{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T19:36:44Z","timestamp":1726083404389},"reference-count":52,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2020,4,14]],"date-time":"2020-04-14T00:00:00Z","timestamp":1586822400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"Recognizing human physical activities from streaming smartphone sensor readings is essential for the successful realization of a smart environment. Physical activity recognition is one of the active research topics to provide users the adaptive services using smart devices. Existing physical activity recognition methods lack in providing fast and accurate recognition of activities. This paper proposes an approach to recognize physical activities using only2-axes of the smartphone accelerometer sensor. It also investigates the effectiveness and contribution of each axis of the accelerometer in the recognition of physical activities. To implement our approach, data of daily life activities are collected labeled using the accelerometer from 12 participants. Furthermore, three machine learning classifiers are implemented to train the model on the collected dataset and in predicting the activities. Our proposed approach provides more promising results compared to the existing techniques and presents a strong rationale behind the effectiveness and contribution of each axis of an accelerometer for activity recognition. To ensure the reliability of the model, we evaluate the proposed approach and observations on standard publicly available dataset WISDM also and provide a comparative analysis with state-of-the-art studies. The proposed approach achieved 93% weighted accuracy with Multilayer Perceptron (MLP) classifier, which is almost 13% higher than the existing methods.<\/jats:p>","DOI":"10.3390\/s20082216","type":"journal-article","created":{"date-parts":[[2020,4,15]],"date-time":"2020-04-15T08:01:46Z","timestamp":1586937706000},"page":"2216","source":"Crossref","is-referenced-by-count":88,"title":["Analyzing the Effectiveness and Contribution of Each Axis of Tri-Axial Accelerometer Sensor for Accurate Activity Recognition"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-0570-1813","authenticated-orcid":false,"given":"Abdul Rehman","family":"Javed","sequence":"first","affiliation":[{"name":"National Center for Cyber Security, Air University, Islamabad 44000, Pakistan"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-7494-3413","authenticated-orcid":false,"given":"Muhammad Usman","family":"Sarwar","sequence":"additional","affiliation":[{"name":"Department of Computer Sciences, National University of Computer and Emerging Sciences, Islamabad 44000, Pakistan"}]},{"given":"Suleman","family":"Khan","sequence":"additional","affiliation":[{"name":"National Center for Cyber Security, Air University, Islamabad 44000, Pakistan"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-4350-3911","authenticated-orcid":false,"given":"Celestine","family":"Iwendi","sequence":"additional","affiliation":[{"name":"Department of Electronics BCC of Central South University of Forestry and Technology, Changsha 410004, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-0878-4615","authenticated-orcid":false,"given":"Mohit","family":"Mittal","sequence":"additional","affiliation":[{"name":"Department of Information Science and Engineering, Kyoto Sangyo University, Kyoto 603-8555, Japan"}]},{"given":"Neeraj","family":"Kumar","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering, Thapar Institute of Engineering and Technology, Patiala, Punjab 147001, India"}]}],"member":"1968","published-online":{"date-parts":[[2020,4,14]]},"reference":[{"key":"ref_1","first-page":"l1495","article-title":"Physical inactivity, cardiometabolic disease, and risk of dementia: An individual-participant meta-analysis","volume":"365","author":"Pentti","year":"2019","journal-title":"BMJ"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Bao, L., and Intille, S.S. 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