This paper is about a fall inducement system for guiding like a real fall. Reliable fall data can be used as an essential element in developing effective fall protection devices. We can get this data if the induced fall is very realistic. The proposed system analyzes gait characteristics and determines when to fall based on the pedestrian's biometric data. To estimate the fall inducement time, an active estimation algorithm was proposed using different biometric values for each pedestrian. The proposed algorithm is designed to response actively to the ratio of gait cycle and a stance period. To verify this system, an experimental environment was implemented using a multi-rail treadmill equipped with a ground reaction force measurement device. An experiment was conducted to induce falls to pedestrians using a fall inducement system. By comparing the experimental scene to the video of the actual fall, it has been confirmed that the proposed system can induce a reliable fall.

As we become an aging society, the number of elderly patients continues to increase. Pressure sores that can easily occur in patients with trauma cause serious socio-economic problems. In general, prevention of bedsores through predicting the patient's posture is being developed. Developed method usually use artificial intelligence techniques to estimate the patient’s posture by measured pressure images in the mattress. In this method, it has a problem the reduction of estimation accuracy when posture of patient is changed. Therefore, it is necessary to use the filter of pressure images in the position transition of patient. In this paper, we propose an algorithm to predict the patient's posture, and an algorithm to reduce the ambiguity that can occur in the patient's posture transition section. By obtaining stable data through this algorithm, learning/prediction stability of the neural network can be expected, and prediction performance is improved accordingly. Through experiments, the effectiveness of the algorithm was verified.