Activity Recognition and Fall Detection in Elderly People

Dichwalkar, Riya; Oak, Shreya; Rajabally, Tania; Kalbande, Dhananjay

doi:10.1109/icccnt49239.2020.9225639

Cited by 4 publications

(3 citation statements)

References 19 publications

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“…The maximum work for fall detection using DL have been done using CNN followed by hybrid, LSTM, Auto-encoder and MLP as shown in Figure 20. These DL based Classification of papers based on the CNN modle used CNN [39,41,44,20,52,45,54,59,61,63,64,65,66,67,68,8,69,71,73,74,76,77,78,79,80,81,82,84,87,88,89,90,91,36,92,93,94,95,99,100,101,102,103,104,107,105,108,106] LSTM [117,…”

Section: Discussion On Limitations and Future Scopementioning

confidence: 99%

“…Dichwalkar (2020) et al [84] proposed a single shot detector (SSD) -MobileNet [85] model for fall detection. The frames of the video were resized to 300 x 300 and converted into blobs which were used as input to the SSD.…”

Section: Cnn Based Techniquesmentioning

confidence: 99%

“…for testing. Similar to Dichwalkar et al[84], [87] proposed a single shot human fall detection (SSHFD) system which also considers some missing information in pose data using occluded joins resilience (OJR). At first, a bounding box of human was generated from a single RGB image which was fed as input to a stacked hourglass (SH) Network to generate a 2D pose.…”

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confidence: 99%

See 2 more Smart Citations

Vision-based Human Fall Detection Systems using Deep Learning: A Review

Alam¹,

Sufian²,

Dutta³

et al. 2022

Preprint

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Human fall is one of the very critical health issues, especially for elders and disabled people living alone. The number of elder populations is increasing steadily worldwide. Therefore, human fall detection is becoming an effective technique for assistive living for those people. For assistive living, deep learning and computer vision have been used largely. In this review article, we discuss deep learning (DL)-based state-of-thearts non-intrusive (vision-based) fall detection techniques. We also present a survey on fall detection benchmark datasets. For a clear understanding, we briefly discuss different metrics which are used to evaluate the performance of the fall detection systems. This article also gives a future direction on vision-based human fall detection techniques.

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