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Sensors Network and Wearables for People Activities and Wellbeing Monitoring
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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 8658

Special Issue Editors

Dr. Sara Casaccia

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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
Dr. Gloria Cosoli

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

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

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

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Published Papers (4 papers)

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Research

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 502
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
(This article belongs to the Special Issue Sensors Network and Wearables for People Activities and Wellbeing Monitoring)
►▼ Show Figures

Figure 1

Figure 1
<p>Physical architecture of the proximity sensor, including electrical power connections (red arrows) and data transfer (blue arrows).</p> Full article ">Figure 2
<p>Design of the electronic hardware for the modules integration, top view (<b>left</b>) and bottom view (<b>right</b>).</p> Full article ">Figure 3
<p>Flow diagram showing the configuration and working modes of the sensor.</p> Full article ">Figure 4
<p>Image of the physical location of the sensors S1 and S2 during the measurement of the mutual signals for two distance values.</p> Full article ">Figure 5
<p>RSSI signal strength for several distance (meters) values and time intervals. (<b>a</b>) Scans of sensor 1 using the data emitted by sensor 2. The values shown are measurements per distance performed every five seconds during eighth-minute time intervals. (<b>b</b>) Similar to (<b>a</b>), but showing the mean values obtained from a moving average with one-minute windows.</p> Full article ">Figure 6
<p>RSSI signal strength for several distance (meters) values and time intervals. (<b>a</b>) Scans of sensor 2 using the data emitted by sensor 1. The values shown are measurements per distance performed every five seconds during eight-minute time intervals. The reference value <math display="inline"><semantics> <mrow> <msub> <mrow> <mo>〈</mo> <mi>R</mi> <mi>S</mi> <mi>S</mi> <mi>I</mi> <mo>〉</mo> </mrow> <mrow> <mn>1</mn> <mi mathvariant="normal">m</mi> </mrow> </msub> <mo>≈</mo> <mo>−</mo> <mn>68</mn> </mrow> </semantics></math> is also shown, which corresponds to the average value at a distance of 1 m. (<b>b</b>) Similar to (<b>a</b>), but showing the mean values obtained from a moving average with one-minute windows.</p> Full article ">Figure 7
<p>Signal strength vs. distance for the measurements from sensors S1 and S2. We observe that the signal intensity decreases as the distance increases. The vertical bars represent the standard deviation over the set of measurements.</p> Full article ">Figure 8
<p>(<b>a</b>) Logarithmic-linear representation of the signal strength (RSSI) vs. distance for data obtained from sensors S1 and S2. This plot shows that the decay is logarithmic with an approximate linear behavior in this semi-log plane. (<b>b</b>) The calculations of the linear regression yield the values for the slopes: <math display="inline"><semantics> <mrow> <msub> <mi>a</mi> <mn>1</mn> </msub> <mo>=</mo> <mo>−</mo> <mn>1.16</mn> </mrow> </semantics></math> (<math display="inline"><semantics> <mrow> <msup> <mi>R</mi> <mn>2</mn> </msup> <mo>=</mo> <mn>0.879</mn> </mrow> </semantics></math>) for S1 and <math display="inline"><semantics> <mrow> <msub> <mi>a</mi> <mn>2</mn> </msub> <mo>=</mo> <mo>−</mo> <mn>1.10</mn> </mrow> </semantics></math> (<math display="inline"><semantics> <mrow> <msup> <mi>R</mi> <mn>2</mn> </msup> <mo>=</mo> <mn>0.851</mn> </mrow> </semantics></math>) for S2.</p> Full article ">Figure 9
<p>(<b>a</b>) Array of sensors in a pre-established star-like configuration. Initially, the distance between the nearest neighboring sensors is set at 0.2 m, then increased in 0.2 m intervals, until 1.2 m is reached. (<b>b</b>–<b>d</b>) Histograms of the number of measurements by S1 for three distance values. (<b>e</b>) Behavior of the average signal strength (RSSI) of all sensors versus distance in a semi-logarithmic plane. The solid line represents a linear regression that leads to the slope <math display="inline"><semantics> <mrow> <msub> <mi>a</mi> <mi>r</mi> </msub> <mo>=</mo> <mo>−</mo> <mn>1.3</mn> </mrow> </semantics></math> (<math display="inline"><semantics> <mrow> <msup> <mi>R</mi> <mn>2</mn> </msup> <mo>=</mo> <mn>0.90</mn> </mrow> </semantics></math>).</p> Full article ">
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 3378
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
(This article belongs to the Special Issue Sensors Network and Wearables for People Activities and Wellbeing Monitoring)
►▼ Show Figures

Figure 1

Figure 1
<p>Architecture diagram of the embedded system for direction detection.</p> Full article ">Figure 2
<p>The physical image of the TI TMS320C6713 DSK.</p> Full article ">Figure 3
<p>Architecture diagram of a hybrid audio source embedded system.</p> Full article ">Figure 4
<p>Flowchart of the hybrid audio source separation algorithm.</p> Full article ">Figure 5
<p>The physical image of the Raspberry Pi 2.</p> Full article ">Figure 6
<p>The physical image of the Cirrus Logic Audio Card.</p> Full article ">Figure 7
<p>Connection between Raspberry Pi 2 and Cirrus Logic Audio Card.</p> Full article ">Figure 8
<p>Setup for azimuth detection experiment.</p> Full article ">Figure 9
<p>Microphone CM503N.</p> Full article ">Figure 10
<p>Experimental scenario of direction detection.</p> Full article ">Figure 11
<p>Experimental Environment 1 for mixed sound source separation.</p> Full article ">Figure 12
<p>Experimental Environment 2 for mixed sound source separation.</p> Full article ">Figure 13
<p>Raspberry Pi 2, Cirrus Logic Audio Card, and 7-inch touch screen.</p> Full article ">Figure 14
<p>Mixed signal for left and right channels (1 m).</p> Full article ">Figure 15
<p>Separated signal after mixed sound source separation (1 m).</p> Full article ">Figure 16
<p>Mixed signal for left and right channels (2 m).</p> Full article ">Figure 17
<p>Separated signal after mixed sound source separation (2 m).</p> Full article ">Figure 18
<p>Speech recognition accuracy.</p> Full article ">
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 2 | Viewed by 2978
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
(This article belongs to the Special Issue Sensors Network and Wearables for People Activities and Wellbeing Monitoring)
►▼ Show Figures

Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>The measurement setup: data are acquired through a couple of IMU sensors, driven by a proprietary MOVELLA DOT App, which communicates with a PC where data processing is carried out in a MATLAB<sup>®</sup> environment R2023b.</p> Full article ">Figure 2
<p>Two different configurations: (<b>a</b>) IMUs are placed on the index and middle of the dominant hand; (<b>b</b>) IMUs are placed on the index fingers of the right and left hand.</p> Full article ">Figure 3
<p>Algorithm block diagram: description of the main steps for acquiring and selecting the most sensitive axis (INPUT DATA), processing (DATA PROCESSING) and analyzing (DATA ANALYSIS) IMU inertial data.</p> Full article ">Figure 4
<p>In configuration (<b>A</b>), the sensor is placed at a fixed distance from the metacarpal joint of 2 cm. In configuration (<b>B</b>), the sensor is placed on the distal phalanx with an unfixed distance.</p> Full article ">Figure 5
<p>The top figure shows the average number of taps obtained in the two different configurations. The second plot compares the coefficient of variation calculated under conditions A and B.</p> Full article ">Figure 6
<p>Example of linear inter-times fitting versus execution time—participant ID: 19; test: UniALT (index finger). The blue points are the raw intertime evaluation data, the red line is the linear fitting curve.</p> Full article ">Figure 7
<p>Mean excursion of tap movement acceleration calculated for each finger for the different case studies.</p> Full article ">Figure 8
<p>Bar chart of average trends of simultaneity times during UniSIM test. The horizontal dashed blue line is the experimental threshold for the simultaneity check.</p> Full article ">Figure 9
<p>Bar chart of average trends of alternation and simultaneity times during BimSIM test. The horizontal dashed blue line is the experimental threshold for the simultaneity check.</p> Full article ">Figure 10
<p>Comparison between the mean and standard deviation of UniSIM and BimSIM tests.</p> Full article ">Figure 11
<p>Bar chart of average trends of simultaneity times during UniALT test. The horizontal dashed blue line is the experimental threshold for the simultaneity check.</p> Full article ">Figure 12
<p>Bar chart of average trends of alternation and simultaneity times during BimALT test. The horizontal dashed blue line is the experimental threshold for the simultaneity check.</p> Full article ">Figure 13
<p>Comparison between the means and standard deviations of UniALT and BimALT tests.</p> Full article ">
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 1 | Viewed by 1213
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
(This article belongs to the Special Issue Sensors Network and Wearables for People Activities and Wellbeing Monitoring)
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Figure 1

Figure 1
<p>BTS Gait Lab: (<b>a</b>) Movement analysis laboratory at the University Hospital of Salerno; (<b>b</b>) Dynamometric platform; (<b>c</b>) Retro-reflective passive markers; (<b>d</b>) Infrared-camera.</p> Full article ">Figure 2
<p>Opal System: (<b>a</b>) Access Point; (<b>b</b>) Docking Station; (<b>c</b>) Mobility Lab software; (<b>d</b>) Opal sensor.</p> Full article ">Figure 3
<p>Cadence. On the left, scatter plot with PB regression line and identity line (BTS = OPAL). On the right, BA plot (average vs. difference).</p> Full article ">Figure 4
<p>Cycle duration. On the left, scatter plot with PB regression line and identity line (BTS = OPAL). On the right, BA plot (average vs. difference).</p> Full article ">Figure 5
<p>Speed. On the left, scatter plot with PB regression line and identity line (BTS = OPAL). On the right, BA plot (average vs. difference).</p> Full article ">Figure 6
<p>Stance phase. On the left, scatter plot with PB regression line and identity line (BTS = OPAL). On the right, BA plot (average vs. difference).</p> Full article ">Figure 7
<p>Swing phase. On the left, scatter plot with PB regression line and identity line (BTS = OPAL). On the right, BA plot (average vs. difference).</p> Full article ">Figure 8
<p>Stride length. On the left, scatter plot with PB regression line and identity line (BTS = OPAL). On the right, BA plot (average vs. difference).</p> Full article ">
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