SARM: Salah Activities Recognition Model Based on Smartphone

Electronics

MuhammadAdil Abid¹  Khalid Iqbal²  Rashid Ahmad²  Naeem Iqbal²  Lansheng Han³  Nafees Ahmad³ 

[1] Department of Computer Science and Technology, Shandong University, Jimo, Qingdao 266237, China;Department of Computer Science, COMSATS University Islamabad, Attock Campus, Attock 43600, Pakistan;School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China;

关键词: Salah activities recognition;    posture recognition;    accelerometer sensor;    human activity recognition;    classification;   

DOI  :  10.3390/electronics8080881

来源: DOAJ

【 摘 要 】

Alzheimer’s is a chronic neurodegenerative disease that frequently occurs in many people today. It has a major effect on the routine activities of affected people. Previous advancement in smartphone sensors technology enables us to help people suffering from Alzheimer’s. For people in the Muslim community, where it is mandatory to offer prayers five times a day, it may mean that they are struggling in their daily life prayers due to Alzheimer’s or lack of concentration. To deal with such a problem, automated mobile sensor-based activity recognition applications can be supportive to design accurate and precise solutions with an objective to direct the Namazi (worshipper). In this paper, a Salah activities recognition model (SARM) using a mobile sensor is proposed with the aim to recognize specific activities, such as Al-Qayam (standing), Ruku (standing to bowing), and Sujud (standing to prostration). This model entails the collection of data, selection and placement of sensor, data preprocessing, segmentation, feature extraction, and classification. The proposed model will provide a stepping edge to develop an application for observing prayer. For these activities’ recognition, data sets were collected from ten subjects, and six different features sets were used to get improved results. Extensive experiments were performed to test and validate the model features to train random forest (RF), K-nearest neighbor (KNN), naive Bayes (NB), and decision tree (DT). The predicted average accuracy of RF, KNN, NB, and DT was 97%, 94%, 71.6%, and 95% respectively.

【 授权许可】

Unknown