Development of ZigBee Triaxial Accelerometer Based Human Activity Recognition System

Irene, S.; Shwetha, N.M.; Haribabu, P; Pitchiah, R.

doi:10.1109/cit/iucc/dasc/picom.2015.357

Cited by 4 publications

(2 citation statements)

References 9 publications

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“…Irene et al [9] and Abdelhedi et al [10] proposed a 3-axial accelerometer-based fall detection system which utilizes multiple predefined threshold values to detect falls. Tri et al [11] employed the smartphone acceleration sensor for automatic fall detection.…”

Section: Related Workmentioning

confidence: 99%

A FPGA threshold-based fall detection algorithm for elderly fall monitoring with verilog

Aziz

2021

Bulletin EEI

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Fall is one of the leading causes of accidental or unintentional injury deaths worldwide due to serious injuries such as head traumas and hip fractures. As life expectancy improved, the rapid increase in aging population implied the need for the development of vital sign detector such as fall detector to help elderly in seeking for medical attention. Immediate rescue could prevent victims from the risk of suspension trauma and reduce the mortality rate among elderly population due to fall accident effectively. This paper presents the development of FPGA-based fall detection algorithm using a threshold-based analytical method. The proposed algorithm is to minimize the rate of false positive fall detection proposed from other researchers by including the non-fall events in the data analysis. Based on the performance evaluation, the proposed algorithm successfully achieved a sensitivity of 97.45% and specificity of 97.38%. The proposed algorithm was able to differentiate fall events and non-fall events effectively, except for fast lying and fall that ending with sitting position. The proposed algorithm shows a good result and the performance of the proposed algorithm can be further improved by using an additional gyroscope to detect the posture of the lower body part.

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Section: Related Workmentioning

confidence: 99%

A FPGA threshold-based fall detection algorithm for elderly fall monitoring with verilog

Aziz

2021

Bulletin EEI

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“…There are many single-sensor HAR-related studies for different applications [11], [12], such as household appliance control [13], exercise data recording [14], and daily activity recognition [15], [16]. Irene et al [17] developed a low-cost wearable sensor node with a triaxial accelerometer that could transmit data to a PC via ZigBee and recognize simple daily life motions based on leg kinematics, including sitting, standing, and walking. Yen et al [18] proposed a waist wearable device for daily activity analysis of patients with diseases like dialysis patients who have blood vessel prostheses on their arms, which make them inconvenient for strenuous exercises.…”

Section: Introductionmentioning

confidence: 99%

Integrated Sensing and Computing for Wearable Human Activity Recognition with MEMS IMU and BLE Network

Zhang

Liu

Meng

et al. 2022

Measurement Science Review

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The miniature sensor devices and power-efficient Body Area Networks (BANs) for Human Activity Recognition (HAR) have gained increasing interest in different fields, including Daily Life Assistants (DLAs), medical treatment, sports analysis, etc. The HAR systems normally collect data with wearable sensors and implement the computational tasks with a host machine, where real-time transmission and processing of sensor data raise a challenge for both the network and the host machine. This investigation focuses on the hardware/software co-design for optimized sensing and computing of wearable HAR sensor networks. The contributions include (1) design of a miniature wearable sensor node integrating a Micro-Electro-Mechanical System Inertial Measurement Unit (MEMS IMU) with a Bluetooth Low Energy (BLE) in-built Micro-Control Unit (MCU) for unobtrusive wearable sensing; (2) task-centric optimization of the computation by shifting data pre-processing and feature extraction to sensor nodes for in-situ computing, which reduces data transmission and relieves the load of the host machine; (3) optimization and evaluation of classification algorithms Particle Swarm Optimization-based Support Vector Machine (PSO-SVM) and Cross Validation-based K-Nearest Neighbors (CV-KNN) for HAR with the presented techniques. Finally, experimental studies were conducted with two sensor nodes worn on the wrist and elbow to verify the effectiveness of the recognition of 10 virtual handwriting activities, where 10 recruited participants each repeated an activity 5 times. The results demonstrate that the proposed system can implement HAR tasks effectively with an accuracy of 99.20 %.

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An EnOcean Wearable Device with Fall Detection Algorithm Integrated with a Smart Home System

et al. 2018

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Development of ZigBee Triaxial Accelerometer Based Human Activity Recognition System

Cited by 4 publications

References 9 publications

A FPGA threshold-based fall detection algorithm for elderly fall monitoring with verilog

A FPGA threshold-based fall detection algorithm for elderly fall monitoring with verilog

Integrated Sensing and Computing for Wearable Human Activity Recognition with MEMS IMU and BLE Network

An EnOcean Wearable Device with Fall Detection Algorithm Integrated with a Smart Home System

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