Human Fall Detection- Multimodality Approach

• URL: http://arxiv.org/abs/2302.00224v1
• Date: Wed, 1 Feb 2023 04:05:14 GMT
• Title: Human Fall Detection- Multimodality Approach
• Authors: Xi Wang, Ramya Penta, Bhavya Sehgal, Dale Chen-Song
• Abstract summary: We use wrist sensor with acclerometer data keeping labels to binary classification, namely fall and no fall from the data set. The experimental results shows that using only wrist data as compared to multi sensor for binary classification did not impact the model prediction performance for fall detection.
• Score: 2.7215474244966296
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Falls have become more frequent in recent years, which has been harmful for senior citizens.Therefore detecting falls have become important and several data sets and machine learning model have been introduced related to fall detection. In this project report, a human fall detection method is proposed using a multi modality approach. We used the UP-FALL detection data set which is collected by dozens of volunteers using different sensors and two cameras. We use wrist sensor with acclerometer data keeping labels to binary classification, namely fall and no fall from the data set.We used fusion of camera and sensor data to increase performance. The experimental results shows that using only wrist data as compared to multi sensor for binary classification did not impact the model prediction performance for fall detection.

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