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Wearable Sensors and IoT-Oriented Systems for Life Quality Improvement

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

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 14862

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Special Issue Editor

Dr. Paolo Visconti

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Guest Editor

Departament of Innovation Engineering, University of Salento, 73100 Lecce, Italy
Interests: design and testing of IoT-based electronic systems; smart remote control of facilities; electronic systems for automation and automotive; energy harvesting systems for sensors nodes; wearable devices for health monitoring; new materials and advanced sensors
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Special Issue Information

Dear Colleagues,

Advances in digital technologies are allowing people and things to constantly stay connected and synchronized with the Internet, introducing us to a world always connected and full of possibilities, thanks to the detection, processing, and sharing of the data collected by the devices. Particularly, the availability of microcontroller-based data-processing electronic boards, smart sensors, and wireless communication modules, all featured by ultra-low power consumption and high performances, offers new application solutions in the wearable devices field, which enable real-time and punctual monitoring of user's biophysical parameters. In this context, the IoMT (Internet of medical things) and IoHT (Internet of healthcare things) paradigms, in the near future, will represent the evolution of healthcare, by equipping the patient with smart devices, with little or no invasiveness, in order to constantly monitor their health conditions. These devices open new possibilities for PoC (point of care) monitoring systems, which involve considerable hardware and software potential, through multi-platform applications, distributed high-speed network connectivity, and the use of big data. In fact, a suitable hardware and software architecture will be needed to store and elaborate the data gathered by the different wearable devices in order to make them available to medical staff, for providing feedback to the patient, or alerting the rescue teams in case of sickness or dangerous situations for workers.

Summing up, this Special Issue “Wearable Sensors and IoT-Oriented Systems for Life Quality Improvement” brings together innovative developments and synergies related, but not limited, to following topics:

Wearable devices for health monitoring;
Data processing of wearable sensor systems;
Flexible electronic wearable devices;
Predictive algorithms for body movements;
Monitoring of elderly and disabled people;
PoC (point of care) sensing devices and the integration of medical platform;
Body sensors networks;
Sensors for fitness and wellbeing;
Non-conventional patient monitoring;
Wearable devices for environmental monitoring;
Accident preventing system in the workplace;
Design and testing of IoMT solutions ;
Design and testing of IoHT solutions;
IoT solutions for remote control of workers in hostile environments.

Prof. Dr. Paolo Visconti
Guest Editor

Manuscript Submission Information

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Keywords

Wearable sensors and systems
Body sensors networks
Wearable devices
Internet of medical things
Internet of healthcare things
Remote control systems
Software and hardware development
Life quality improvement
Data processing
Monitoring health platforms

Published Papers (4 papers)

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Research

22 pages, 14561 KiB

Open AccessArticle

Motion Sensor-Based Detection of Outlier Days Supporting Continuous Health Assessment for Single Older Adults

by Marc Mertens, Glen Debard, Jesse Davis, Els Devriendt, Koen Milisen, Jos Tournoy, Tom Croonenborghs and Bart Vanrumste

Sensors 2021, 21(18), 6080; https://doi.org/10.3390/s21186080 - 10 Sep 2021

Cited by 3 | Viewed by 1950

Abstract

The aging population has resulted in interest in remote monitoring of elderly individuals’ health and well being. This paper describes a simple unsupervised monitoring system that can automatically detect if an elderly individual’s pattern of presence deviates substantially from the recent past. The proposed system uses a small set of low-cost motion sensors and analyzes the produced data to establish an individual’s typical presence pattern. Then, the algorithm uses a distance function to determine whether the individual’s observed presence for each day significantly deviates from their typical pattern. Empirically, the algorithm is validated on both synthetic data and data collected by installing our system in the residences of three older individuals. In the real-world setting, the system detected, respectively, five, four, and one deviating days in the three locations. The deviating days detected by the system could result from a health issue that requires attention. The information from the system can aid caregivers in assessing the subject’s health status and allows for a targeted intervention. Although the system can be refined, we show that otherwise hidden but relevant events (e.g., fall incident and irregular sleep patterns) are detected and reported to the caregiver. Full article

(This article belongs to the Special Issue Wearable Sensors and IoT-Oriented Systems for Life Quality Improvement)

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31 pages, 12333 KiB

Open AccessArticle

Development of a Self-Powered Piezo-Resistive Smart Insole Equipped with Low-Power BLE Connectivity for Remote Gait Monitoring

by Roberto de Fazio, Elisa Perrone, Ramiro Velázquez, Massimo De Vittorio and Paolo Visconti

Sensors 2021, 21(13), 4539; https://doi.org/10.3390/s21134539 - 01 Jul 2021

Cited by 28 | Viewed by 6062

Abstract

The evolution of low power electronics and the availability of new smart materials are opening new frontiers to develop wearable systems for medical applications, lifestyle monitoring, and performance detection. This paper presents the development and realization of a novel smart insole for monitoring the plantar pressure distribution and gait parameters; indeed, it includes a piezoresistive sensing matrix based on a Velostat layer for transducing applied pressure into an electric signal. At first, an accurate and complete characterization of Velostat-based pressure sensors is reported as a function of sizes, support material, and pressure trend. The realization and testing of a low-cost and reliable piezoresistive sensing matrix based on a sandwich structure are discussed. This last is interfaced with a low power conditioning and processing section based on an Arduino Lilypad board and an analog multiplexer for acquiring the pressure data. The insole includes a 3-axis capacitive accelerometer for detecting the gait parameters (swing time and stance phase time) featuring the walking. A Bluetooth Low Energy (BLE) 5.0 module is included for transmitting in real-time the acquired data toward a PC, tablet or smartphone, for displaying and processing them using a custom Processing^® application. Moreover, the smart insole is equipped with a piezoelectric harvesting section for scavenging energy from walking. The onfield tests indicate that for a walking speed higher than 1 ms⁻¹, the device’s power requirements (i.e.,

\bar{P} = 5.84 mW

) was fulfilled. However, more than 9 days of autonomy are guaranteed by the integrated 380-mAh Lipo battery in the total absence of energy contributions from the harvesting section. Full article

(This article belongs to the Special Issue Wearable Sensors and IoT-Oriented Systems for Life Quality Improvement)

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13 pages, 389 KiB

Open AccessCommunication

Error Estimation of Ultra-Short Heart Rate Variability Parameters: Effect of Missing Data Caused by Motion Artifacts

by Alessio Rossi, Dino Pedreschi, David A. Clifton and Davide Morelli

Sensors 2020, 20(24), 7122; https://doi.org/10.3390/s20247122 - 11 Dec 2020

Cited by 9 | Viewed by 2964

Abstract

Application of ultra–short Heart Rate Variability (HRV) is desirable in order to increase the applicability of HRV features to wrist-worn wearable devices equipped with heart rate sensors that are nowadays becoming more and more popular in people’s daily life. This study is focused in particular on the the two most used HRV parameters, i.e., the standard deviation of inter-beat intervals (SDNN) and the root Mean Squared error of successive inter-beat intervals differences (rMSSD). The huge problem of extracting these HRV parameters from wrist-worn devices is that their data are affected by the motion artifacts. For this reason, estimating the error caused by this huge quantity of missing values is fundamental to obtain reliable HRV parameters from these devices. To this aim, we simulate missing values induced by motion artifacts (from 0 to 70%) in an ultra-short time window (i.e., from 4 min to 30 s) by the random walk Gilbert burst model in 22 young healthy subjects. In addition, 30 s and 2 min ultra-short time windows are required to estimate rMSSD and SDNN, respectively. Moreover, due to the fact that ultra-short time window does not permit assessing very low frequencies, and the SDNN is highly affected by these frequencies, the bias for estimating SDNN continues to increase as the time window length decreases. On the contrary, a small error is detected in rMSSD up to 30 s due to the fact that it is highly affected by high frequencies which are possible to be evaluated even if the time window length decreases. Finally, the missing values have a small effect on rMSSD and SDNN estimation. As a matter of fact, the HRV parameter errors increase slightly as the percentage of missing values increase. Full article

(This article belongs to the Special Issue Wearable Sensors and IoT-Oriented Systems for Life Quality Improvement)

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22 pages, 1503 KiB

Open AccessArticle

Leveraging Wearable Sensors for Human Daily Activity Recognition with Stacked Denoising Autoencoders

by Qin Ni, Zhuo Fan, Lei Zhang, Chris D. Nugent, Ian Cleland, Yuping Zhang and Nan Zhou

Sensors 2020, 20(18), 5114; https://doi.org/10.3390/s20185114 - 08 Sep 2020

Cited by 21 | Viewed by 2938

Abstract

Activity recognition has received considerable attention in many research fields, such as industrial and healthcare fields. However, many researches about activity recognition have focused on static activities and dynamic activities in current literature, while, the transitional activities, such as stand-to-sit and sit-to-stand, are more difficult to recognize than both of them. Consider that it may be important in real applications. Thus, a novel framework is proposed in this paper to recognize static activities, dynamic activities, and transitional activities by utilizing stacked denoising autoencoders (SDAE), which is able to extract features automatically as a deep learning model rather than utilize manual features extracted by conventional machine learning methods. Moreover, the resampling technique (random oversampling) is used to improve problem of unbalanced samples due to relatively short duration characteristic of transitional activity. The experiment protocol is designed to collect twelve daily activities (three types) by using wearable sensors from 10 adults in smart lab of Ulster University, the experiment results show the significant performance on transitional activity recognition and achieve the overall accuracy of 94.88% on three types of activities. The results obtained by comparing with other methods and performances on other three public datasets verify the feasibility and priority of our framework. This paper also explores the effect of multiple sensors (accelerometer and gyroscope) to determine the optimal combination for activity recognition. Full article

(This article belongs to the Special Issue Wearable Sensors and IoT-Oriented Systems for Life Quality Improvement)

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