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Well-Being, Comfort and Health Monitoring through Wearable Sensors

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

Deadline for manuscript submissions: closed (5 December 2022) | Viewed by 17247

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Special Issue Editors

Dr. Gian Marco Revel

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Guest Editor

Department of Industrial Engineering and Mathematical Sciences, Università Politecnica delle Marche, 60131 Ancona, Italy
Interests: pir sensor; home measurements; sleep-related parameters; sleep latency; sleep interruptions; time to wake; sleep efficiency
Special Issues, Collections and Topics in MDPI journals

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

Special Issue Information

The development of wearable technologies over the past years has opened up the possibility of extracting physiological parameters just through using low cost and non-invasive systems. Continuous monitoring of physiological and personal parameters, e.g., electrocardiograms, electrodermal activity, electroencephalograms, skin temperature, activity level, etc., through wearable sensors has been demonstrated to define the user's well-being, comfort, and health status in the life environments, both indoor and outdoor.

In this Special Issue, we call for papers presenting innovative solutions and signal processing techniques to measure the well-being, comfort, and health status of the user in the life environments, i.e., indoor and outdoor, through wearable sensors eventually integrated in sensor networks. The papers have to consider the accuracy in the measurement of such quantities.

Dr. Gian Marco Revel
Dr. Sara Casaccia
Guest Editors

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Keywords

wearable sensors
well-being, health
comfort
measurements
accuracy
life environment

Published Papers (6 papers)

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Research

12 pages, 990 KiB

Open AccessCommunication

Instrumental and Non-Instrumental Measurements in Patients with Peripheral Vestibular Dysfunctions

by Anna Gawronska, Oskar Rosiak, Anna Pajor, Magdalena Janc, Rafal Kotas, Marek Kaminski, Ewa Zamyslowska-Szmytke and Magdalena Jozefowicz-Korczynska

Sensors 2023, 23(4), 1994; https://doi.org/10.3390/s23041994 - 10 Feb 2023

Viewed by 1528

Abstract

Vestibular dysfunction is a disturbance of the body’s balance system. The control of balance and gait has a particular influence on the quality of life. Currently, assessing patients with these problems is mainly subjective. New assessment options using wearables may provide complementary and more objective information. Posturography makes it possible to determine the extent and type of posture dysfunction, which makes it possible to plan and monitor the effectiveness of physical rehabilitation therapy. This study evaluates the effectiveness of non-instrumental clinical tests and the instrumental mobile posturography MediPost device for patients with unilateral vestibular disorders. The study group included 40 patients. A subjective description of the symptoms was evaluated using a questionnaire about the intensity of dizziness using the Dizziness Handicap Inventory (DHI) and Vertigo Syndrome Scale—short form (VSS-sf). The clinical protocol contained clinical tests and MediPost measurements using a Modified Clinical Test of Sensory Interaction on Balance. All patients underwent vestibular rehabilitation therapy (VRT) for four weeks. The non-instrumental measurement results were statistically significant, and the best was in the Timed Up and Go test (TUG). In MediPost, condition 4 was the most valuable. This research demonstrated the possibilities of using an instrumental test (MediPost) as an alternative method to assess balance. Full article

(This article belongs to the Special Issue Well-Being, Comfort and Health Monitoring through Wearable Sensors)

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11 pages, 1320 KiB

Open AccessArticle

Constant Load Pedaling Exercise Combined with Electrical Muscle Stimulation Leads to an Early Increase in Sweat Lactate Levels

by Tomonori Sawada, Hiroki Okawara, Daisuke Nakashima, Kaito Ikeda, Joji Nagahara, Haruki Fujitsuka, Sosuke Hoshino, Yuta Maeda, Yoshinori Katsumata, Masaya Nakamura and Takeo Nagura

Sensors 2022, 22(24), 9585; https://doi.org/10.3390/s22249585 - 7 Dec 2022

Cited by 3 | Viewed by 2169

Abstract

A novel exercise modality combined with electrical muscle stimulation (EMS) has been reported to increase cardiovascular and metabolic responses, such as blood lactate concentration. We aimed to examine the effect of constant load pedaling exercise, combined with EMS, by non-invasively and continuously measuring sweat lactate levels. A total of 22 healthy young men (20.7 ± 0.8 years) performed a constant load pedaling exercise for 20 min at 125% of the pre-measured ventilatory work threshold with (EMS condition) and without (control condition) EMS stimulation. Blood lactate concentration was measured by blood samples obtained from the earlobe every minute. Sweat lactate was monitored in real time using a sensor placed on the forearm. The sweat lactate threshold (sLT) was defined as the point of increase in sweat lactate. sLT occurred significantly earlier in the EMS condition than in the control condition. In the single regression analysis, the difference in sLT between the two conditions, as the independent variable, was a significant predictor of the difference in blood lactate concentrations at the end of the exercise (p < 0.05, r = −0.52). Sweat lactate measurement may be a noninvasive and simple alternative to blood lactate measurement to determine the effectiveness of exercise combined with EMS. Full article

(This article belongs to the Special Issue Well-Being, Comfort and Health Monitoring through Wearable Sensors)

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11 pages, 3089 KiB

Open AccessArticle

Validation of Wearable Device Consisting of a Smart Shirt with Built-In Bioelectrodes and a Wireless Transmitter for Heart Rate Monitoring in Light to Moderate Physical Work

by Yuki Hashimoto, Rieko Sato, Kazuhiko Takagahara, Takako Ishihara, Kento Watanabe and Hiroyoshi Togo

Sensors 2022, 22(23), 9241; https://doi.org/10.3390/s22239241 - 28 Nov 2022

Cited by 4 | Viewed by 2154

Abstract

Real-time monitoring of heart rate is useful for monitoring workers. Wearable heart rate monitors worn on the upper body are less susceptible to artefacts caused by arm and wrist movements than popular wristband-type sensors using the photoplethysmography method. Therefore, they are considered suitable for stable and accurate measurement for various movements. In this study, we conducted an experiment to verify the accuracy of our developed and commercially available wearable heart rate monitor consisting of a smart shirt with bioelectrodes and a transmitter, assuming a real-world work environment with physical loads. An exercise protocol was designed to light to moderate intensity according to international standards because no standard exercise protocol for the validation simulating these works has been reported. This protocol includes worker-specific movements such as applying external vibration and lifting and lowering loads. In the experiment, we simultaneously measured the instantaneous heart rate with the above wearable device and a Holter monitor as a reference to evaluate mean absolute percentage error (MAPE). The MAPE was 0.92% or less for all exercise protocols conducted. This value indicates that the accuracy of the wearable device is high enough for use in real-world cases of physical load in light to moderate intensity tasks such as those in our experimental protocol. In addition, the experimental protocol and measurement data devised in this study can be used as a benchmark for other wearable heart rate monitors for use for similar purposes. Full article

(This article belongs to the Special Issue Well-Being, Comfort and Health Monitoring through Wearable Sensors)

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14 pages, 5038 KiB

Open AccessCommunication

Development of Smartphone Application for Markerless Three-Dimensional Motion Capture Based on Deep Learning Model

by Yukihiko Aoyagi, Shigeki Yamada, Shigeo Ueda, Chifumi Iseki, Toshiyuki Kondo, Keisuke Mori, Yoshiyuki Kobayashi, Tadanori Fukami, Minoru Hoshimaru, Masatsune Ishikawa and Yasuyuki Ohta

Sensors 2022, 22(14), 5282; https://doi.org/10.3390/s22145282 - 14 Jul 2022

Cited by 6 | Viewed by 3052

Abstract

To quantitatively assess pathological gait, we developed a novel smartphone application for full-body human motion tracking in real time from markerless video-based images using a smartphone monocular camera and deep learning. As training data for deep learning, the original three-dimensional (3D) dataset comprising more than 1 million captured images from the 3D motion of 90 humanoid characters and the two-dimensional dataset of COCO 2017 were prepared. The 3D heatmap offset data consisting of 28 × 28 × 28 blocks with three red–green–blue colors at the 24 key points of the entire body motion were learned using the convolutional neural network, modified ResNet34. At each key point, the hottest spot deviating from the center of the cell was learned using the tanh function. Our new iOS application could detect the relative tri-axial coordinates of the 24 whole-body key points centered on the navel in real time without any markers for motion capture. By using the relative coordinates, the 3D angles of the neck, lumbar, bilateral hip, knee, and ankle joints were estimated. Any human motion could be quantitatively and easily assessed using a new smartphone application named Three-Dimensional Pose Tracker for Gait Test (TDPT-GT) without any body markers or multipoint cameras. Full article

(This article belongs to the Special Issue Well-Being, Comfort and Health Monitoring through Wearable Sensors)

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23 pages, 4141 KiB

Open AccessArticle

The Problem of Monitoring Activities of Older People in Multi-Resident Scenarios: An Innovative and Non-Invasive Measurement System Based on Wearables and PIR Sensors

by Riccardo Naccarelli, Sara Casaccia and Gian Marco Revel

Sensors 2022, 22(9), 3472; https://doi.org/10.3390/s22093472 - 3 May 2022

Cited by 11 | Viewed by 2667

Abstract

This paper presents an innovative multi-resident activity detection sensor network that uses the Bluetooth Low Energy (BLE) signal emitted by tags worn by residents and passive infrared (PIR) motion sensors deployed in the house to locate residents and monitor their activities. This measurement system solves the problem of monitoring older people and measuring their activities in multi-resident scenarios. Metrics are defined to analyze and interpret the collected data to understand daily habits and measure the activity level (AL) of older people. The accuracy of the system in detecting movements and discriminating residents is measured. As the sensor-to-person distance increases, the system decreases its ability to detect small movements, while still being able to detect large ones. The accuracy in discriminating the identity of residents can be improved by up to 96% using the Decision Tree (DT) classifier. The effectiveness of the measurement system is demonstrated in a real multi-resident scenario where two older people are monitored during their daily life. The collected data are processed, obtaining the AL and habits of the older people to assess their behavior. Full article

(This article belongs to the Special Issue Well-Being, Comfort and Health Monitoring through Wearable Sensors)

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19 pages, 5415 KiB

Open AccessArticle

Estimating Sleep Stages Using a Head Acceleration Sensor

by Motoki Yoshihi, Shima Okada, Tianyi Wang, Toshihiro Kitajima and Masaaki Makikawa

Sensors 2021, 21(3), 952; https://doi.org/10.3390/s21030952 - 1 Feb 2021

Cited by 6 | Viewed by 4228

Abstract

Sleep disruption from causes, such as changes in lifestyle, stress from aging, family issues, or life pressures are a growing phenomenon that can lead to serious health problems. As such, sleep disorders need to be identified and addressed early on. In recent years, studies have investigated sleep patterns through body movement information collected by wristwatch-type devices or cameras. However, these methods capture only the individual’s awake and sleep states and lack sufficient information to identify specific sleep stages. The aim of this study was to use a 3-axis accelerometer attached to an individual’s head to capture information that can identify three specific sleep stages: rapid eye movement (REM) sleep, light sleep, and deep sleep. These stages are measured by heart rate features captured by a ballistocardiogram and body movement. The sleep experiment was conducted for two nights among eight healthy adult men. According to the leave-one-out cross-validation results, the F-scores were: awake 76.6%, REM sleep 52.7%, light sleep 78.2%, and deep sleep 67.8%. The accuracy was 74.6% for the four estimates. This proposed measurement system was able to estimate the sleep stages with high accuracy simply by using the acceleration in the individual’s head. Full article

(This article belongs to the Special Issue Well-Being, Comfort and Health Monitoring through Wearable Sensors)

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