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Sensors Network and Wearables for People Activities and Wellbeing Monitoring

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

Deadline for manuscript submissions: 20 May 2025 | Viewed by 12737

Special Issue Editors


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Guest Editor
Department of Industrial Engineering and Mathematical Sciences, Università Politecnica delle Marche, v. Brecce Bianche 12, 60131 Ancona, Italy
Interests: non-invasive measurement techniques; measurement procedures; measurement uncertainty; active and assisted-living solutions; sensors network; physiological and environmental signals; AI; comfort and wellbeing
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Industrial Engineering and Mathematical Sciences, Università Politecnica delle Marche, v. Brecce Bianche 12, 60131 Ancona, Italy
Interests: non-invasive measurement techniques; measurement procedures; measurement uncertainty; wearable sensors; physiological signals; comfort and wellbeing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The development of sensor networks and wearable technologies over the past years has given the possibility of extracting activities and behavioural parameters of people just using low-cost and non-invasive systems. Indeed, the gathered data can be processed with dedicated techniques, often based on artificial intelligence technologies. The monitoring of activities through non-invasive sensor networks and wearable sensors has been demonstrated to be able to depict the user’s well-being, comfort, and global health status in living environments, both indoors and outdoors.

In this Special Issue, we call for papers presenting innovative solutions and signal processing techniques, e.g., artificial intelligence, to measure the wellbeing and activities of people in living environments, both indoor and outdoor, through sensors network and wearable sensors. The papers should properly consider the accuracy in the measurement of such quantities.

Dr. Sara Casaccia
Dr. Gloria Cosoli
Guest Editors

Manuscript Submission Information

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Keywords

  • sensor networks
  • wearable sensors
  • well-being
  • health
  • comfort
  • measurements
  • accuracy
  • living environment
  • data processing
  • artificial intelligence

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Published Papers (6 papers)

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Research

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9 pages, 1409 KiB  
Communication
Physical Frailty Prediction Using Cane Usage Characteristics during Walking
by Haruki Toda and Takaaki Chin
Sensors 2024, 24(21), 6910; https://doi.org/10.3390/s24216910 - 28 Oct 2024
Viewed by 789
Abstract
This study aimed to determine the characteristics of accelerations and angular velocities obtained by an inertial measurement unit (IMU) attached to a cane between older people with and without physical frailty. Community-dwelling older people walked at a comfortable speed using a cane with [...] Read more.
This study aimed to determine the characteristics of accelerations and angular velocities obtained by an inertial measurement unit (IMU) attached to a cane between older people with and without physical frailty. Community-dwelling older people walked at a comfortable speed using a cane with a built-in IMU. Physical frailty was assessed using exercise-related items extracted from the Kihon Check List. The efficacy of five machine learning models in distinguishing older people with physical frailty was investigated. This study included 48 older people, of which 24 were frail and 24 were not. Compared with the non-frail participants, the older people with physical frailty had a small root mean square value in the vertical and anteroposterior directions and angular velocity in the anteroposterior direction (p < 0.001, r = 0.36; p < 0.001, r = 0.29; p < 0.001, r = 0.30, respectively) and a large mean power frequency value in the vertical direction (p = 0.042, r = 0.18). The decision tree model could most effectively classify physical frailty, with an accuracy, F1 score, and area under the curve of 78.6%, 91.8%, and 0.81, respectively. The characteristics of IMU-attached cane usage by older adults with physical frailty can be utilized to effectively evaluate and determine physical frailty in their usual environments. Full article
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15 pages, 1480 KiB  
Article
Proximity Sensor for Measuring Social Interaction in a School Environment
by Tania Karina Hernández-Heredia, Cesar Fabián Reyes-Manzano, Diego Alonso Flores-Hernández, Gabriel Ramos-Fernández and Lev Guzmán-Vargas
Sensors 2024, 24(15), 4822; https://doi.org/10.3390/s24154822 - 25 Jul 2024
Viewed by 1156
Abstract
Social interactions are characterized by being very diverse and changing over time. Understanding this diversity and dynamics, as well as their emerging patterns, is of great interest from social, health, and educational perspectives. The development of new devices has been made possible in [...] Read more.
Social interactions are characterized by being very diverse and changing over time. Understanding this diversity and dynamics, as well as their emerging patterns, is of great interest from social, health, and educational perspectives. The development of new devices has been made possible in recent years by advances in applied technology. This paper presents the design and development of a novel device composed of several sensors. Specifically, we propose a proximity sensor integrated by three devices: a Bluetooth sensor, a global positioning system (GPS) unit and an accelerometer. By means of this sensor it is possible to detect the presence of neighboring sensors in various configurations and operating conditions. Profiles based on the Received Signal Strength Indicator (RSSI) exhibit behavior consistent with that reported by empirical relationships. The present sensor is functional in detecting the proximity of other sensors and is thus useful for the identification of interactions between people in relevant contexts such as schools. Full article
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17 pages, 5232 KiB  
Article
Implementation of Sound Direction Detection and Mixed Source Separation in Embedded Systems
by Jian-Hong Wang, Phuong Thi Le, Weng-Sheng Bee, Wenny Ramadha Putri, Ming-Hsiang Su, Kuo-Chen Li, Shih-Lun Chen, Ji-Long He, Tuan Pham, Yung-Hui Li and Jia-Ching Wang
Sensors 2024, 24(13), 4351; https://doi.org/10.3390/s24134351 - 4 Jul 2024
Viewed by 4069
Abstract
In recent years, embedded system technologies and products for sensor networks and wearable devices used for monitoring people’s activities and health have become the focus of the global IT industry. In order to enhance the speech recognition capabilities of wearable devices, this article [...] Read more.
In recent years, embedded system technologies and products for sensor networks and wearable devices used for monitoring people’s activities and health have become the focus of the global IT industry. In order to enhance the speech recognition capabilities of wearable devices, this article discusses the implementation of audio positioning and enhancement in embedded systems using embedded algorithms for direction detection and mixed source separation. The two algorithms are implemented using different embedded systems: direction detection developed using TI TMS320C6713 DSK and mixed source separation developed using Raspberry Pi 2. For mixed source separation, in the first experiment, the average signal-to-interference ratio (SIR) at 1 m and 2 m distances was 16.72 and 15.76, respectively. In the second experiment, when evaluated using speech recognition, the algorithm improved speech recognition accuracy to 95%. Full article
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18 pages, 4904 KiB  
Article
An Overall Automated Architecture Based on the Tapping Test Measurement Protocol: Hand Dexterity Assessment through an Innovative Objective Method
by Tommaso Di Libero, Chiara Carissimo, Gianni Cerro , Angela Marie Abbatecola , Alessandro Marino, Gianfranco Miele , Luigi Ferrigno  and Angelo Rodio
Sensors 2024, 24(13), 4133; https://doi.org/10.3390/s24134133 - 26 Jun 2024
Cited by 3 | Viewed by 3471
Abstract
The present work focuses on the tapping test, which is a method that is commonly used in the literature to assess dexterity, speed, and motor coordination by repeatedly moving fingers, performing a tapping action on a flat surface. During the test, the activation [...] Read more.
The present work focuses on the tapping test, which is a method that is commonly used in the literature to assess dexterity, speed, and motor coordination by repeatedly moving fingers, performing a tapping action on a flat surface. During the test, the activation of specific brain regions enhances fine motor abilities, improving motor control. The research also explores neuromuscular and biomechanical factors related to finger dexterity, revealing neuroplastic adaptation to repetitive movements. To give an objective evaluation of all cited physiological aspects, this work proposes a measurement architecture consisting of the following: (i) a novel measurement protocol to assess the coordinative and conditional capabilities of a population of participants; (ii) a suitable measurement platform, consisting of synchronized and non-invasive inertial sensors to be worn at finger level; (iii) a data analysis processing stage, able to provide the final user (medical doctor or training coach) with a plethora of useful information about the carried-out tests, going far beyond state-of-the-art results from classical tapping test examinations. Particularly, the proposed study underscores the importance interdigital autonomy for complex finger motions, despite the challenges posed by anatomical connections; this deepens our understanding of upper limb coordination and the impact of neuroplasticity, holding significance for motor abilities assessment, improvement, and therapeutic strategies to enhance finger precision. The proof-of-concept test is performed by considering a population of college students. The obtained results allow us to consider the proposed architecture to be valuable for many application scenarios, such as the ones related to neurodegenerative disease evolution monitoring. Full article
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13 pages, 8793 KiB  
Article
Agreement between Optoelectronic System and Wearable Sensors for the Evaluation of Gait Spatiotemporal Parameters in Progressive Supranuclear Palsy
by Carlo Ricciardi, Noemi Pisani, Leandro Donisi, Filomena Abate, Marianna Amboni, Paolo Barone, Marina Picillo, Mario Cesarelli and Francesco Amato
Sensors 2023, 23(24), 9859; https://doi.org/10.3390/s23249859 - 16 Dec 2023
Cited by 6 | Viewed by 1522
Abstract
The use of wearable sensors for calculating gait parameters has become increasingly popular as an alternative to optoelectronic systems, currently recognized as the gold standard. The objective of the study was to evaluate the agreement between the wearable Opal system and the optoelectronic [...] Read more.
The use of wearable sensors for calculating gait parameters has become increasingly popular as an alternative to optoelectronic systems, currently recognized as the gold standard. The objective of the study was to evaluate the agreement between the wearable Opal system and the optoelectronic BTS SMART DX system for assessing spatiotemporal gait parameters. Fifteen subjects with progressive supranuclear palsy walked at their self-selected speed on a straight path, and six spatiotemporal parameters were compared between the two measurement systems. The agreement was carried out through paired data test, Passing Bablok regression, and Bland-Altman Analysis. The results showed a perfect agreement for speed, a very close agreement for cadence and cycle duration, while, in the other cases, Opal system either under- or over-estimated the measurement of the BTS system. Some suggestions about these misalignments are proposed in the paper, considering that Opal system is widely used in the clinical context. Full article
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Review

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28 pages, 424 KiB  
Review
A Review on Assisted Living Using Wearable Devices
by Grazia Iadarola, Alessandro Mengarelli, Paolo Crippa, Sandro Fioretti and Susanna Spinsante
Sensors 2024, 24(23), 7439; https://doi.org/10.3390/s24237439 - 21 Nov 2024
Viewed by 948
Abstract
Forecasts about the aging trend of the world population agree on identifying increased life expectancy as a serious risk factor for the financial sustainability of social healthcare systems if not properly supported by innovative care management policies. Such policies should include the integration [...] Read more.
Forecasts about the aging trend of the world population agree on identifying increased life expectancy as a serious risk factor for the financial sustainability of social healthcare systems if not properly supported by innovative care management policies. Such policies should include the integration within traditional healthcare services of assistive technologies as tools for prolonging healthy and independent living at home, but also for introducing innovations in clinical practice such as long-term and remote health monitoring. For their part, solutions for active and assisted living have now reached a high degree of technological maturity, thanks to the considerable amount of research work carried out in recent years to develop highly reliable and energy-efficient wearable sensors capable of enabling the development of systems to monitor activity and physiological parameters over time, and in a minimally invasive manner. This work reviews the role of wearable sensors in the design and development of assisted living solutions, focusing on human activity recognition by joint use of onboard electromyography sensors and inertial measurement units and on the acquisition of parameters related to overall physical and psychological conditions, such as heart activity and skin conductance. Full article
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