Research

24 pages, 8143 KiB

Open AccessArticle

PIFall: A Pressure Insole-Based Fall Detection System for the Elderly Using ResNet3D

by Wei Guo, Xiaoyang Liu, Chenghong Lu and Lei Jing

Electronics 2024, 13(6), 1066; https://doi.org/10.3390/electronics13061066 - 13 Mar 2024

Viewed by 484

Falls among the elderly are a significant public health issue, resulting in about 684,000 deaths annually. Such incidents often lead to severe consequences including fractures, contusions, and cranial injuries, immensely affecting the quality of life and independence of the elderly. Existing fall detection [...] Read more.

Falls among the elderly are a significant public health issue, resulting in about 684,000 deaths annually. Such incidents often lead to severe consequences including fractures, contusions, and cranial injuries, immensely affecting the quality of life and independence of the elderly. Existing fall detection methods using cameras and wearable sensors face challenges such as privacy concerns, blind spots in vision and being troublesome to wear. In this paper, we propose PIFall, a Pressure Insole-Based Fall Detection System for the Elderly, utilizing the ResNet3D algorithm. Initially, we design and fabricate a pair of insoles equipped with low-cost resistive films to measure plantar pressure, arranging

5 \times 9

pressure sensors on each insole. Furthermore, we present a fall detection method that combines ResNet(2+1)D with an insole-based sensor matrix, utilizing time-series ‘stress videos’ derived from pressure map data as input. Lastly, we collect data on 12 different actions from five subjects, including fall risk activities specifically designed to be easily confused with actual falls. The system achieves an overall accuracy of 91% in detecting falls and 94% in identifying specific fall actions. Additionally, feedback is gathered from eight elderly individuals using a structured questionnaire to assess user experience and satisfaction with the pressure insoles. Full article

(This article belongs to the Special Issue Wearable Sensing Devices and Technology)

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42 pages, 12018 KiB

Open AccessArticle

Towards Smarter Positioning through Analyzing Raw GNSS and Multi-Sensor Data from Android Devices: A Dataset and an Open-Source Application

by Antoine Grenier, Elena Simona Lohan, Aleksandr Ometov and Jari Nurmi

Electronics 2023, 12(23), 4781; https://doi.org/10.3390/electronics12234781 - 25 Nov 2023

Viewed by 1297

Abstract

The state-of-the-art Android environment, available on a major market share of smartphones, provides an open playground for sensor data gathering. Moreover, the rise in new types of devices (e.g., wearables/smartwatches) is further extending the market opportunities with a variety of new sensor types. [...] Read more.

The state-of-the-art Android environment, available on a major market share of smartphones, provides an open playground for sensor data gathering. Moreover, the rise in new types of devices (e.g., wearables/smartwatches) is further extending the market opportunities with a variety of new sensor types. The existing implementations of biometric/medical sensors can allow the general public to directly access their health measurements, such as Electrocardiogram (ECG) or Oxygen Saturation (SpO2). This access greatly increases the possible applications of these devices with the combination of all the onboard sensors that are broadly in use nowadays. In this study, we look beyond the current state of the art into the positioning capacities of Android smart devices and wearables, with a focus on raw Global Navigation Satellite Systems (GNSS) measurements that are still mostly lacking in the research world. We develop a novel open-source Android application working in both smartphone and smartwatch environments for multi-sensor measurement data logging that also includes GNSS, an Inertial Navigation System (INS) magnetometer, and a barometer. Four smartphones and one smartwatch are used to perform surveys in different scenarios. The extraction of GNSS raw data from a wearable device has not been reported yet in the literature and no open-source app has existed so far for extracting GNSS data from wearables. Not only the developed app but also the results of these measurement surveys are provided as an open-access dataset. We start by defining our methodology and the acquisition protocol, and we dive into the structure of the dataset files. We also propose a first analysis of the data logged and evaluate the data according to several performance metrics. A discussion reviewing the capacities of smart devices for advanced positioning is proposed, as well as the current open challenges. Full article

(This article belongs to the Special Issue Wearable Sensing Devices and Technology)

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23 pages, 5567 KiB

Open AccessArticle

VR Drumming Pedagogy: Action Observation, Virtual Co-Embodiment, and Development of Drumming “Halvatar”

by James Pinkl and Michael Cohen

Electronics 2023, 12(17), 3708; https://doi.org/10.3390/electronics12173708 - 01 Sep 2023

Viewed by 935

Abstract

Virtual Co-embodiment (vc) is a relatively new field of VR, enabling a user to share control of an avatar with other users or entities. According to a recent study, vc was shown to have the highest motor skill learning efficiency out [...] Read more.

Virtual Co-embodiment (vc) is a relatively new field of VR, enabling a user to share control of an avatar with other users or entities. According to a recent study, vc was shown to have the highest motor skill learning efficiency out of three VR-based methods. This contribution expands on these findings, as well as previous work relating to Action Observation (ao) and drumming, to realize a new concept to teach drumming. Users “duet” with an exemplar half in a virtual scene with concurrent feedback to learn rudiments and polyrhythms. We call this puppet avatar controlled by both a user and separate processes a “halvatar”. The development is based on body-part-segmented vc techniques and uses programmed animation, electromechanical drum strike detection, and optical bimanual hand-tracking informed by head-tracking. A pilot study was conducted with primarily non-musicians showing the potential effectiveness of this tool and approach. Full article

(This article belongs to the Special Issue Wearable Sensing Devices and Technology)

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18 pages, 11526 KiB

Open AccessArticle

Enhanced Wearable Force-Feedback Mechanism for Free-Range Haptic Experience Extended by Pass-Through Mixed Reality

by Peter Kudry and Michael Cohen

Electronics 2023, 12(17), 3659; https://doi.org/10.3390/electronics12173659 - 30 Aug 2023

Viewed by 1689

Abstract

We present an extended prototype of a wearable force-feedback mechanism coupled with a Meta Quest 2 head-mounted display to enhance immersion in virtual environments. Our study focuses on the development of devices and virtual experiences that place significant emphasis on personal sensing capabilities, [...] Read more.

We present an extended prototype of a wearable force-feedback mechanism coupled with a Meta Quest 2 head-mounted display to enhance immersion in virtual environments. Our study focuses on the development of devices and virtual experiences that place significant emphasis on personal sensing capabilities, such as precise inside-out optical hand, head, and controller tracking, as well as lifelike haptic feedback utilizing servos and vibration rumble motors, among others. The new prototype addresses various limitations and deficiencies identified in previous stages of development, resulting in significant user performance improvements. Key enhancements include weight reduction, wireless connectivity, optimized power delivery, refined haptic feedback intensity, improved stylus alignment, and smooth transitions between stylus use and hand-tracking. Furthermore, the integration of a mixed reality pass-through feature enables users to experience a comprehensive and immersive environment that blends physical and virtual worlds. These advancements pave the way for future exploration of mixed reality applications, opening up new possibilities for immersive and interactive experiences that combine useful aspects of real and virtual environments. Full article

(This article belongs to the Special Issue Wearable Sensing Devices and Technology)

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14 pages, 1547 KiB

Open AccessArticle

An Internet of Things-Based Home Telehealth System for Smart Healthcare by Monitoring Sleep and Water Usage: A Preliminary Study

by Zunyi Tang, Linlin Jiang, Xin Zhu and Ming Huang

Electronics 2023, 12(17), 3652; https://doi.org/10.3390/electronics12173652 - 29 Aug 2023

Viewed by 1010

Abstract

Recently, the Internet of Things (IoT) has attracted wide attention from many fields, especially healthcare, because of its large capacities for information perception and collection. In this paper, we present an IoT-based home telehealth system for providing smart healthcare management for individuals, especially [...] Read more.

Recently, the Internet of Things (IoT) has attracted wide attention from many fields, especially healthcare, because of its large capacities for information perception and collection. In this paper, we present an IoT-based home telehealth system for providing smart healthcare management for individuals, especially older people. Each client node of the system is mainly composed of an electronic water meter that records the user’s daily water usage, in order to analyze their living patterns and lifestyle as well as ascertain their well-being, and an unobtrusive sleep sensor that monitors the user’s physiological parameters during sleep, such as heart rate (HR), respiratory rate (RR), body movement (BM), and their states on the bed or outside the bed. The collected data can be transmitted to a remote centralized cloud service by a wireless home gateway for analyzing the living pattern and rhythm of users. Furthermore, the periodic feedback of results can be provided to users themselves, as well as their family and health advisers. In the present study, data was collected from a total of 18 older subjects for one year to evaluate the effectiveness of the proposed system. By analyzing living patterns and rhythm, preliminary results indicate the effectiveness of the telehealth system and suggest the potential of the system regarding improvement in the quality of life (QoL) of older people and promotion of their health. Full article

(This article belongs to the Special Issue Wearable Sensing Devices and Technology)

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14 pages, 1581 KiB

Open AccessArticle

A Viscoelastic Model to Evidence Reduced Upper-Limb-Swing Capabilities during Gait for Parkinson’s Disease-Affected Subjects

by Luca Pietrosanti, Cristiano Maria Verrelli, Franco Giannini, Antonio Suppa, Francesco Fattapposta, Alessandro Zampogna, Martina Patera, Viviana Rosati and Giovanni Saggio

Electronics 2023, 12(15), 3347; https://doi.org/10.3390/electronics12153347 - 04 Aug 2023

Viewed by 694

Abstract

Parkinson’s disease (PD) is a chronic neurodegenerative disorder with high worldwide prevalence that manifests with muscle rigidity, tremor, postural instability, and slowness of movement. These motor symptoms are mainly evaluated by clinicians via direct observations of patients and, as such, can potentially be [...] Read more.

Parkinson’s disease (PD) is a chronic neurodegenerative disorder with high worldwide prevalence that manifests with muscle rigidity, tremor, postural instability, and slowness of movement. These motor symptoms are mainly evaluated by clinicians via direct observations of patients and, as such, can potentially be influenced by personal biases and inter- and intra-rater differences. In order to provide more objective assessments, researchers have been developing technology-based systems aimed at objective measurements of motor symptoms, among which are the reduced and/or trembling swings of the lower limbs during gait tests, resulting in data that are potentially prone to more objective evaluations. Within this frame, although the swings of the upper limbs during walking are likewise important, no efforts have been made to reveal their support significance. To fill this lack, this work concerns a technology-based assessment of the forearm-swing capabilities of PD patients with respect to their healthy counterparts. This was obtained by adopting a viscoelastic model validated via measurements during gait tests tackled as an inverse dynamic problem aimed at determining the torque forces acting on the forearms. The obtained results evidence differences in the forearm movements during gait tests of healthy subjects and PD patients with different pathology levels, and, in particular, we evidenced how the worsening of the disease can cause the worsening of the mechanical support offered by the forearm’s swing to the walking process. Full article

(This article belongs to the Special Issue Wearable Sensing Devices and Technology)

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19 pages, 571 KiB

Open AccessArticle

Dynamic Fall Detection Using Graph-Based Spatial Temporal Convolution and Attention Network

by Rei Egawa, Abu Saleh Musa Miah, Koki Hirooka, Yoichi Tomioka and Jungpil Shin

Electronics 2023, 12(15), 3234; https://doi.org/10.3390/electronics12153234 - 26 Jul 2023

Cited by 4 | Viewed by 1632

Abstract

The prevention of falls has become crucial in the modern healthcare domain and in society for improving ageing and supporting the daily activities of older people. Falling is mainly related to age and health problems such as muscle, cardiovascular, and locomotive syndrome weakness, [...] Read more.

The prevention of falls has become crucial in the modern healthcare domain and in society for improving ageing and supporting the daily activities of older people. Falling is mainly related to age and health problems such as muscle, cardiovascular, and locomotive syndrome weakness, etc. Among elderly people, the number of falls is increasing every year, and they can become life-threatening if detected too late. Most of the time, ageing people consume prescription medication after a fall and, in the Japanese community, the prevention of suicide attempts due to taking an overdose is urgent. Many researchers have been working to develop fall detection systems to observe and notify about falls in real-time using handcrafted features and machine learning approaches. Existing methods may face difficulties in achieving a satisfactory performance, such as limited robustness and generality, high computational complexity, light illuminations, data orientation, and camera view issues. We proposed a graph-based spatial-temporal convolutional and attention neural network (GSTCAN) with an attention model to overcome the current challenges and develop an advanced medical technology system. The spatial-temporal convolutional system has recently proven the power of its efficiency and effectiveness in various fields such as human activity recognition and text recognition tasks. In the procedure, we first calculated the motion along the consecutive frame, then constructed a graph and applied a graph-based spatial and temporal convolutional neural network to extract spatial and temporal contextual relationships among the joints. Then, an attention module selected channel-wise effective features. In the same procedure, we repeat it six times as a GSTCAN and then fed the spatial-temporal features to the network. Finally, we applied a softmax function as a classifier and achieved high accuracies of 99.93%, 99.74%, and 99.12% for ImViA, UR-Fall, and FDD datasets, respectively. The high-performance accuracy with three datasets proved the proposed system’s superiority, efficiency, and generality. Full article

(This article belongs to the Special Issue Wearable Sensing Devices and Technology)

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16 pages, 4078 KiB

Open AccessFeature PaperArticle

Usability of Inexpensive Optical Pulse Sensors for Textile Integration and Heartbeat Detection Code Development

by Niclas Richter, Khorolsuren Tuvshinbayar, Guido Ehrmann and Andrea Ehrmann

Electronics 2023, 12(7), 1521; https://doi.org/10.3390/electronics12071521 - 23 Mar 2023

Viewed by 1173

Abstract

Low-cost sensors and single circuit boards such as Arduino and Raspberry Pi have increased the possibility of measuring biosignals by smart textiles with embedded electronics. One of the main problems with such e-textiles is their washability. While batteries are usually removed before washing, [...] Read more.

Low-cost sensors and single circuit boards such as Arduino and Raspberry Pi have increased the possibility of measuring biosignals by smart textiles with embedded electronics. One of the main problems with such e-textiles is their washability. While batteries are usually removed before washing, single-board computers and microcontrollers, as well as electronic sensors, would ideally be kept inside a user-friendly smart garment. Here, we show results of washing tests with optical pulse sensors, which can be used in smart gloves not only for hospitalized patients, and ATtiny85 as an example of a single-board microcontroller, sewn onto different cotton fabrics. We report that even without any encapsulation, all tested sensors and microcontrollers endured 10 washing cycles at 30–60 °C without defects. For easier garment integration, we suggest using an ESP8266 with integrated Wi-Fi functionality and offer a new program code to measure beats per minute (BMP) with optimized accuracy. Full article

(This article belongs to the Special Issue Wearable Sensing Devices and Technology)

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22 pages, 6883 KiB

Open AccessArticle

Lidom: A Disease Risk Prediction Model Based on LightGBM Applied to Nursing Homes

by Feng Zhou, Shijing Hu, Xin Du, Xiaoli Wan, Zhihui Lu and Jie Wu

Electronics 2023, 12(4), 1009; https://doi.org/10.3390/electronics12041009 - 17 Feb 2023

Cited by 2 | Viewed by 1476

Abstract

With the innovation of technologies such as sensors and artificial intelligence, some nursing homes use wearable devices to monitor the movement and physiological indicators of the elderly and provide prompts for any health risks. Nevertheless, this kind of risk warning is a decision [...] Read more.

With the innovation of technologies such as sensors and artificial intelligence, some nursing homes use wearable devices to monitor the movement and physiological indicators of the elderly and provide prompts for any health risks. Nevertheless, this kind of risk warning is a decision based on a particular physiological indicator. Therefore, such decisions cannot effectively predict health risks. To achieve this goal, we propose a model Lidom (A LightGBM-based Disease Prediction Model) based on the combination of the LightGBM algorithm, InterpretML framework, and sequence confrontation network (SeqGAN). The Lidom model first solves the problem of uneven samples based on the sequence confrontation network (SeqGAN), then trains the model based on the LightGBM algorithm, uses the InterpretML framework for analysis, and finally obtains the best model. This paper uses the public dataset MIMIC-III, subject data, and the early diabetes risk prediction dataset in UCI as sample data. The experimental results show that the Lidom model has an accuracy rate of 93.46% for disease risk prediction and an accuracy rate of 99.8% for early diabetes risk prediction. The results show that the Lidom model can provide adequate support for the prediction of the health risks of the elderly. Full article

(This article belongs to the Special Issue Wearable Sensing Devices and Technology)

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16 pages, 1720 KiB

Open AccessArticle

MDP-Based MAC Protocol for WBANs in Edge-Enabled eHealth Systems

by Haoru Su, Meng-Shiuan Pan, Huamin Chen and Xiliang Liu

Electronics 2023, 12(4), 947; https://doi.org/10.3390/electronics12040947 - 14 Feb 2023

Cited by 8 | Viewed by 1425

Abstract

In recent years, eHealth systems based on the Internet of Things (IoT) have attracted considerable attention. The wireless body area network (WBAN) is an essential technology of eHealth systems. A major challenge in WBAN is the design of the medium access control (MAC) [...] Read more.

In recent years, eHealth systems based on the Internet of Things (IoT) have attracted considerable attention. The wireless body area network (WBAN) is an essential technology of eHealth systems. A major challenge in WBAN is the design of the medium access control (MAC) protocol, which plays a significant role in avoiding collisions, enhancing the energy efficiency, maximizing the network life, and improving the quality of service (QoS) as well as the quality of experience (QoE). In this study, we apply the mobile edge computing (MEC) network architecture to an eHealth system and design a multi-channel MAC protocol for WBAN based on the Markov decision process (MDP). In this protocol, the channel condition and the reward value are considered. By continuously interacting with the environment, the optimal channel resource allocation strategy is generated. Simulation results indicate that the proposed WBAN MAC protocol can adaptively assign different channels to the sensor nodes for data transmission, thereby reducing the collision rate, decreasing the energy consumption, improving the channel utilization, and enhancing the system throughput and QoE. Full article

(This article belongs to the Special Issue Wearable Sensing Devices and Technology)

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15 pages, 3498 KiB

Open AccessArticle

Data Glove with Bending Sensor and Inertial Sensor Based on Weighted DTW Fusion for Sign Language Recognition

by Chenghong Lu, Shingo Amino and Lei Jing

Electronics 2023, 12(3), 613; https://doi.org/10.3390/electronics12030613 - 26 Jan 2023

Cited by 6 | Viewed by 1957

Abstract

There are numerous communication barriers between people with and without hearing impairments. Writing and sign language are the most common modes of communication. However, written communication takes a long time. Furthermore, because sign language is difficult to learn, few people understand it. It [...] Read more.

There are numerous communication barriers between people with and without hearing impairments. Writing and sign language are the most common modes of communication. However, written communication takes a long time. Furthermore, because sign language is difficult to learn, few people understand it. It is difficult to communicate between hearing-impaired people and hearing people because of these issues. In this research, we built the Sign-Glove system to recognize sign language, a device that combines a bend sensor and WonderSense (an inertial sensor node). The bending sensor was used to recognize the hand shape, and WonderSense was used to recognize the hand motion. The system collects a more comprehensive sign language feature. Following that, we built a weighted DTW fusion multi-sensor. This algorithm helps us to combine the shape and movement of the hand to recognize sign language. The weight assignment takes into account the feature contributions of the sensors to further improve the recognition rate. In addition, a set of interfaces was created to display the meaning of sign language words. The experiment chose twenty sign language words that are essential for hearing-impaired people in critical situations. The accuracy and recognition rate of the system were also assessed. Full article

(This article belongs to the Special Issue Wearable Sensing Devices and Technology)

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20 pages, 2004 KiB

Open AccessArticle

A Novel Anchor-Free Localization Method Using Cross-Technology Communication for Wireless Sensor Network

by Nan Jing, Bowen Zhang and Lin Wang

Electronics 2022, 11(23), 4025; https://doi.org/10.3390/electronics11234025 - 04 Dec 2022

Cited by 3 | Viewed by 1334

Abstract

In recent years, wireless sensor networks have been used in a wide range of indoor localization-based applications. Although promising, the existing works are dependent on a large number of anchor nodes to achieve localization, which brings the issues of increasing cost and additional [...] Read more.

In recent years, wireless sensor networks have been used in a wide range of indoor localization-based applications. Although promising, the existing works are dependent on a large number of anchor nodes to achieve localization, which brings the issues of increasing cost and additional maintenance. Inspired by the cross-technology communication, an emerging technique that enables direct communication among heterogeneous wireless devices, we propose an anchor-free distributed method, which leverages the installed Wi-Fi APs to range instead of traditional anchor nodes. More specifically, for the asymmetric coverage of Wi-Fi and ZigBee nodes, we first design a progressive method, where the first unknown node estimates its location based on two Wi-Fi APs and a sink node, then once achieving its position, it acts as the alternative sink node of the next hop node. This process is repeated until the new members can obtain their positions. Second, as a low-power technology, ZigBee signal may be submerged in strong signal such as Wi-Fi. To overcome this problem, a maximum prime number is deployed to be the Wi-Fi broadcasting period based on the numerical analysis theory. Among many of prime numbers, we have the opportunity to select an appropriate one to achieve full coverage with the relatively small packet collisions. Last, simulations and experiments are performed to evaluate the proposal. The evaluation results show that the proposal can achieve decimeter level accuracy without deploying any anchor node. Moreover, the proposal demonstrates the anti-interference ability in the crowded open spectrum environment. Full article

(This article belongs to the Special Issue Wearable Sensing Devices and Technology)

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20 pages, 7083 KiB

Open AccessArticle

MetaEar: Imperceptible Acoustic Side Channel Continuous Authentication Based on ERTF

by Zhuo Chang, Lin Wang, Binbin Li and Wenyuan Liu

Electronics 2022, 11(20), 3401; https://doi.org/10.3390/electronics11203401 - 20 Oct 2022

Cited by 4 | Viewed by 1664

Abstract

With the development of ubiquitous mobile devices, biometrics authentication has received much attention from researchers. For immersive experiences in AR (augmented reality), convenient continuous biometric authentication technologies are required to provide security for electronic assets and transactions through head-mounted devices. Existing fingerprint or [...] Read more.

With the development of ubiquitous mobile devices, biometrics authentication has received much attention from researchers. For immersive experiences in AR (augmented reality), convenient continuous biometric authentication technologies are required to provide security for electronic assets and transactions through head-mounted devices. Existing fingerprint or face authentication methods are vulnerable to spoof attacks and replay attacks. In this paper, we propose MetaEar, which harnesses head-mounted devices to send FMCW (Frequency-Modulated Continuous Wave) ultrasonic signals for continuous biometric authentication of the human ear. CIR (channel impulse response) leveraged the channel estimation theory to model the physiological structure of the human ear, called the Ear Related Transfer Function (ERTF). It extracts unique representations of the human ear’s intrinsic and extrinsic biometric features. To overcome the data dependency of Deep Learning and improve its deployability in mobile devices, we use the lightweight learning approach for classification and authentication. Our implementation and evaluation show that the average accuracy can reach about 96% in different scenarios with small amounts of data. MetaEar enables one to handle immersive deployable authentication and be more sensitive to replay and impersonation attacks. Full article

(This article belongs to the Special Issue Wearable Sensing Devices and Technology)

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