GA-SVM applied to the fall detection system

Kao, Hsi-Chun; Hung, Jui-Chung; Huang, Chih-Pang

doi:10.1109/icasi.2017.7988446

Cited by 10 publications

(5 citation statements)

References 8 publications

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“…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%

“…Smartwatch is another commercial solution for fall detection. Accelerometers embedded in smartwatches have been studied to detect falls (Kao et al, 2017 ; Wu et al, 2019 ). Moreover, Apple Watch Series 4 and later versions are equipped with the fall detection function, and it can help the consumer to connect to the emergency service.…”

Section: Projects and Applications Around Fall Detectionmentioning

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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Section: Sensor Fusion By Sensor Networkmentioning

confidence: 99%

Section: Projects and Applications Around Fall Detectionmentioning

confidence: 99%

Elderly Fall Detection Systems: A Literature Survey

2020

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“…k-Means clustering performed poorly with 60% classification accuracy and 582 seconds model building time. Kao et al [49] use an ensemble of spectrum analysis, with GA-SVM, SVM, and C4.5 classifiers. e sensor readings were from 3-axis accelerometers.…”

Section: Tsinganos and Skodrasmentioning

confidence: 99%

A Survey on Recent Advances in Wearable Fall Detection Systems

Ramachandran

Karuppiah

2020

BioMed Research International

108

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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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“…In this study, the SVM classifier method is used to obtain the suspected fall signal data from the first input first output (FIFO), and six of the eigenvalues are extracted. By matching with the fall detection model, (18) an accurate second judgment result can be obtained.…”

Section: Fall Detection Algorithm Based On Quadratic Decisionmentioning

confidence: 99%

Human Fall Detection Algorithm Design Based on Sensor Fusion and Multi-threshold Comprehensive Judgment

Qu¹,

Wu²,

Li³

et al. 2020

Sensors and Materials

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The use of a single method of acceleration threshold discrimination cannot fully characterize the change in human fall behavior, which can easily result in misjudgment. In this paper, we propose a human fall detection algorithm that combines human posture, support vector machine (SVM), and quadratic threshold decision. Firstly, a large number of human posture data are collected through a six-axis inertial measurement module (MPU6050). A fall detection model is established through filtering preprocessing, eigenvalue extraction, classification, and SVM training. Secondly, a first-level threshold determination is performed through a wearable wristband device. When a suspected fall occurs, six eigenvalues will be captured and uploaded to a cloud platform to trigger second-level SVM fall judgments. By matching the eigenvalues with the fall detection model, it can be determined accurately whether a fall has taken place. The experimental results show that the fall detection has a recognition rate of 92.2%, a false rate of 3.593%, and missing rate of 2.187%, which can better distinguish other nonfall actions.

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GA-SVM applied to the fall detection system

Cited by 10 publications

References 8 publications

Elderly Fall Detection Systems: A Literature Survey

Elderly Fall Detection Systems: A Literature Survey

A Survey on Recent Advances in Wearable Fall Detection Systems

Human Fall Detection Algorithm Design Based on Sensor Fusion and Multi-threshold Comprehensive Judgment

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