Abstract
Summary
Smartphones are equipped with precise hardware sensors including accelerometer, gyroscope, and magnetometer. These devices provide real‐time semantic data that can be used to recognize daily life physical activities for personalized smart health assessment. Existing studies focus on the recognition of simple physical activities but they lacked in providing accurate recognition of physical activities having complex interclass variations. Therefore, this research focuses on the accurate recognition of physical activities having complex interclass variations. We propose a two‐layered approach called PARCIVthat first clusters similar activities based on semantic data and then recognize them using a machine learning classifier. Our two‐layered approach first bounds the highly indistinguishable activities in clusters to avoid misclassification with other distinguishable activities and thereafter recognize them on a fine‐grained level within each cluster. To evaluate our approach, we make an android application that collects labeled data by using smartphone sensors from 10 participants, while performing activities. PARCIV recognizes distinguishable as well as indistinguishable activities with high accuracy of 99% on the self‐collected dataset. Furthermore, PARCIV achieve 95% accuracy on the publicly available dataset used by state‐of‐the‐art studies. PARCIVoutperforms various state‐of‐the‐art studies by 8%‐17% for simple activities as well as complex activities.