281, USA
Interests: Computer Vision; Machine Learning; Geometry; Topology; Wearables

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Guest Editor
College of Health Solutions, Arizona State University, Phoenix, AZ 85004, USA
Interests: physical activity; sedentary behavior; sleep; mobile health; lifestyle interventions
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
School of Manufacturing Systems and Networks, Ira Fulton School of Engineering, Arizona State University, Mesa, AZ 85212, USA
Interests: physiological sensing; dynamics; control; robotics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

For this Special Issue, we invite papers on human activity analysis, with a focus on innovations in wearable sensors, systems, modeling, and algorithms. Applications of interest include health and wellness, human performance enhancement, interactive systems, and related areas. We especially invite submissions from interdisciplinary teams of researchers that bring to the fore the combination of a new application area with use-inspired engineering and computing advances. Potential contributors are encouraged to contact the editors so that they can judge whether the proposed submission fits with the scope of the Special Issue. Topics of interest include, but are not limited to, the following:

  1. Design of novel self-powered and low-power sensors;
  2. Multimodal sensor systems and small form factor sensors;
  3. Machine learning techniques applied to multimodal sensor data;
  4. Privacy preserving sensing and privacy preserving statistical models;
  5. Applications in health and disease management, e.g., Parkinson’s, stroke, and sedentary behavior;
  6. Human performance enhancement via real-time interactive systems.

Dr. Pavan Turaga
Dr. Matthew Buman
Dr. Sangram Redkar
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

  • self-powered and low-power sensors
  • multimodal sensor systems
  • machine learning
  • privacy preserving sensing
  • health and disease management
  • human performance enhancement

Published Papers (1 paper)

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Research

39 pages, 3098 KiB  
Article
Capturing Upper Body Kinematics and Localization with Low-Cost Sensors for Rehabilitation Applications
by Anik Sarker, Don-Roberts Emenonye, Aisling Kelliher, Thanassis Rikakis, R. Michael Buehrer and Alan T. Asbeck
Sensors 2022, 22(6), 2300; https://doi.org/10.3390/s22062300 - 16 Mar 2022
Cited by 6 | Viewed by 3389
Abstract
For upper extremity rehabilitation, quantitative measurements of a person’s capabilities during activities of daily living could provide useful information for therapists, including in telemedicine scenarios. Specifically, measurements of a person’s upper body kinematics could give information about which arm motions or movement features [...] Read more.
For upper extremity rehabilitation, quantitative measurements of a person’s capabilities during activities of daily living could provide useful information for therapists, including in telemedicine scenarios. Specifically, measurements of a person’s upper body kinematics could give information about which arm motions or movement features are in need of additional therapy, and their location within the home could give context to these motions. To that end, we present a new algorithm for identifying a person’s location in a region of interest based on a Bluetooth received signal strength (RSS) and present an experimental evaluation of this and a different Bluetooth RSS-based localization algorithm via fingerprinting. We further present algorithms for and experimental results of inferring the complete upper body kinematics based on three standalone inertial measurement unit (IMU) sensors mounted on the wrists and pelvis. Our experimental results for localization find the target location with a mean square error of 1.78 m. Our kinematics reconstruction algorithms gave lower errors with the pelvis sensor mounted on the person’s back and with individual calibrations for each test. With three standalone IMUs, the mean angular error for all of the upper body segment orientations was close to 21 degrees, and the estimated elbow and shoulder angles had mean errors of less than 4 degrees. Full article
(This article belongs to the Special Issue Wearable Sensor Systems for Human Activity Analysis)
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