关键词: 摔倒检测, 行为识别, 普适计算, 迁移学习, 机器学习, 不均衡学习

Abstract: The fact that it is difficult to get the fall data leads to the imbalance between normal activity and abnormal activity. The fall detection model trained on the imbalance data set has high missing alarm rate and false alarm rate, and can’t meet the actual requirement. To solve this problem, a fall detection method based on weighted extreme learning machine is proposed. This method will consider the influence of sample quantity of different kinds of activities, assign a weight value to each kind of activity, thus to partly resolve the imbalance learning problem. Tested on real activity data set, compared with the traditional method, the method presented in this paper can achieve a higher performance about 10%.

Key words: fall detection, activity recognition, pervasive computing, transfer learning；machine learning；imbalance learning