Abstract:

In a nonmedical biometric monitoring system,the monitoring parameters are preceded with machine learning for precision promotion of diagnosis and prediction.Considering the problems of insufficient information mining and low prediction accuracy in multi task time series,both supervised and unsupervised machine learning techniques were applied to predict the physical condition of the remote health care.These techniques were K-means for clustering the similar group of data and MTLS-SVM model for training and testing historical data to perform a trend prediction.In order to evaluate the effectiveness of the method,the proposed method was compared with MTLS-SVM method.The experimental results show that the proposed method has higher prediction accuracy.

Key words: physiological parameter, time series prediction, K-means clustering, multi-task learning