Detecting Falls and Slips in Wheelchair Users Using Low-Resolution Thermal Imaging

The demand for monitoring in private rooms is increasing as the aging population grows, and thermal cameras are expected for privacy-preserving monitoring to deal with accidents in private rooms. This paper focuses specifically on wheelchair users who are more prone to falls. Falls can be classified into high-risk falls that cause injury and low-risk slips where users gradually slide out of the wheelchair. Treating both equally risks generating excessive warnings and missing true emergencies. In this study, we used low-resolution thermal images to detect and distinguish high-risk falls and low-risk slips. However, video analysis requires clear images of the limbs and other details of the body, but thermal cameras cannot consistently capture such detailed features. To overcome this, we developed a system called “Fall Detection using CNN and Torso Features (FDCTF).” While CNN-extracted features require clear textures and shapes, torso angle changes are less dependent on resolution and can effectively differentiate falls from slips based on the speed and duration of changes. By combining CNN and torso features within an LSTM network, our system achieved an F1 score of 1.0 for fall detection, which represents a 0.09 increase compared to using CNN features only, and distinguished falls from slips with over 80% accuracy.