Label Disambiguation and Sequence Modeling for Identifying Human Activities from Wearable Physiological Sensors

Abstract

Wearable physiological sensors can provide a faithful record of a patient's physiological states without constant attention of caregivers. A computer program that can infer human activities from physiological recordings will be an valuable tool for physicians. In this paper we investigate to what extent current machine learning algorithms can correctly identify human activities from physiological sensors. We further identify two challenges that developers need to address. The first problem is that the labels of training data are inevitably noisy due to difficulties of annotating thousands hours of data. The second problem lies in the continuous nature of human activities, which violates the independence assumption made by many learning algorithms. We approach the first problem of noisy labeling in the multiple-label framework, and develop a conditional Markov Models to take temporal context into consideration. We evaluate the proposed methods on 12,000 hours of the physiological recordings. The results show that Support Vector Machines are effective to identify human activities from physiological signals, and efforts of disambiguating noisy labels are worthwhile.