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Human Motion Monitoring and Modeling

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

Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 2514

Special Issue Editors

Computer Science and Engineering, University of California Merced, Merced, CA 95343, USA
Interests: cyber-physical systems; Internet of Things; ubiquitous computing
Department of Computer Science, Stony Brook University, Stony Brook, NY 11794, USA
Interests: Cyber-Physical Systems; Mobile Health; Ubiquitous Computing; Smart Environments
Computer Science and Engineering, The University of Texas at Arlington, Arlington, TX 12329, USA
Interests: Cyber-Physical Systems; Internet of Things; Mobile Health; Mobile System Security

Special Issue Information

Dear Colleagues,

Long-term continuous monitoring and modeling of human motion could enable various new applications of Internet of Things (e.g., smart homes, new HCIs, patient/elderly care) as well as novel diagnostic tools (e.g., fall risk, dementia, rehabilitation). Therefore, non-intrusive, fine-grained, and accurate human motion monitoring and modeling have become essential for various smart applications. Current state-of-the-art solutions include various on-body and off-body sensors as well as multimodal heterogeneous sensing systems. The challenges include and are not limited to 1) limited labeled data, 2) limited devices/sensors, 3) limited computational resources, 4) personalization and user variance, 5) behavior profiling and anomaly detection, and 6) system quantification and optimization. Potential submissions could cover 1) the development of new sensors, 2) repurposing existing sensors, and/or 3) combining heterogeneous sensors to capture human motion.

Submissions could also cover methods and systems that acquire finer-grained human motion and/or utilize captured human motion to achieve behavior modeling.

Dr. Shijia Pan
Dr. Shubham Jain
Dr. VP Nguyen
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

  • human motion monitoring
  • fine-grained motion modeling
  • wearables
  • on-body/off-body sensing
  • multimodal
  • human variance

Published Papers (1 paper)

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Research

11 pages, 7970 KiB  
Article
HeadSLAM: Pedestrian SLAM with Head-Mounted Sensors
by Xinyu Hou and Jeroen Bergmann
Sensors 2022, 22(4), 1593; https://0-doi-org.brum.beds.ac.uk/10.3390/s22041593 - 18 Feb 2022
Cited by 4 | Viewed by 2115
Abstract
Research focused on human position tracking with wearable sensors has been developing rapidly in recent years, and it has shown great potential for application within healthcare, smart homes, sports, and emergency services. Pedestrian Dead Reckoning (PDR) with Inertial Measurement Units (IMUs) is one [...] Read more.
Research focused on human position tracking with wearable sensors has been developing rapidly in recent years, and it has shown great potential for application within healthcare, smart homes, sports, and emergency services. Pedestrian Dead Reckoning (PDR) with Inertial Measurement Units (IMUs) is one of the most promising solutions within this domain, as it does not rely on any additional infrastructure, whilst also being suitable for use in a diverse set of scenarios. However, PDR is only accurate for a limited period of time before unbounded errors, due to drift, affect the position estimate. Error correction can be difficult as there is often a lack of efficient methods for calibration. HeadSLAM, a method specifically designed for head-mounted IMUs, is proposed to improve the accuracy during longer tracking times (10 min). Research participants (n = 7) were asked to walk in both indoor and outdoor environments wearing head-mounted sensors, and the obtained HeadSLAM accuracy was subsequently compared to that of the PDR method. A significant difference (p < 0.001) in the average root-mean-squared error and absolute error was found between the two methods. HeadSLAM had a consist lower error across all scenarios and subjects in a 20 h walking dataset. The findings of this study show how the HeadSLAM algorithm can provide a more accurate long-term location service for head-mounted, low-cost sensors. The improved performance can support inexpensive applications for infrastructureless navigation. Full article
(This article belongs to the Special Issue Human Motion Monitoring and Modeling)
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