The impaired behaviors of people with special needs bring heavy psychological pressure and economic burden to individuals， families and the whole society. This paper aims to explore the possibility of sensing the impaired behaviors of people with special needs by combining advanced AI techniques with wearable device embedded with 9-axis motion sensors to prevent accidents and reduce nursing costs. Firstly， the self-collected data are analyzed and preprocessed to extract the features of 108 dimensions. Secondly， in the process of feature selection， the feature is divided into three feature subsets by using two methods of priori analysis and random forest respectively. The purpose is to reduce the time cost on the premise of ensuring the recognition accuracy. Finally， two validation methods and six classifiers are used for evaluation. Experimental results show that multi-sensor data fusion can greatly improve the recognition rate of the classifier and the feature selection can ensure the recognition rate of the classifier under the premise of low performance loss. Feature subset 3 is more suitable for representing impaired behaviors of people with special needs. The light gradient boosting machine （LightGBM） has an obvious performance advantage， and the average recognition rate of 10-fold cross-verification can reach 93%， which turned out to be more feasible and practical considering both computation cost and classification accuracy.