Impact of Sampling Rate on Wearable-Based Fall Detection Systems Based on Machine Learning Models

Liu, Kai-Chun; Hsieh, Chia-Yeh; Hsu, Steen J.; Chan, Chia-Tai

doi:10.1109/jsen.2018.2872835

Cited by 64 publications

(53 citation statements)

References 39 publications

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“…Abuhania et al [ 28 ] used a chest-strapped accelerometer and the acceleration sum vector as a feature, and reported a 90% fall detection accuracy using a k-Nearest Neighbour (kNN) classifier. A waist-strapped accelerometer was used by Liu et al [ 29 ], who extracted multiple features from the accelerometer signals. A 97.60% classification accuracy for fall detection was reported using SVM with RBF kernel.…”

Section: Introductionmentioning

confidence: 99%

Triaxial Accelerometer-Based Falls and Activities of Daily Life Detection Using Machine Learning

Althobaiti

Katsigiannis

Ramzan

2020

Sensors

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The detection of activities of daily living (ADL) and the detection of falls is of utmost importance for addressing the issue of serious injuries and death as a consequence of elderly people falling. Wearable sensors can provide a viable solution for monitoring people in danger of falls with minimal external involvement from health or care home workers. In this work, we recorded accelerometer data from 35 healthy individuals performing various ADLs, as well as falls. Spatial and frequency domain features were extracted and used for the training of machine learning models with the aim of distinguishing between fall and no fall events, as well as between falls and other ADLs. Supervised classification experiments demonstrated the efficiency of the proposed approach, achieving an F1-score of 98.41% for distinguishing between fall and no fall events, and an F1-score of 88.11% for distinguishing between various ADLs, including falls. Furthermore, the created dataset, named “ShimFall&ADL” will be publicly released to facilitate further research on the field.

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Section: Introductionmentioning

confidence: 99%

Triaxial Accelerometer-Based Falls and Activities of Daily Life Detection Using Machine Learning

Althobaiti

Katsigiannis

Ramzan

2020

Sensors

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“…It is reported that the fall detection accuracy was highest for kNN, while the accuracy for recognizing different activities was highest for random forest. Yet another research [52] attempts to find a correlation between sampling rate and performance accuracy of machine learning models. In this paper, the authors compare the performance of SVM, Naive-Bayes, kNN, and decision trees with various sampling rates of sensors.…”

Section: Tsinganos and Skodrasmentioning

confidence: 99%

A Survey on Recent Advances in Wearable Fall Detection Systems

Ramachandran

Karuppiah

2020

BioMed Research International

116

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With advances in medicine and healthcare systems, the average life expectancy of human beings has increased to more than 80 yrs. As a result, the demographic old-age dependency ratio (people aged 65 or above relative to those aged 15–64) is expected to increase, by 2060, from ∼28% to ∼50% in the European Union and from ∼33% to ∼45% in Asia (Ageing Report European Economy, 2015). Therefore, the percentage of people who need additional care is also expected to increase. For instance, per studies conducted by the National Program for Health Care of the Elderly (NPHCE), elderly population in India will increase to 12% of the national population by 2025 with 8%–10% requiring utmost care. Geriatric healthcare has gained a lot of prominence in recent years, with specific focus on fall detection systems (FDSs) because of their impact on public lives. According to a World Health Organization report, the frequency of falls increases with increase in age and frailty. Older people living in nursing homes fall more often than those living in the community and 40% of them experience recurrent falls (World Health Organization, 2007). Machine learning (ML) has found its application in geriatric healthcare systems, especially in FDSs. In this paper, we examine the requirements of a typical FDS. Then we present a survey of the recent work in the area of fall detection systems, with focus on the application of machine learning. We also analyze the challenges in FDS systems based on the literature survey.

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“…To guarantee a reliable evaluation, a large public dataset known as the SisFall dataset is used in this work [25]. This dataset has been used in previous work for its diversity and integrity [26]. The dataset was recorded with a self-developed embedded device composed of a Kinets MKL25Z128VLK4 microcontroller (NPX, Austin, TX, USA), an Analog Devices (Norwood, MA, USA) ADXL345 accelerometer, a Freescale MMA8451Q accelerometer, an ITG3200 gyroscope, and a 1000 mA/h generic battery.…”

Section: Dataset Descriptionmentioning

confidence: 99%

“…Since it has been found that there is no significant gain for having sampling frequency higher than 25 Hz in fall detection [26], the original acceleration measurements are first downsampled to 25 Hz. In data downsampling, original acceleration measurements are decimated by an integer factor instead of resampling sensing data, where artifacts and distortion may occur.…”

Section: Dataset Descriptionmentioning

confidence: 99%

See 1 more Smart Citation

Elderly Fall Detection with an Accelerometer Using Lightweight Neural Networks

Wang

et al. 2019

Electronics

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Falls have been one of the main threats to people’s health, especially for the elderly. Detecting falls in time can prevent the long lying time, which is extremely fatal. This paper intends to show the efficacy of detecting falls using a wearable accelerometer. In the past decade, the fall detection problem has been extensively studied. However, since the hardware resources of wearable devices are limited, designing highly accurate embeddable models with feasible computational cost remains an open research problem. In this paper, different types of shallow and lightweight neural networks, including supervised and unsupervised models are explored to improve the fall detection results. Experiment results on a large open dataset show that the lightweight neural networks proposed have obtained much better results than machine learning methods used in previous work. Moreover, the storage and computation requirements of these lightweight models are only a few hundredths of deep neural networks in literature. In tested lightweight neural networks, the best one is proved to be the supervised convolutional neural network (CNN) that can achieve an accuracy beyond 99.9% with only 441 parameters. Its storage and computation requirements are only 1.2 KB and 0.008 MFLOPs, which make it more suitable to be implemented in wearable devices with restricted memory size and computation power.

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Impact of Sampling Rate on Wearable-Based Fall Detection Systems Based on Machine Learning Models

Cited by 64 publications

References 39 publications

Triaxial Accelerometer-Based Falls and Activities of Daily Life Detection Using Machine Learning

Triaxial Accelerometer-Based Falls and Activities of Daily Life Detection Using Machine Learning

A Survey on Recent Advances in Wearable Fall Detection Systems

Elderly Fall Detection with an Accelerometer Using Lightweight Neural Networks

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