A classifier based approach to real-time fall detection using low-cost wearable sensors

Diep, Nguyen Ngoc; Pham, Cuong; Phương, Từ Minh

doi:10.1109/socpar.2013.7054110

Cited by 10 publications

(7 citation statements)

References 25 publications

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“…To reduce the number of false alarms while maintaining a high detection rate, several studies used machine learning as an alternative [ 10 , 11 , 15 , 18 , 19 ]. Vallejo et al [ 15 ] developed an artificial neural-network-based approach for fall detection using FNSW with a 10-sample length.…”

Section: Related Workmentioning

confidence: 99%

“…A study conducted by Diep et al [ 10 ] used a Wii remote controller as a low-cost fall detector with a support vector machine (SVM)-based classifier, using acceleration data from 12 subjects in a laboratory environment. Their approach used FOSW with a length of 1.8 s and an 0.6 s overlap, and was able to reach 91.9% precision and 94.4% recall.…”

Section: Related Workmentioning

confidence: 99%

“…Threshold-based approaches detect falls by checking if the measured acceleration exceeds a predefined (and fixed) value [ 6 , 7 , 8 ]. Machine learning-based approaches use labeled data to train a classifier using supervised machine learning algorithms (e.g., the support vector machine (SVM), decision tree, and/or artificial neural networks) that can recognize the characteristic features of falls [ 9 , 10 , 11 , 12 , 13 , 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

“…Machine learning-based approaches usually need a fixed-length overlapping sliding window (FOSW) [ 10 , 11 , 18 ] or a fixed-length non-overlapping sliding window (FNSW) [ 19 ] to split the data sequence into several segments. Then, features are extracted from each segment for training and testing of the classifier.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

An Event-Triggered Machine Learning Approach for Accelerometer-Based Fall Detection

Putra

Brusey

Gaura

et al. 2017

Sensors

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The fixed-size non-overlapping sliding window (FNSW) and fixed-size overlapping sliding window (FOSW) approaches are the most commonly used data-segmentation techniques in machine learning-based fall detection using accelerometer sensors. However, these techniques do not segment by fall stages (pre-impact, impact, and post-impact) and thus useful information is lost, which may reduce the detection rate of the classifier. Aligning the segment with the fall stage is difficult, as the segment size varies. We propose an event-triggered machine learning (EvenT-ML) approach that aligns each fall stage so that the characteristic features of the fall stages are more easily recognized. To evaluate our approach, two publicly accessible datasets were used. Classification and regression tree (CART), k-nearest neighbor (k-NN), logistic regression (LR), and the support vector machine (SVM) were used to train the classifiers. EvenT-ML gives classifier F-scores of 98% for a chest-worn sensor and 92% for a waist-worn sensor, and significantly reduces the computational cost compared with the FNSW- and FOSW-based approaches, with reductions of up to 8-fold and 78-fold, respectively. EvenT-ML achieves a significantly better F-score than existing fall detection approaches. These results indicate that aligning feature segments with fall stages significantly increases the detection rate and reduces the computational cost.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Event-Triggered Machine Learning Approach for Accelerometer-Based Fall Detection

Putra

Brusey

Gaura

et al. 2017

Sensors

View full text Add to dashboard Cite

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“…Wearable-sensor-based methods have been widely studied as they are low cost and non-invasive in terms of privacy. Traditional machine-learning-based fall-detection methods involving wearable sensors usually use a non-lapping [43] or overlapping [44,45] sliding window to segment the data then extract features to classify fall and non-fall events. However, these methods may lose useful information.…”

Section: Related Workmentioning

confidence: 99%

Robust Self-Adaptation Fall-Detection System Based on Camera Height

Kong

Chen

Wang

et al. 2019

Sensors

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Vision-based fall-detection methods have been previously studied but many have limitations in terms of practicality. Due to differences in rooms, users do not set the camera or sensors at the same height. However, few studies have taken this into consideration. Moreover, some fall-detection methods are lacking in terms of practicality because only standing, sitting and falling are taken into account. Hence, this study constructs a data set consisting of various daily activities and fall events and studies the effect of camera/sensor height on fall-detection accuracy. Each activity in the data set is carried out by eight participants in eight directions and taken with the depth camera at five different heights. Many related studies heavily depended on human segmentation by using Kinect SDK but this is not reliable enough. To address this issue, this study proposes Enhanced Tracking and Denoising Alex-Net (ETDA-Net) to improve tracking and denoising performance and classify fall and non-fall events. Experimental results indicate that fall-detection accuracy is affected by camera height, against which ETDA-Net is robust, outperforming traditional deep learning based fall-detection methods.

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Recent Advances and Future Directions of Assistive Technologies for Alzheimer’s Patients

Gowda

Arakeri²

2021

Emerging Research in Computing, Information, Communication and Applications

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According to Alzheimer's worldwide statistics-2019, nearly more than 50 million people are suffering from Alzheimer's or dementia related diseases around the world out of which 4.1 million in India alone. The number of peoples having Alzheimer's disease is growing faster all over the world. One out of nine people age 65 and older have this disease. Nearly half of the dementia people across the globe live in Asia. Alzheimer's Association estimated the cost of Alzheimer's and dementia related diseases at about $605 billion and it is about 1% of the gross domestic product of the world [1]. Dementia is the incapability and dependency of elderly people on caretakers. It significantly decreases the mental performance and one of the considerable types of brain disease. Therefore these patients depend more on caretakers. Its symptoms start with a progressive loss of memory, thinking, and analysis. There are different causes behind this process such as blood rushes into the brain, stroke, malnutrition or traumatic conditions. The most common types of dementia are as follows: Alzheimer's disease (AD), vascular dementia, dementia with Lewy bodies, mixed dementia, Parkinson's disease, front temporal lobar degeneration, Creutzfeldt-Jakob disease and normal pressure hydrocephalus [2].Alzheimer's disease (AD) is the most common form of brain disorder among older people aged above 60. Dementia is a disorder of the brain that seriously affects the ability of a person to carry out routine activities. Firstly, AD affects major parts of

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A classifier based approach to real-time fall detection using low-cost wearable sensors

Cited by 10 publications

References 25 publications

An Event-Triggered Machine Learning Approach for Accelerometer-Based Fall Detection

An Event-Triggered Machine Learning Approach for Accelerometer-Based Fall Detection

Robust Self-Adaptation Fall-Detection System Based on Camera Height

Recent Advances and Future Directions of Assistive Technologies for Alzheimer’s Patients

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