Evaluating the specifications of built-in accelerometers in smartphones on fall detection performance

Chen, Kuang-Hsuan; Hsu, Yu-Wei; Yang, Jing-Jung; Jaw, Fu‐Shan

doi:10.1080/10739149.2017.1363054

Cited by 19 publications

(12 citation statements)

References 23 publications

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“…Bourke et al (2007) also found that accelerometers are regarded as the most popular sensors for fall detection mainly due to its affordable cost, easy installation and relatively good performance. Although smartphones have gained attention for studying falls, the underlying sensors of systems using them are still accelerometers and gyroscopes (Shi et al, 2016;Islam et al, 2017;Medrano et al, 2017;Chen et al, 2018). Users are more likely to carry smartphones all day rather than extra wearable devices, so smartphones are useful for eventual real-world deployments (Zhang et al, 2006;Dai et al, 2010).…”

Section: Individual Wearable Sensorsmentioning

confidence: 99%

“…In El-Bendary et al ( 2013 ), the authors group the types of falls in three basic categories, namely forward, lateral , and backward . Putra et al ( 2017 ) divided falls into a broader set of categories, namely forward, backward, left-side, right-side, blinded-forward , and blinded-backward , and in the study by Chen et al ( 2018 ) falls are grouped in more specific categories including fall lateral left lie on the floor, fall lateral left and sit up from floor, fall lateral right and lie on the floor, fall lateral and left sit up from the floor, fall forward and lie on the floor , and fall backward and lie on the floor .…”

Section: Types Of Falls and Previous Reviews On Elderly Fall Detecmentioning

confidence: 99%

“…Although smartphones have gained attention for studying falls, the underlying sensors of systems using them are still accelerometers and gyroscopes (Shi et al, 2016 ; Islam et al, 2017 ; Medrano et al, 2017 ; Chen et al, 2018 ). Users are more likely to carry smartphones all day rather than extra wearable devices, so smartphones are useful for eventual real-world deployments (Zhang et al, 2006 ; Dai et al, 2010 ).…”

Section: Fall Detection Using Individual Sensorsmentioning

confidence: 99%

“…The extraction of handcrafted features requires domain expertise and are, therefore, limited to the knowledge of the domain experts. Though such a limitation is imposed, literature shows that traditional machine learning, based on support vector machines, hidden Markov models, and decision trees are still very active in the field of fall detection that uses individual wearable non-visual or ambient sensors (e.g., accelerometer) (Wang et al, 2017a , b ; Chen et al, 2018 ; Saleh and Jeannès, 2019 ; Wu et al, 2019 ). For visual sensors the trend has been moving toward deep learning for convolutional neural networks (CNN) (Adhikari et al, 2017 ; Kong et al, 2019 ; Han et al, 2020 ), or LSTM (Shojaei-Hashemi et al, 2018 ).…”

Section: Fall Detection Using Individual Sensorsmentioning

confidence: 99%

“…Data transmission for fall detection using sensor networks can be done in different ways. In particular, Bluetooth (Pierleoni et al, 2015 ; Yang et al, 2016 ), Wi-Fi, ZigBee (Hsieh et al, 2014 ), cellular network using smart phones (Chen et al, 2018 ) and smart watches (Kao et al, 2017 ), as well as wired connection have all been explored. In studies that used wearable devices, most of them applied wireless methods, such as Bluetooth, which allowed the subject to move unrestricted.…”

Section: Sensor Fusion By Sensor Networkmentioning

confidence: 99%

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Elderly Fall Detection Systems: A Literature Survey

2020

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Falling is among the most damaging event elderly people may experience. With the ever-growing aging population, there is an urgent need for the development of fall detection systems. Thanks to the rapid development of sensor networks and the Internet of Things (IoT), human-computer interaction using sensor fusion has been regarded as an effective method to address the problem of fall detection. In this paper, we provide a literature survey of work conducted on elderly fall detection using sensor networks and IoT. Although there are various existing studies which focus on the fall detection with individual sensors, such as wearable ones and depth cameras, the performance of these systems are still not satisfying as they suffer mostly from high false alarms. Literature shows that fusing the signals of different sensors could result in higher accuracy and lower false alarms, while improving the robustness of such systems. We approach this survey from different perspectives, including data collection, data transmission, sensor fusion, data analysis, security, and privacy. We also review the benchmark data sets available that have been used to quantify the performance of the proposed methods. The survey is meant to provide researchers in the field of elderly fall detection using sensor networks with a summary of progress achieved up to date and to identify areas where further effort would be beneficial.

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