Elderly Fall Detection and Location Tracking System Using Heterogeneous Wireless Networks

Fung, Ng Ming; Ann, Jackson Wong Sing; Tung, Yew Hoe; Kheau, Chung Seng; Chekima, Ali

doi:10.1109/iscaie.2019.8743664

Cited by 17 publications

(7 citation statements)

References 8 publications

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“…The system can also be expanded by implementing alternative connectivity options such as cellular connection to allow usage of the system in areas without Wi-Fi connectivity or in independent settings such as in private residents for home quarantine and self-monitoring. The proposed system can also be further expanded by utilizing global positioning system (GPS) and global system for mobile communications (GSM) as used in [27], where emergency alerts through SMS that contains location coordinates can be sent to relevant healthcare workers for swift and immediate intervention. This is especially relevant in cases where a home quarantine is done instead of monitoring at quarantine centers or hospitals.…”

Section: Discussionmentioning

confidence: 99%

Internet of things based real-time coronavirus 2019 disease patient health monitoring system

Ejin

Yew

Mamat

et al. 2022

IJECE

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<span lang="EN-US">The coronavirus disease (COVID-19) outbreak has led to many infected worldwide and has become a global crisis. COVID-19 manifests in the form of shortness of breath, coughing and fever. More people are getting infected and healthcare systems worldwide are overwhelmed as healthcare workers become exhausted and infected. Thus, remote monitoring for COVID-19 patients is required. An internet of things (IoT) based real-time health monitoring system for COVID-19 patients was proposed. It features monitoring of five physiological parameters, namely electrocardiogram (ECG), heart rate (HR), respiratory rate (RR), oxygen saturation (SpO2) and body temperature. These vitals are processed by the main controller and transmitted to the cloud for storage. Healthcare professionals can read real-time patient vitals on the web-based dashboard which is equipped with an alert service. The proposed system was able to transmit and display all parameters in real-time accurately without any packet loss or transmission errors. The accuracy of body temperature readings, RR, SpO2 and HR, is up to 99.7%, 100%, 97.97% and 98.34%, respectively. Alerts were successfully sent when the parameters reached unsafe levels. With the proposed system, healthcare professionals can remotely monitor COVID-19 patients with greater ease, lessen their exposure to the pathogen, and improve patient monitoring.</span>

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Section: Discussionmentioning

confidence: 99%

Internet of things based real-time coronavirus 2019 disease patient health monitoring system

Ejin

Yew

Mamat

et al. 2022

IJECE

Self Cite

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“…They can determine the main factors' effect on localization accuracy in cluttered environments: numbers and layout of RF receivers, location density of receiving RSSI, and person direction that carries the transmitter. In [26], the authors proposed location determination and fall detection for the elderly who live alone using WiFi and GPS. The proposed system is wearable hardware connected to WiFi and an accelerometer sensor to detect falls.…”

Section: Related Workmentioning

confidence: 99%

Children Tracking System Based on ZigBee Wireless Network and Neural Network

Ahmed

Gharghan

Mutlag

et al. 2023

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The safety of children is one of the fundamental concerns of parents. Recently, child kidnapping has increased by a large percentage, some children have been found, and some children have not found yet. This paper proposes an indoor localization system based on ZigBee wireless sensor network (WSN) and Backpropagation Artificial Neural Network (BP-ANN) to locate the child in an indoor environment. Several ANN topologies were investigated to select the best one with minimum tracking or localization error. The Received Signal Strength Indicator (RSSI) was collected from four ZigBee XBee S2C anchor nodes by the mobile node carried by the child in an indoor area of 32m × 32m. The RSSI was collected from 127 test points inside the tested area. The measured RSSI was used to train, test, and validate the performance of BP-ANN to determine the two dimensions (2D) of the target child’s location. Different topologies of ANN have been examined for training, testing, and validation which are 5-5, 10-10, 15-15, and 20-20 neurons in the hidden layer. The findings indicate that the 20-20 ANN topology can achieve higher accuracy than other topologies. Additionally, 20-20 topology localization errors were 1.0, 1.157, and 1.356 m for training, testing, and validating ANN performance.

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“…A wide range of approaches have been explored to design effective fall detection systems, including the use of wearable devices [7][8][9][10][11] and smartphones [12][13][14][15] with microphones [16], cameras [17], accelerometers and gyroscopes [18,19], GPS [20], and combinations of multiple sensors [21].…”

Section: Related Workmentioning

confidence: 99%

“…Signal processing techniques play a crucial role in all of the aforementioned fall detection systems, as they extract meaningful features from sensor data, facilitating the accurate detection of fall events across diverse system types. Multiple signal processing techniques have been employed in fall detection systems, including orientation filters [9], quaternions [10], thresholding techniques [13,[18][19][20], histograms of oriented gradients [17], clustering algorithms [21], Bayesian segmentation approaches [23], spatiotemporal analysis methods [27], Kalman Filters [30], sensor data fusion [31], and wavelet transforms [32].…”

Section: Related Workmentioning

confidence: 99%

Dataset and System Design for Orthopedic Walker Fall Detection and Activity Logging Using Motion Classification

Huang,

Garcia

2023

Applied Sciences

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An accurate, economical, and reliable device for detecting falls in persons ambulating with the assistance of an orthopedic walker is crucially important for the elderly and patients with limited mobility. Existing wearable devices, such as wristbands, are not designed for walker users, and patients may not wear them at all times. This research proposes a novel idea of attaching an internet-of-things (IoT) device with an inertial measurement unit (IMU) sensor directly to an orthopedic walker to perform real-time fall detection and activity logging. A dataset is collected and labeled for walker users in four activities, including idle, motion, step, and fall. Classic machine learning algorithms are evaluated using the dataset by comparing their classification performance. Deep learning with a convolutional neural network (CNN) is also explored. Furthermore, the hardware prototype is designed by integrating a low-power microcontroller for onboard machine learning, an IMU sensor, a rechargeable battery, and Bluetooth wireless connectivity. The research results show the promise of improved safety and well-being of walker users.

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Elderly Fall Detection and Location Tracking System Using Heterogeneous Wireless Networks

Cited by 17 publications

References 8 publications

Internet of things based real-time coronavirus 2019 disease patient health monitoring system

Internet of things based real-time coronavirus 2019 disease patient health monitoring system

Children Tracking System Based on ZigBee Wireless Network and Neural Network

Dataset and System Design for Orthopedic Walker Fall Detection and Activity Logging Using Motion Classification

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