Automatic Fall Monitoring: A Review

Pannurat, Natthapon; Thiemjarus, Surapa; Nantajeewarawat, Ekawit

doi:10.3390/s140712900

Cited by 223 publications

(142 citation statements)

References 120 publications

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“…From Figure 3, features C 2 and C 8 obtained the higher accuracy once the filter was applied (95.0% ± 1.2% and 96.1% ± 0.75%, respectively). This result is consistent with the literature ( [9], Table 1). In this case, C 2 would be preferred as it is static, i.e., it requires less memory and computational effort to be computed.…”

Section: Effect Of Filtering As the Preprocessing Stagesupporting

confidence: 83%

SisFall: A Fall and Movement Dataset

Sucerquia

López

Vargas-Bonilla

2016

Preprint

112

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Abstract:Research on fall and movement detection with wearable devices has witnessed promising growth. However, there are few publicly available datasets, all recorded with smartphones, which are insufficient for testing new proposals due to their absence of objective population, lack of performed activities, and limited information. Here, we present a dataset of falls and activities of daily living (ADLs) acquired with a self-developed device composed of two types of accelerometer and one gyroscope. It consists of 19 ADLs and 15 fall types performed by 23 young adults, 15 ADL types performed by 14 healthy and independent participants over 62 years old, and data from one participant of 60 years old that performed all ADLs and falls. These activities were selected based on a survey and a literature analysis. We test the dataset with widely used feature extraction and a simple to implement threshold based classification, achieving up to 96% of accuracy in fall detection. An individual activity analysis demonstrates that most errors coincide in a few number of activities where new approaches could be focused. Finally, validation tests with elderly people significantly reduced the fall detection performance of the tested features. This validates findings of other authors and encourages developing new strategies with this new dataset as the benchmark.

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Section: Effect Of Filtering As the Preprocessing Stagesupporting

confidence: 83%

SisFall: A Fall and Movement Dataset

Sucerquia

López

Vargas-Bonilla

2016

Preprint

112

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“…The majority of these approaches can be divided in two main types: threshold-based and machine learning (or data mining) [12]. Both types are based on features extracted from the recorded signals.…”

Section: Related Work Based On Inertial Sensorsmentioning

confidence: 99%

A Smart Device Enabled System for Autonomous Fall Detection and Alert

Jian

Wang

2016

International Journal of Distributed Sensor Networks

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The activity model based on 3D acceleration and gyroscope is created in this paper, and the difference between the activities of daily living (ADLs) and falls is analyzed at first. Meanwhile, the NN algorithm and sliding window are introduced to develop a smart device enabled system for fall detection and alert, which is composed of a wearable motion sensor board and a smart phone. The motion sensor board integrated with triaxial accelerometer, gyroscope, and Bluetooth is attached to a custom vest worn by the elderly to capture the reluctant acceleration and angular velocity of ADLs in real time. The stream data via Bluetooth is then sent to a smart phone, which runs a program based on the NN algorithm and sliding window to analyze the stream data and detect falls in the background. At last, the experiment shows that the system identifies simulated falls from ADLs with a high accuracy of 97.7%, while sensitivity and specificity are 94% and 99%, respectively. Besides, the smart phone can issue an alarm and notify caregivers to provide timely and accurate help for the elderly, as soon as a fall is detected.

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“…Finally Then the degree of the similarity of the signal window and the mother wavelet is computed, here a threshold of the degree is set to predict the fall. Except the threshold-based method Most methods used in literature are various features extracted from the data window with machine learning [8][9]. In the paper of Ryan M.Gibson et.…”

Section: Introductionmentioning

confidence: 99%