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Real-Time Privacy-Preserving Fall Risk Assessment with a Single Body-Worn Tracking Camera

DOI:
10.60864/6q8f-sr03
Citation Author(s):
Faranguisse Kakhi Sadrieh, Yi-Ting Shen, Giovanni Oppizzi, Li-Qun Zhang, Yang Tao
Submitted by:
Chiao-Yi Wang
Last updated:
6 June 2024 - 10:27am
Document Type:
Poster
 

Falls are a major cause of injury among elderly adults. In addition, recent research indicates that elderly adults often fall because of the same individual biomechanical causes. Therefore, real-time individual fall risk assessment is crucial for designing a more effective and personalized fall reduction training program. However, existing methods for fall risk assessment either raise privacy concerns or require multiple wearable sensors. In this paper, we propose EgoFall, a real-time privacy-preserving fall risk assessment system using a commercial tracking camera. EgoFall utilizes a chest-mounted tracking camera and a carefully designed data pre-processing pipeline to acquire the ego-body motion data of the subject. The ego-body motion data is then fed to a lightweight CNNTransformer model for fall risk assessment. We demonstrate that EgoFall not only outperforms the baseline methods but also has very low computational complexity, which is highly suitable for real-time processing.

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