Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.8
3.9
Journals
Sensors
Special Issues
Sensors and Sensing Technologies for Activity and Motion Detection and...
sensors-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Sensors Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Sensors and Sensing Technologies for Activity and Motion Detection and Recognition

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

Deadline for manuscript submissions: 19 July 2024 | Viewed by 639

Special Issue Editor

Prof. Dr. Frederic Marin

E-Mail Website
Guest Editor
Centre of Excellence for Human and Animal Movement Biomechanics, Université de Technologie de Compiègne (UTC), UMR BMBI CNRS 7338, Alliance Sorbonne Université, Paris, France
Interests: motion capture; motion analysis; inertial sensors; biomechanics; osteo-articular modeling; musculoskeletal modeling; physical activities monitoring
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Sensors and sensing technologies are a thriving area of research, especially in the context of activity and motion detection and recognition. This not only encompasses human activities but also the behavior of animals and object tracking. In fact, advancements and the emergence of next-generation sensing and sensor technologies enable the monitoring of motion not only in close and open spaces but also over both short and extended periods of time. Versatility and ubiquity represent emerging trends in sensing and sensor technologies. In tandem with the raw data generated by hardware technology, post-processing through dedicated algorithms not only facilitates detection but also recognition. Furthermore, machine learning techniques, including supervised and unsupervised methods, offer a variety of approaches. Both time and memory cost calculation of algorithms are as important as the accuracy and precision of the motion detection and recognition to evaluate the portability or the efficiency of the software solution.

The objective of this Special Issue is to present the current state of the art in Sensors and Sensing Technologies for Activity and Motion Detection and Recognition. Therefore, we will showcase various applications and innovative approaches in both hardware and software domains.

Prof. Dr. Frederic Marin
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

  • activity detection
  • motion detection
  • activity recognition
  • motion recognition
  • sensing technique

Published Papers (1 paper)

Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

16 pages, 1618 KiB  
Article
MocapMe: DeepLabCut-Enhanced Neural Network for Enhanced Markerless Stability in Sit-to-Stand Motion Capture
by Dario Milone, Francesco Longo, Giovanni Merlino, Cristiano De Marchis, Giacomo Risitano and Luca D’Agati
Sensors 2024, 24(10), 3022; https://0-doi-org.brum.beds.ac.uk/10.3390/s24103022 - 10 May 2024
Viewed by 430
Abstract
This study examined the efficacy of an optimized DeepLabCut (DLC) model in motion capture, with a particular focus on the sit-to-stand (STS) movement, which is crucial for assessing the functional capacity in elderly and postoperative patients. This research uniquely compared the performance of [...] Read more.
This study examined the efficacy of an optimized DeepLabCut (DLC) model in motion capture, with a particular focus on the sit-to-stand (STS) movement, which is crucial for assessing the functional capacity in elderly and postoperative patients. This research uniquely compared the performance of this optimized DLC model, which was trained using ’filtered’ estimates from the widely used OpenPose (OP) model, thereby emphasizing computational effectiveness, motion-tracking precision, and enhanced stability in data capture. Utilizing a combination of smartphone-captured videos and specifically curated datasets, our methodological approach included data preparation, keypoint annotation, and extensive model training, with an emphasis on the flow of the optimized model. The findings demonstrate the superiority of the optimized DLC model in various aspects. It exhibited not only higher computational efficiency, with reduced processing times, but also greater precision and consistency in motion tracking thanks to the stability brought about by the meticulous selection of the OP data. This precision is vital for developing accurate biomechanical models for clinical interventions. Moreover, this study revealed that the optimized DLC maintained higher average confidence levels across datasets, indicating more reliable and accurate detection capabilities compared with standalone OP. The clinical relevance of these findings is profound. The optimized DLC model’s efficiency and enhanced point estimation stability make it an invaluable tool in rehabilitation monitoring and patient assessments, potentially streamlining clinical workflows. This study suggests future research directions, including integrating the optimized DLC model with virtual reality environments for enhanced patient engagement and leveraging its improved data quality for predictive analytics in healthcare. Overall, the optimized DLC model emerged as a transformative tool for biomechanical analysis and physical rehabilitation, promising to enhance the quality of patient care and healthcare delivery efficiency. Full article
(This article belongs to the Special Issue Sensors and Sensing Technologies for Activity and Motion Detection and Recognition)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-1
Back to TopTop