一种基于Attention-TCN的跌倒预测算法OA

With the intensifying global aging population,fall events among the elderly are happening increasingly,posing a serious threat to their physical and mental health.Although many studies have been devoted to fall detection and prediction,most of them primarily analyze the temporal features of the time-series data acquired by inertial sensors,while the study of spatial features is less.In order to predict fall events among the elderly more effectively and activate protective devices such as airbags promptly,a new fall prediction algo-rithm based on Attention-TCN is proposed,which combines the attention mechanism and temporal convolutional networks(TCN),capa-ble of extracting global spatial-temporal features from fall time-series data and autonomously fusing the features through the adaptive fea-ture fusion(AFF)method to provide accurate fall classification.Meanwhile,a downsampling technique is used to reduce the model size and inference time while improving the model prediction performance.In offline experiments conducted on the SisFall public fall dataset on PC,the method achieves an accuracy of 98.67%,a sensitivity of 98.89%,and a specificity of 98.52%,with an average fall prediction leading time of 221.16 ms,a model inference time of 0.19±0.05 ms,and a model size of 673 KB,which confirms its high efficiency and practicality in predicting falls among the elderly.