ISFDNet: Using Low-Resolution Infrared Array Sensors to Detect Elderly Fall in Home Elevator Scenarios

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Abstract
Falls are a significant cause of injury in older people, and in the home elevator environment, the consequences of a fall can be more severe due to the confined and narrow space. This paper focuses on the home elevator scenario. First, we construct a fall detection dataset based on infrared array sensors, considering non-human factors such as pets in a home environment. Secondly, we propose a background filtering method based on Otsu’s method to eliminate the interference of non-human factors in the background. In addition, we introduce a new and more generalized single-frame fall detection model, ISFDNet, which shows higher fall detection precision than traditional models and achieves a precision rate of 96.64%. Finally, we adopt a single-frame detection approach combined with a sliding window-based fall detection event triggering mechanism, which ensures fall detection accuracy, reduces memory requirements, and improves inference speed; the method obtained a 100% precision rate of fall event detection.
Funding Information
  • Zhejiang Provincial Natural Science Foundation of China (LTGN24F020003)

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