A machine learning approach to fall detection algorithm using wearable sensor

Hsieh, Chia-Yeh; Huang, Chih-Ning; Liu, Kai-Chun; Chu, Woei-Chyn; Chan, Chia-Tai

doi:10.1109/icamse.2016.7840209

Cited by 22 publications

(4 citation statements)

References 18 publications

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“…ey also identified the patterns associated with fall based on the locations of the smartphone in the user's body such as pants' side pocket, shirt chest's pocket, or texting. e performance of the k-NN classifier discussed by Hsieh et al [29] is slightly better than the performance achieved by our LSTM model in [29] k-NN, SVM k-NN ACC: 96.2 Colon et al [30] reshold ACC: 81.3 Ajerla et al [31] LSTM ACC: 95.8 fall detection. But our experiments also provided insights on the optimum sensor combination as well as the optimum frequency for data collection for fall detection.…”

Section: Methodsmentioning

confidence: 65%

A Real-Time Patient Monitoring Framework for Fall Detection

Ajerla

Mahfuz

Zulkernine

2019

Wireless Communications and Mobile Computing

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Fall detection is a major problem in the healthcare department. Elderly people are more prone to fall than others. There are more than 50% of injury-related hospitalizations in people aged over 65. Commercial fall detection devices are expensive and charge a monthly fee for their services. A more affordable and adaptable system is necessary for retirement homes and clinics to build a smart city powered by IoT and artificial intelligence. An effective fall detection system would detect a fall and send an alarm to the appropriate authorities. We propose a framework that uses edge computing where instead of sending data to the cloud, wearable devices send data to a nearby edge device like a laptop or mobile device for real-time analysis. We use cheap wearable sensor devices from MbientLab, an open source streaming engine called Apache Flink for streaming data analytics, and a long short-term memory (LSTM) network model for fall classification. The model is trained using a published dataset called “MobiAct.” Using the trained model, we analyse optimal sampling rates, sensor placement, and multistream data correction. Our edge computing framework can perform real-time streaming data analytics to detect falls with an accuracy of 95.8%.

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Section: Methodsmentioning

confidence: 65%

A Real-Time Patient Monitoring Framework for Fall Detection

Ajerla

Mahfuz

Zulkernine

2019

Wireless Communications and Mobile Computing

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“…Over the previous ten or more years, the development of fall detection systems became a quite hot topic due to the aging society. Various technologies and methodologies have been adopted, such as the IoT [ 33 , 34 , 35 ] and artificial intelligence [ 36 , 37 ].…”

Section: Related Workmentioning

confidence: 99%

Fall Recognition Based on an IMU Wearable Device and Fall Verification through a Smart Speaker and the IoT

Lin

Chen

Lee

et al. 2023

Sensors

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A fall is one of the most devastating events that aging people can experience. Fall-related physical injuries, hospital admission, or even mortality among the elderly are all critical health issues. As the population continues to age worldwide, there is an imperative need to develop fall detection systems. We propose a system for the recognition and verification of falls based on a chest-worn wearable device, which can be used for elderly health institutions or home care. The wearable device utilizes a built-in three-axis accelerometer and gyroscope in the nine-axis inertial sensor to determine the user’s postures, such as standing, sitting, and lying down. The resultant force was obtained by calculation with three-axis acceleration. Integration of three-axis acceleration and a three-axis gyroscope can obtain a pitch angle through the gradient descent algorithm. The height value was converted from a barometer. Integration of the pitch angle with the height value can determine the behavior state including sitting down, standing up, walking, lying down, and falling. In our study, we can clearly determine the direction of the fall. Acceleration changes during the fall can determine the force of the impact. Furthermore, with the IoT (Internet of Things) and smart speakers, we can verify whether the user has fallen by asking from smart speakers. In this study, posture determination is operated directly on the wearable device through the state machine. The ability to recognize and report a fall event in real-time can help to lessen the response time of a caregiver. The family members or care provider monitor, in real-time, the user’s current posture via a mobile device app or internet webpage. All collected data supports subsequent medical evaluation and further intervention.

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“…Sensörler, veri toplama işlemi sırasında çeşitli düşme parametreleriyle ilgili veriler üretir. Bu bağlamda, uygulamanın gereksinimlerine smartofjournal.com / editorsmartjournal@gmail.com / Open Access Refereed / E-Journal / Refereed / Indexed göre düşme eylemlerini sınıflandırmak veya tanımlamak için makine öğrenimi yöntemlerinden yararlanılır (Hsieh et al, 2016).…”

Section: Sağlik Hi̇zmetleri̇nde Yapay Zekaunclassified

Yaşlı Bakım: Modern Teknolojiler

Turgut¹

2023

smryj

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Günümüzde, dünya çapındaki çoğu insan yeni teknolojiler, tıbbi gelişmeler ve sosyal gelişmeler nedeniyle altmışlı yaşlarında ve sonrasında yaşamayı bekleyebilir. Bu nedenle, hemen hemen tüm ülkeler nüfuslarındaki yaşlı yetişkinlerin (65 yaş ve üstü ) oranında artış yaşamaktadır. Dünya Sağlık Örgütü'nün (WHO) yayınladığı rapora göre, nüfus yaşlanması hem gelişmiş hem de gelişmekte olan ülkelerde giderek daha büyümektedir (

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A machine learning approach to fall detection algorithm using wearable sensor

Cited by 22 publications

References 18 publications

A Real-Time Patient Monitoring Framework for Fall Detection

A Real-Time Patient Monitoring Framework for Fall Detection

Fall Recognition Based on an IMU Wearable Device and Fall Verification through a Smart Speaker and the IoT

Yaşlı Bakım: Modern Teknolojiler

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