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Wearable System-Based Sensors for Ambient Assisted Living
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Wearable System-Based Sensors for Ambient Assisted Living

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

Deadline for manuscript submissions: closed (15 August 2019) | Viewed by 13463

Special Issue Editor

Prof. Dr. Eng Hock Francis Tay

E-Mail Website
Guest Editor
Mechanical Engineering, National University of Singapore, Singapore 117575, Singapore
Interests: MEMS; medicine; healthcare; rehabilitation; wearable sensors

Special Issue Information

Dear Colleagues,

Wearable sensors have come a long way in providing care for the wearer. This is a rapidly evolving field due to technological breakthroughs. There has been much development in terms of size, weight, reliability and applications, among others. Wearable systems have become more ubiquitous and this Special Issue is dedicated to ambient assisted living, which includes people with or without medical conditions, the young and the elderly.

This Special Issue will integrate these research findings and developments in wearable systems, especially as applied to ambient living conditions.

Technologies of interest include but are not limited to IOT, MEMS, flexible electronics, rapid manufacturing, 4G communications and energy sources.

Prof. Dr. Eng Hock Francis Tay
Guest Editor

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

  • Wearable Systems
  • Ambient Assisted Living
  • MEMS
  • Rehabilitation
  • Engineering in Medicine
  • Wearable Healthcare
  • Wearable Lifestyle
  • Extended Wear
  • Energy Harvesting
  • Low Power Communications
  • Flexible Electronics
  • IOT
  • AI in Medical Systems

Published Papers (4 papers)

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Research

16 pages, 1237 KiB  
Article
A Novel Sensor Data Pre-Processing Methodology for the Internet of Things Using Anomaly Detection and Transfer-By-Subspace-Similarity Transformation
by Yan Zhong, Simon Fong, Shimin Hu, Raymond Wong and Weiwei Lin
Sensors 2019, 19(20), 4536; https://doi.org/10.3390/s19204536 - 18 Oct 2019
Cited by 11 | Viewed by 3486
Abstract
The Internet of Things (IoT) and sensors are becoming increasingly popular, especially in monitoring large and ambient environments. Applications that embrace IoT and sensors often require mining the data feeds that are collected at frequent intervals for intelligence. Despite the fact that such [...] Read more.
The Internet of Things (IoT) and sensors are becoming increasingly popular, especially in monitoring large and ambient environments. Applications that embrace IoT and sensors often require mining the data feeds that are collected at frequent intervals for intelligence. Despite the fact that such sensor data are massive, most of the data contents are identical and repetitive; for example, human traffic in a park at night. Most of the traditional classification algorithms were originally formulated decades ago, and they were not designed to handle such sensor data effectively. Hence, the performance of the learned model is often poor because of the small granularity in classification and the sporadic patterns in the data. To improve the quality of data mining from the IoT data, a new pre-processing methodology based on subspace similarity detection is proposed. Our method can be well integrated with traditional data mining algorithms and anomaly detection methods. The pre-processing method is flexible for handling similar kinds of sensor data that are sporadic in nature that exist in many ambient sensing applications. The proposed methodology is evaluated by extensive experiment with a collection of classical data mining models. An improvement over the precision rate is shown by using the proposed method. Full article
(This article belongs to the Special Issue Wearable System-Based Sensors for Ambient Assisted Living)
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16 pages, 2448 KiB  
Article
Quantitative Assessment of Head Tremor in Patients with Essential Tremor and Cervical Dystonia by Using Inertial Sensors
by Lazar Berbakov, Čarna Jovanović, Marina Svetel, Jelena Vasiljević, Goran Dimić and Nenad Radulović
Sensors 2019, 19(19), 4246; https://doi.org/10.3390/s19194246 - 30 Sep 2019
Cited by 5 | Viewed by 2817
Abstract
Tremor is most common among the movement disabilities that affect older people, having a prevalence rate of 4.6% in the population older than 65 years. Despite this, distinguishing different types of tremors is clinically challenging, often leading to misdiagnosis. However, due to advances [...] Read more.
Tremor is most common among the movement disabilities that affect older people, having a prevalence rate of 4.6% in the population older than 65 years. Despite this, distinguishing different types of tremors is clinically challenging, often leading to misdiagnosis. However, due to advances in microelectronics and wireless communication, it is now possible to easily monitor tremor in hospitals and even in home environments. In this paper, we propose an architecture of a system for remote health-care and one possible implementation of such system focused on head tremor monitoring. In particular, the aim of the study presented here was to test new tools for differentiating essential tremor from dystonic tremor. To that aim, we propose a number of temporal and spectral features that are calculated from measured gyroscope signals, and identify those that provide optimal differentiation between two groups. The mean signal amplitude feature results in sensitivity = 0.8537 and specificity = 0.8039 in distinguishing patients having cervical dystonia with or without tremor. In addition, mean signal amplitude was shown to be significantly higher in patients with essential tremor than in patients with cervical dystonia, whereas the mean peak frequency is not different between two groups. Full article
(This article belongs to the Special Issue Wearable System-Based Sensors for Ambient Assisted Living)
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17 pages, 620 KiB  
Article
Towards Real-Time Prediction of Freezing of Gait in Patients With Parkinson’s Disease: Addressing the Class Imbalance Problem
by Nader Naghavi, Aaron Miller and Eric Wade
Sensors 2019, 19(18), 3898; https://doi.org/10.3390/s19183898 - 10 Sep 2019
Cited by 32 | Viewed by 3453
Abstract
Freezing of gait (FoG) is a common motor symptom in patients with Parkinson’s disease (PD). FoG impairs gait initiation and walking and increases fall risk. Intelligent external cueing systems implementing FoG detection algorithms have been developed to help patients recover gait after freezing. [...] Read more.
Freezing of gait (FoG) is a common motor symptom in patients with Parkinson’s disease (PD). FoG impairs gait initiation and walking and increases fall risk. Intelligent external cueing systems implementing FoG detection algorithms have been developed to help patients recover gait after freezing. However, predicting FoG before its occurrence enables preemptive cueing and may prevent FoG. Such prediction remains challenging given the relative infrequency of freezing compared to non-freezing events. In this study, we investigated the ability of individual and ensemble classifiers to predict FoG. We also studied the effect of the ADAptive SYNthetic (ADASYN) sampling algorithm and classification cost on classifier performance. Eighteen PD patients performed a series of daily walking tasks wearing accelerometers on their ankles, with nine experiencing FoG. The ensemble classifier formed by Support Vector Machines, K-Nearest Neighbors, and Multi-Layer Perceptron using bagging techniques demonstrated highest performance (F1 = 90.7) when synthetic FoG samples were added to the training set and class cost was set as twice that of normal gait. The model identified 97.4% of the events, with 66.7% being predicted. This study demonstrates our algorithm’s potential for accurate prediction of gait events and the provision of preventive cueing in spite of limited event frequency. Full article
(This article belongs to the Special Issue Wearable System-Based Sensors for Ambient Assisted Living)
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17 pages, 853 KiB  
Article
Detection of Sleep Biosignals Using an Intelligent Mattress Based on Piezoelectric Ceramic Sensors
by Min Peng, Zhizhong Ding, Lusheng Wang and Xusheng Cheng
Sensors 2019, 19(18), 3843; https://doi.org/10.3390/s19183843 - 5 Sep 2019
Cited by 19 | Viewed by 3295
Abstract
Physiological information such as respiratory rate and heart rate in the sleep state can be used to evaluate the health condition of the sleeper. Traditional sleep monitoring systems need body contact and are intrusive, which limits their applicability. Thus, a comfortable sleep biosignals [...] Read more.
Physiological information such as respiratory rate and heart rate in the sleep state can be used to evaluate the health condition of the sleeper. Traditional sleep monitoring systems need body contact and are intrusive, which limits their applicability. Thus, a comfortable sleep biosignals detection system with both high accuracy and low cost is important for health care. In this paper, we design a sleep biosignals detection system based on low-cost piezoelectric ceramic sensors. 18 piezoelectric ceramic sensors are deployed under the mattress to capture the pressure data. The appropriate sensor that captures respiration and heartbeat sensitively is selected by the proposed channel-selection algorithm. Then, we propose a dynamic smoothing algorithm to extract respiratory rate and heart rate using the selected data. The dynamic smoothing can separate heartbeat signals from respiratory signals with low complexity by dynamically choosing the smooth window, and it is suitable for real-time implementation in low-cost embedded systems. For comparison, wavelet analysis and ensemble empirical mode decomposition (EEMD) are performed in a personal computer (PC). Experimental results show that data collected by piezoelectric ceramic sensors can be used for respiratory-rate and heart-rate detection with high accuracy. In addition, the dynamic smoothing can achieve high accuracy close to wavelet analysis and EEMD, while it has much lower complexity. Full article
(This article belongs to the Special Issue Wearable System-Based Sensors for Ambient Assisted Living)
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