Fall Detection with Supervised Machine Learning using Wearable Sensors

Giuffrida, Davide; Benetti, Guido; Martini, Daniele De; Facchinetti, Tullio

doi:10.1109/indin41052.2019.8972246

Cited by 22 publications

(7 citation statements)

References 15 publications

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“…The wearable is used to quantify the disorder's motor symptoms 6. D. Giuffrida et al (Giuffrida et al, 2019) Fall detection system Used sensors such as accelerometer and gyroscope SVM classifier…”

Section: Movement Disorders Quantificationmentioning

confidence: 99%

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Review on the wearable health-care monitoring system with robust motion artifacts reduction techniques

Prabakaran

Rufus

2021

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Purpose Wearables are gaining prominence in the health-care industry and their use is growing. The elderly and other patients can use these wearables to monitor their vitals at home and have them sent to their doctors for feedback. Many studies are being conducted to improve wearable health-care monitoring systems to obtain clinically relevant diagnoses. The accuracy of this system is limited by several challenges, such as motion artifacts (MA), power line interference, false detection and acquiring vitals using dry electrodes. This paper aims to focus on wearable health-care monitoring systems in the literature and provides the effect of MA on the wearable system. Also presents the problems faced while tracking the vitals of users. Design/methodology/approach MA is a major concern and certainly needs to be suppressed. An analysis of the causes and effects of MA on wearable monitoring systems is conducted. Also, a study from the literature on motion artifact detection and reduction is carried out and presented here. The benefits of a machine learning algorithm in a wearable monitoring system are also presented. Finally, distinct applications of the wearable monitoring system have been explored. Findings According to the study reduction of MA and multiple sensor data fusion increases the accuracy of wearable monitoring systems. Originality/value This study also presents the outlines of design modification of dry/non-contact electrodes to minimize the MA. Also, discussed few approaches to design an efficient wearable health-care monitoring system.

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Section: Movement Disorders Quantificationmentioning

confidence: 99%

“…A. The authors evaluated the system with the SisFall data set, obtaining an F1 score higher than 97% and a recall higher than 99.7% (Giuffrida et al, 2019). A wearable electroencephalogram (EEG) device to detect the changes in brain activity associated with visual information was created by (Suzuki et al, 2019).…”

Section: Movement Disorders Quantificationmentioning

confidence: 99%

Review on the wearable health-care monitoring system with robust motion artifacts reduction techniques

Prabakaran

Rufus

2021

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“…Giuffrida et al [ 21 ] selected the 10 most frequently leveraged features in different combinations of features to train the parameters of a Support Vector Machine (SVM) model. Yu et al [ 22 ] proposed a fall detection algorithm based on the Hidden Markov Model (HMM).…”

Section: Related Workmentioning

confidence: 99%

A Class-Imbalanced Deep Learning Fall Detection Algorithm Using Wearable Sensors

Zhang

Wang

2021

Sensors

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Falling represents one of the most serious health risks for elderly people; it may cause irreversible injuries if the individual cannot obtain timely treatment after the fall happens. Therefore, timely and accurate fall detection algorithm research is extremely important. Recently, a number of researchers have focused on fall detection and made many achievements, and most of the relevant algorithm studies are based on ideal class-balanced datasets. However, in real-life applications, the possibilities of Activities of Daily Life (ADL) and fall events are different, so the data collected by wearable sensors suffers from class imbalance. The previously developed algorithms perform poorly on class-imbalanced data. In order to solve this problem, this paper proposes an algorithm that can effectively distinguish falls from a large amount of ADL signals. Compared with the state-of-the-art fall detection algorithms, the proposed method can achieve the highest score in multiple evaluation methods, with a sensitivity of 99.33%, a specificity of 91.86%, an F-Score of 98.44% and an AUC of 98.35%. The results prove that the proposed algorithm is effective on class-imbalanced data and more suitable for real-life application compared to previous works.

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“…Among all classifiers, SVM reports a highest accuracy of 99.98%. Daniele De Martini et al [4] proposed wearable sensors and ML based fall determining system aimed to detect fall occurrence in real-time such that a remote notification is triggered automatically prompting the request for necessary assistance. It is based on supervised training on SVM algorithm for fall detection.…”

Section: Literature Surveymentioning

confidence: 99%

Detecting Real-Time Fall of Elderly People Using Machine Learning

Prathima¹

2021

IJRASET

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Fall is a significant national health issue for the elderly people, generally resulting in severe injuries when the person lies down on the floor over an extended period without any aid after experiencing a great fall. Thus, elders need to be cared very attentively. A supervised-machine learning based fall detection approach with accelerometer, gyroscope is devised. The system can detect falls by grouping different actions as fall or non-fall events and the care taker is alerted immediately as soon as the person falls. The public dataset SisFall with efficient class of features is used to identify fall. The Random Forest (RF) and Support Vector Machine (SVM) machine learning algorithms are employed to detect falls with lesser false alarms. The SVM algorithm obtain a highest accuracy of 99.23% than RF algorithm. Keywords: Fall detection, Machine learning, Supervised classification, Sisfall, Activities of daily living, Wearable sensors, Random Forest, Support Vector Machine

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Fall Detection with Supervised Machine Learning using Wearable Sensors

Cited by 22 publications

References 15 publications

Review on the wearable health-care monitoring system with robust motion artifacts reduction techniques

Review on the wearable health-care monitoring system with robust motion artifacts reduction techniques

A Class-Imbalanced Deep Learning Fall Detection Algorithm Using Wearable Sensors

Detecting Real-Time Fall of Elderly People Using Machine Learning

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