Few-shot Fall Detection using Shallow Siamese Network

Bakshi, Satyake; Rajan, Sreeraman

doi:10.1109/memea52024.2021.9478605

Cited by 3 publications

(1 citation statement)

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“…However, the easiest yet a sufficient way for human activity recognition would be the raw key points extracted from the human subject. For instance, studies from [10], [11], [13], [17], and [93] applied OpenPose and PoseNet, the human pose estimators, to obtain the key points from a human subject and feed the features into the proposed models for senior's fall detection. There are strengths and weaknesses for both sensor-based input and vision-based input.…”

Section: B Vision-based Inputmentioning

confidence: 99%

A Review of Abnormal Behavior Detection in Activities of Daily Living

et al. 2023

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Abnormal behavior detection (ABD) systems are built to automatically identify and recognize abnormal behavior from various input data types, such as sensor-based and vision-based input. As much as the attention received for ABD systems, the number of studies on ABD in activities of daily living (ADL) is limited. Owing to the increasing rate of elderly accidents in the home compound, ABD in ADL research should be given as much attention to preventing accidents by sending out signals when abnormal behavior such as falling is detected. In this study, we compare and contrast the formation of the ABD system in ADL from input data types (sensor-based input and vision-based input) to modeling techniques (conventional and deep learning approaches). We scrutinize the public datasets available and provide solutions for one of the significant issues: the lack of datasets in ABD in ADL. This work aims to guide new research to understand the field of ABD in ADL better and serve as a reference for future study of better Ambient Assisted Living with the growing smart home trend. INDEX TERMS abnormal behavior detection, activities of daily living

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Section: B Vision-based Inputmentioning

confidence: 99%