Title of article :
Unsupervised learning for human activity recognition using smartphone sensors
Author/Authors :
Kwon، نويسنده , , Yongjin and Kang، نويسنده , , Kyuchang and Bae، نويسنده , , Changseok، نويسنده ,
Issue Information :
روزنامه با شماره پیاپی سال 2014
Abstract :
To provide more sophisticated healthcare services, it is necessary to collect the precise information on a patient. One impressive area of study to obtain meaningful information is human activity recognition, which has proceeded through the use of supervised learning techniques in recent decades. Previous studies, however, have suffered from generating a training dataset and extending the number of activities to be recognized. In this paper, to find out a new approach that avoids these problems, we propose unsupervised learning methods for human activity recognition, with sensor data collected from smartphone sensors even when the number of activities is unknown. Experiment results show that the mixture of Gaussian exactly distinguishes those activities when the number of activities k is known, while hierarchical clustering or DBSCAN achieve above 90% accuracy by obtaining k based on Caliński–Harabasz index, or by choosing appropriate values for ɛ and MinPts when k is unknown. We believe that the results of our approach provide a way of automatically selecting an appropriate value of k at which the accuracy is maximized for activity recognition, without the generation of training datasets by hand.
Keywords :
unsupervised learning , Human activity recognition , Healthcare Services , Sensor data analysis , Smartphone sensors
Journal title :
Expert Systems with Applications
Journal title :
Expert Systems with Applications