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Non-contact Sensing Technologies for Motion Analysis and Health Monitoring

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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

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Keywords

vital signs
signal processing
health
machine learning
noncontact sensing

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21 pages, 8768 KiB

Open AccessArticle

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

Viewed by 284

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 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

Open AccessArticle

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

Viewed by 381

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 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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Non-contact Sensing Technologies for Motion Analysis and Health Monitoring

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