PARCIV: Recognizing physical activities having complex interclass variations using semantic data of smartphone. (27th May 2020)

• Record Type: Journal Article
• Authors: Usman Sarwar, Muhammad; Rehman Javed, Abdul; Kulsoom, Farzana; Khan, Suleman; Tariq, Usman; Kashif Bashir, Ali
• Other Names: Baker Thar guestEditor.; Al‐Jumeily Dhiya guestEditor.; Maamar Zakaria guestEditor.; Tari Zahir guestEditor.
• Is Part Of: Software, practice & experience. Volume 51:Number 3(2021)
• Page Start: 532
• Page End: 549
• Publication Date: 2020-05-27
• Subjects: accelerometer -- complex confused activities -- healthcare -- interclass variations -- semantic data -- smartphone sensors; Computer software -- Periodicals; Computer programming -- Periodicals; Computer programs -- Periodicals; 005.3
• DOI: 10.1002/spe.2846
• Languages: English
• ISSNs: 0038-0644

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 PARCIV that 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. PARCIV outperforms various state‐of‐the‐art studies by 8%‐17% for simple activities as well as complex activities.