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

Torres, Guilherme Gerzson; Henriques, Renato Ventura Bayan; Pereira, Carlos Eduardo; Müller, Ivan

doi:10.1016/j.ifacol.2018.06.228

Cited by 21 publications

(9 citation statements)

References 10 publications

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“…From the performance evaluation in detecting fall events, the proposed algorithm has achieved 100% in true positive fall detection for all type of fall events, including forward, backward, leftward and rightward falling. The specificity of the proposed algorithm was slightly lower compared to Torres et al [15] due to no number of test coverage on non-fall like activities. Vetsandonphong [13], shows the sensitivity is 95% and specificity 90% based on 20 activities of ADL and falling down motion.…”

Section: Performance Evaluationuationmentioning

confidence: 63%

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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: Performance Evaluationuationmentioning

confidence: 63%

“…ISSN: 2302-9285  Torres et al [15] introduced a chest mounted EnOcean fall detection wearable sensor for ultra-low power networks that capable to interact with a smart home system. The wearable sensor detects fall incident by using data fusion of accelerometer and gyroscope.…”

Section: Bulletin Of Electr Eng and Infmentioning

confidence: 99%

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

Aziz

2021

Bulletin EEI

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“…Ref. [27] proposes a wearable sensor for ultra-low-power networks that connect with a smart home system. The equipment is capable of detecting falls which can assist in the monitoring of older people to improve safety.…”

Section: State-of-the-art Workmentioning

confidence: 99%

XAI-Fall: Explainable AI for Fall Detection on Wearable Devices Using Sequence Models and XAI Techniques

et al. 2022

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A fall detection system is vital for the safety of older people, as it contacts emergency services when it detects a person has fallen. There have been various approaches to detect falls, such as using a single tri-axial accelerometer to detect falls or fixing sensors on the walls of a room to detect falls in a particular area. These approaches have two major drawbacks: either (i) they use a single sensor, which is insufficient to detect falls, or (ii) they are attached to a wall that does not detect a person falling outside its region. Hence, to provide a robust method for detecting falls, the proposed approach uses three different sensors for fall detection, which are placed at five different locations on the subject’s body to gather the data used for training purposes. The UMAFall dataset is used to attain sensor readings to train the models for fall detection. Five models are trained corresponding to the five sensors models, and a majority voting classifier is used to determine the output. Accuracy of 93.5%, 93.5%, 97.2%, 94.6%, and 93.1% is achieved on each of the five sensors models, and 92.54% is the overall accuracy achieved by the majority voting classifier. The XAI technique called LIME is incorporated into the system in order to explain the model’s outputs and improve the model’s interpretability.

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“…The device becomes part of the smart home system designed to provide convenience and observation for the elderly thanks to its integration with the Home Assistant Platform. The system is able to detecting efficiently 4 different types of falls and four different forms of Activities of Daily Living (ADL), and after the event of a falling the device will detect and transmit an alarm telegram [10]. J. C. Dogan and M. S. Hossain gathered data from smartphone sensors and used it to provide a brand-new two-step fall detection algorithm.…”

Section: Fall Detectionmentioning

confidence: 99%

Smartphone Inertial Sensors Applications in Medical Care Field: A Comprehensive Review

Ibrahim¹,

Makki²

2022

IJERAT

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Sensors of various types are built into the majority of current smartphones each with a distinct function that aids the device's performance. Micro-Electro-Mechanical System (MEMS) inertial sensors, such as gyroscopes (gyros) and accelerometers, are built into smartphones. These embedded inertial sensors have improved user experience and accelerated smartphone development and it can be used to detect events or collect information about human activities. Additionally, machine learning algorithms applied to smartphone sensor data could have a wide range of uses in entertainment, health care, finance and other fields. There have been massive financial gains when a smartphone is utilized to provide telemedicine and remote healthcare assistance. This paper provides a brief overview of all the recent work that have been done in the medical care field by utilizing inertial navigation sensors (gyroscopes and accelerometers) whether the inertial sensors were used together or separately. The goal of this review is to outline the most recent trends in four topics. First, we’ll go over fall detection systems released research publications. Second, we'll go over the most recent scholarly papers on human activity monitoring systems that have been published. Third, we’ll review the most recently released research publications on epilepsy detection. Last, we’ll look over the recently released papers on Parkinson’s disease. This work also includes some important applications that use external inertial sensors for comparison purposes.

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

Cited by 21 publications

References 10 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

XAI-Fall: Explainable AI for Fall Detection on Wearable Devices Using Sequence Models and XAI Techniques

Smartphone Inertial Sensors Applications in Medical Care Field: A Comprehensive Review

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