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Non-contact Sensing Technologies for Motion Analysis and Health Monitoring
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Non-contact Sensing Technologies for Motion Analysis and Health Monitoring

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

Deadline for manuscript submissions: closed (30 October 2024) | Viewed by 4579

Special Issue Editors

Dr. Gianluca Ciattaglia

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Guest Editor
Department of Information Engineering, Università Politecnica delle Marche, Via Brecce Bianche 12, 60131 Ancona, Italy
Interests: automotive radars; radar measurements; radar digital signal processing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Ennio Gambi

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Guest Editor
Department of Information Engineering, Università Politecnica delle Marche, Via Brecce Bianche 12, 60131 Ancona, Italy
Interests: communication systems and technologies for ambient-assisted living
Special Issues, Collections and Topics in MDPI journals
Dr. Agnese Sbrollini

E-Mail Website
Guest Editor
Department of Information Engineering, Università Politecnica delle Marche, Via Brecce Bianche 12, 60131 Ancona, Italy
Interests: bioengineering; biomedical signal processing; biostatistics; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Linda Senigagliesi

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Guest Editor
Department of Information Engineering, Università Politecnica delle Marche, Via Brecce Bianche 12, 60131 Ancona, Italy
Interests: communication systems; machine learning techniques for radar applications; physical layer security
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

There has been a great deal of research on the use of contactless sensors to monitor people’s health and motion, with the aim of improving their quality of life. Subject monitoring encompasses a wide range of applications, involving the use of extremely differentiated sensor technologies capable of monitoring many aspects of people’s health. In this context, various sensor technologies can be leveraged to obtain such information. These can be obtained from contact or noncontact sensing, directly from sensors, by applying signal processing techniques or machine learning and deep learning approaches. Since the recognition of human health characteristics can be crucial to improving and preserving people’s lives, the progress of the technologies in this field is extremely exciting and pioneering.

This Special Issue intends to collect contributions of the most recent research in the context of people motion and health monitoring based on contactless technologies.

Prof. Dr. Ennio Gambi
Dr. Gianluca Ciattaglia
Dr. Agnese Sbrollini
Dr. Linda Senigagliesi
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

  • vital signs
  • signal processing
  • health
  • machine learning
  • noncontact sensing

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Published Papers (3 papers)

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Research

16 pages, 21005 KiB  
Article
Measuring Changes in Upper Body Movement Due to Fasting Using a Camera
by Longfei Chen, Muhammad Ahmed Raza, Imran Saied, Tughrul Arslan and Robert B. Fisher
Sensors 2024, 24(22), 7242; https://doi.org/10.3390/s24227242 - 13 Nov 2024
Viewed by 745
Abstract
Understanding activity levels during fasting is important for promoting healthy fasting practices. While most existing studies focus on step counts to objectively assess the impact of fasting on activity levels and behavioral changes, the results have been mixed. Despite evidence showing that individuals [...] Read more.
Understanding activity levels during fasting is important for promoting healthy fasting practices. While most existing studies focus on step counts to objectively assess the impact of fasting on activity levels and behavioral changes, the results have been mixed. Despite evidence showing that individuals spend a significant amount of time sitting while fasting, there has been no objective measurement of body movement or activity levels during sitting and fasting. This research employs a video-based, unobtrusive human body movement measurement system to monitor upper body movements during fasting and non-fasting periods over several days. Key movement features, such as inactivity, movement speed, and movement scale, were automatically extracted from the video monitoring data using a computer vision pipeline. These features were then statistically compared using t tests between fasting and non-fasting periods, analyzed by hour of the day and across different days. The results of the monitoring of five participants during typical daily sitting office work and fasting for 3–5 days indicate no consistent pattern of upper body movement changes due to fasting among the participants. Full article
(This article belongs to the Special Issue Non-contact Sensing Technologies for Motion Analysis and Health Monitoring)
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20 pages, 8768 KiB  
Article
ML-Based Edge Node for Monitoring Peoples’ Frailty Status
by Antonio Nocera, Linda Senigagliesi, Gianluca Ciattaglia, Michela Raimondi and Ennio Gambi
Sensors 2024, 24(13), 4386; https://doi.org/10.3390/s24134386 - 5 Jul 2024
Cited by 1 | Viewed by 1390
Abstract
The development of contactless methods to assess the degree of personal hygiene in elderly people is crucial for detecting frailty and providing early intervention to prevent complete loss of autonomy, cognitive impairment, and hospitalisation. The unobtrusive nature of the technology is essential in [...] Read more.
The development of contactless methods to assess the degree of personal hygiene in elderly people is crucial for detecting frailty and providing early intervention to prevent complete loss of autonomy, cognitive impairment, and hospitalisation. The unobtrusive nature of the technology is essential in the context of maintaining good quality of life. The use of cameras and edge computing with sensors provides a way of monitoring subjects without interrupting their normal routines, and has the advantages of local data processing and improved privacy. This work describes the development an intelligent system that takes the RGB frames of a video as input to classify the occurrence of brushing teeth, washing hands, and fixing hair. No action activity is considered. The RGB frames are first processed by two Mediapipe algorithms to extract body keypoints related to the pose and hands, which represent the features to be classified. The optimal feature extractor results from the most complex Mediapipe pose estimator combined with the most complex hand keypoint regressor, which achieves the best performance even when operating at one frame per second. The final classifier is a Light Gradient Boosting Machine classifier that achieves more than 94% weighted F1-score under conditions of one frame per second and observation times of seven seconds or more. When the observation window is enlarged to ten seconds, the F1-scores for each class oscillate between 94.66% and 96.35%. Full article
(This article belongs to the Special Issue Non-contact Sensing Technologies for Motion Analysis and Health Monitoring)
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17 pages, 3007 KiB  
Article
A Novel AI Approach for Assessing Stress Levels in Patients with Type 2 Diabetes Mellitus Based on the Acquisition of Physiological Parameters Acquired during Daily Life
by Gonçalo Ribeiro, João Monge, Octavian Postolache and José Miguel Dias Pereira
Sensors 2024, 24(13), 4175; https://doi.org/10.3390/s24134175 - 27 Jun 2024
Cited by 2 | Viewed by 1972
Abstract
Stress is the inherent sensation of being unable to handle demands and occurrences. If not properly managed, stress can develop into a chronic condition, leading to the onset of additional chronic health issues, such as cardiovascular illnesses and diabetes. Various stress meters have [...] Read more.
Stress is the inherent sensation of being unable to handle demands and occurrences. If not properly managed, stress can develop into a chronic condition, leading to the onset of additional chronic health issues, such as cardiovascular illnesses and diabetes. Various stress meters have been suggested in the past, along with diverse approaches for its estimation. However, in the case of more serious health issues, such as hypertension and diabetes, the results can be significantly improved. This study presents the design and implementation of a distributed wearable-sensor computing platform with multiple channels. The platform aims to estimate the stress levels in diabetes patients by utilizing a fuzzy logic algorithm that is based on the assessment of several physiological indicators. Additionally, a mobile application was created to monitor the users’ stress levels and integrate data on their blood pressure and blood glucose levels. To obtain better performance metrics, validation experiments were carried out using a medical database containing data from 128 patients with chronic diabetes, and the initial results are presented in this study. Full article
(This article belongs to the Special Issue Non-contact Sensing Technologies for Motion Analysis and Health Monitoring)
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