{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T02:16:37Z","timestamp":1760235397274,"version":"build-2065373602"},"reference-count":48,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2021,8,19]],"date-time":"2021-08-19T00:00:00Z","timestamp":1629331200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Many smart city and society applications such as smart health (elderly care, medical applications), smart surveillance, sports, and robotics require the recognition of user activities, an important class of problems known as human activity recognition (HAR). Several issues have hindered progress in HAR research, particularly due to the emergence of fog and edge computing, which brings many new opportunities (a low latency, dynamic and real-time decision making, etc.) but comes with its challenges. This paper focuses on addressing two important research gaps in HAR research: (i) improving the HAR prediction accuracy and (ii) managing the frequent changes in the environment and data related to user activities. To address this, we propose an HAR method based on Soft-Voting and Self-Learning (SVSL). SVSL uses two strategies. First, to enhance accuracy, it combines the capabilities of Deep Learning (DL), Generalized Linear Model (GLM), Random Forest (RF), and AdaBoost classifiers using soft-voting. Second, to classify the most challenging data instances, the SVSL method is equipped with a self-training mechanism that generates training data and retrains itself. We investigate the performance of our proposed SVSL method using two publicly available datasets on six human activities related to lying, sitting, and walking positions. The first dataset consists of 562 features and the second dataset consists of five features. The data are collected using the accelerometer and gyroscope smartphone sensors. The results show that the proposed method provides 6.26%, 1.75%, 1.51%, and 4.40% better prediction accuracy (average over the two datasets) compared to GLM, DL, RF, and AdaBoost, respectively. We also analyze and compare the class-wise performance of the SVSL methods with that of DL, GLM, RF, and AdaBoost.<\/jats:p>","DOI":"10.3390\/a14080245","type":"journal-article","created":{"date-parts":[[2021,8,19]],"date-time":"2021-08-19T09:58:06Z","timestamp":1629367086000},"page":"245","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["SVSL: A Human Activity Recognition Method Using Soft-Voting and Self-Learning"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3796-0294","authenticated-orcid":false,"given":"Aiiad","family":"Albeshri","sequence":"first","affiliation":[{"name":"Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Yigitcanlar, T., Butler, L., Windle, E., Desouza, K.C., Mehmood, R., and Corchado, J.M. (2020). Can Building \u2018Artificially Intelligent Cities\u2019 Safeguard Humanity from Natural Disasters, Pandemics, and Other Catastrophes? An Urban Scholar\u2019s Perspective. Sensors, 20.","DOI":"10.3390\/s20102988"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Mehmood, R., See, S., Katib, I., and Chlamtac, I. (2020). Smart Infrastructure and Applications: Foundations for Smarter Cities and Societies, Springer International Publishing.","DOI":"10.1007\/978-3-030-13705-2"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Alotaibi, S., Mehmood, R., Katib, I., Rana, O., and Albeshri, A. (2020). Sehaa: A Big Data Analytics Tool for Healthcare Symptoms and Diseases Detection Using Twitter, Apache Spark, and Machine Learning. Appl. Sci., 10.","DOI":"10.3390\/app10041398"},{"key":"ref_4","unstructured":"Alomari, E., Katib, I., and Mehmood, R. (2021, July 08). Iktishaf: A Big Data Road-Traffic Event Detection Tool Using Twitter and Spark Machine Learning. Available online: https:\/\/link.springer.com\/article\/10.1007%2Fs11036-020-01635-y."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1177\/2399808317751169","article-title":"Artificial intelligence and smart cities","volume":"45","author":"Batty","year":"2018","journal-title":"Environ. Plan. B Urban Anal. City Sci."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Yigitcanlar, T., Corchado, J.M., Mehmood, R., Li, R.Y.M., Mossberger, K., and Desouza, K. (2021). Responsible Urban Innovation with Local Government Artificial Intelligence (AI): A Conceptual Framework and Research Agenda. J. Open Innov. Technol. Mark. Complex., 7.","DOI":"10.3390\/joitmc7010071"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Yigitcanlar, T., Kankanamge, N., Regona, M., Ruiz Maldonado, A., Rowan, B., Ryu, A., Desouza, K.C., Corchado, J.M., Mehmood, R., and Li, R.Y.M. (2020). Artificial intelligence technologies and related urban planning and development concepts: How are they perceived and utilized in Australia?. J. Open Innov. Technol. Mark. Complex., 6.","DOI":"10.3390\/joitmc6040187"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Bragan\u00e7a, H., Colonna, J.G., Lima, W.S., and Souto, E. (2020). A smartphone lightweight method for human activity recognition based on information theory. Sensors, 20.","DOI":"10.3390\/s20071856"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Gao, Z., Liu, D., Huang, K., and Huang, Y. (2019). Context-aware human activity and smartphone position-mining with motion sensors. Remote Sens., 11.","DOI":"10.3390\/rs11212531"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"698","DOI":"10.1016\/j.procs.2019.08.100","article-title":"Human Activity Recognition: A Survey","volume":"155","author":"Jobanputra","year":"2019","journal-title":"Procedia Comput. Sci."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Ogbuabor, G., and La, R. (2018, January 26\u201328). Human Activity Recognition for Healthcare using Smartphones. Proceedings of the 2018 10th International Conference on Machine Learning and Computing, Macau, China.","DOI":"10.1145\/3195106.3195157"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2132138","DOI":"10.1155\/2020\/2132138","article-title":"Wearable Sensor-Based Human Activity Recognition Using Hybrid Deep Learning Techniques","volume":"2020","author":"Wang","year":"2020","journal-title":"Secur. Commun. Netw."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2615","DOI":"10.1109\/ACCESS.2017.2668840","article-title":"UTiLearn: A personalised ubiquitous teaching and learning system for smart societies","volume":"5","author":"Mehmood","year":"2017","journal-title":"IEEE Access"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Htike, K.K., Khalifa, O.O., Ramli, H.A.M., and Abushariah, M.A.M. (May, January 29). Human activity recognition for video surveillance using sequences of postures. Proceedings of the The Third International Conference on e-Technologies and Networks for Development (ICeND2014), Beirut, Lebanon.","DOI":"10.1109\/ICeND.2014.6991357"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Alam, F., Almaghthawi, A., Katib, I., Albeshri, A., and Mehmood, R. (2021). iResponse: An AI and IoT-Enabled Framework for Autonomous COVID-19 Pandemic Management. Sustainability, 13.","DOI":"10.3390\/su13073797"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"30509","DOI":"10.1007\/s11042-020-09004-3","article-title":"Vision-based human activity recognition: A survey","volume":"79","author":"Beddiar","year":"2020","journal-title":"Multimed. Tools Appl."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Arfat, Y., Usman, S., Mehmood, R., and Katib, I. (2020). Big data for smart infrastructure design: Opportunities and challenges. Smart Infrastructure and Applications Foundations for Smarter Cities and Societies, Springer.","DOI":"10.1007\/978-3-030-13705-2_20"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Janbi, N., Katib, I., Albeshri, A., and Mehmood, R. (2020). Distributed Artificial Intelligence-as-a-Service (DAIaaS) for Smarter IoE and 6G Environments. Sensors, 20.","DOI":"10.3390\/s20205796"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Mohammed, T., Albeshri, A., Katib, I., and Mehmood, R. (2020). UbiPriSEQ\u2014Deep reinforcement learning to manage privacy, security, energy, and QoS in 5G IoT hetnets. Appl. Sci., 10.","DOI":"10.3390\/app10207120"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1016\/j.future.2019.02.050","article-title":"Edge computing: A survey","volume":"97","author":"ZadaKhan","year":"2019","journal-title":"Futur. Gener. Comput. Syst."},{"key":"ref_21","first-page":"1","article-title":"Ensemble methods in machine learning","volume":"Volume 1857","author":"Dietterich","year":"2000","journal-title":"Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"e1249","DOI":"10.1002\/widm.1249","article-title":"Ensemble learning: A survey","volume":"8","author":"Sagi","year":"2018","journal-title":"Wiley Interdiscip. Rev. Data Min. Knowl. Discov."},{"key":"ref_23","unstructured":"Anguita, D., Ghio, A., Oneto, L., Parra, X., and Reyes-Ortiz, J.L. (2013, January 24\u201326). A Public Domain Dataset for Human Activity Recognition Using Smartphones. Proceedings of the 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges, Belgium."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"437","DOI":"10.1016\/j.procs.2016.09.068","article-title":"Analysis of Eight Data Mining Algorithms for Smarter Internet of Things (IoT)","volume":"98","author":"Alam","year":"2016","journal-title":"Procedia Comput. Sci."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Ka\u0144toch, E. (2018, January 24\u201327). Human activity recognition for physical rehabilitation using wearable sensors fusion and artificial neural networks. Proceedings of the 2017 Computing in Cardiology (CinC), Rennes, France.","DOI":"10.22489\/CinC.2017.296-332"},{"key":"ref_26","unstructured":"Mai, D., and Hoang, K. (2013, January 25\u201328). Motorbike theft detection based on object detection and human activity recognition. Proceedings of the 2013 International Conference on Control, Automation and Information Sciences (ICCAIS), Nha Trang, Vietnam."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Palaniappan, A., Bhargavi, R., and Vaidehi, V. (2012, January 19\u201321). Abnormal human activity recognition using SVM based approach. Proceedings of the International Conference on Recent Trends in Information Technology, ICRTIT 2012, Chennai, India.","DOI":"10.1109\/ICRTIT.2012.6206829"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Manosha Chathuramali, K.G., and Rodrigo, R. (2012, January 12\u201315). Faster human activity recognition with SVM. Proceedings of the International Conference on Advances in ICT for Emerging Regions, ICTer 2012, Colombo, Sri Lanka.","DOI":"10.1109\/ICTer.2012.6421415"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"012017","DOI":"10.1088\/1742-6596\/1192\/1\/012017","article-title":"Human activity recognition using support vector machine for automatic security system","volume":"1192","author":"Supriyatna","year":"2019","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_30","first-page":"140820","article-title":"Human Activity Recognition Based on the Hierarchical Feature Selection and Classification Framework","volume":"34","author":"Zheng","year":"2015","journal-title":"J. Electr. Comput. Eng."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Kerboua, A., Batouche, M., and Debbah, A. (2016, January 23\u201314). RGB-D & SVM action recognition for security improvement. Proceedings of the Mediterranean Conference on Pattern Recognition and Artificial Intelligence, Tebessa, Algeria.","DOI":"10.1145\/3038884.3038907"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.procs.2018.10.298","article-title":"Sensor based human activity recognition using adaboost ensemble classifier","volume":"140","author":"Subasi","year":"2018","journal-title":"Procedia Comput. Sci."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Uddin, M.T., and Uddiny, M.A. (2015, January 21\u201323). A guided random forest based feature selection approach for activity recognition. Proceedings of the 2015 International Conference on Electrical Engineering and Information Communication Technology (ICEEICT), Savar, Bangladesh.","DOI":"10.1109\/ICEEICT.2015.7307376"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1177\/0020294018813692","article-title":"Human activity recognition from smart watch sensor data using a hybrid of principal component analysis and random forest algorithm","volume":"52","author":"Balli","year":"2019","journal-title":"Meas. Control"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"012087","DOI":"10.1088\/1742-6596\/1655\/1\/012087","article-title":"Random Forest for Human Daily Activity Recognition","volume":"1655","author":"Nurwulan","year":"2020","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"12027","DOI":"10.1088\/1742-6596\/1456\/1\/012027","article-title":"Classification methods performance on human activity recognition","volume":"1456","author":"Bustoni","year":"2020","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Steven Eyobu, O., and Han, D.S. (2018). Feature Representation and Data Augmentation for Human Activity Classification Based on Wearable IMU Sensor Data Using a Deep LSTM Neural Network. Sensors, 18.","DOI":"10.3390\/s18092892"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Alawneh, L., Alsarhan, T., Al-Zinati, M., Al-Ayyoub, M., Jararweh, Y., and Lu, H. (2021, July 08). Enhancing Human Activity Recognition Using Deep Learning and Time Series Augmented Data. Available online: https:\/\/link.springer.com\/article\/10.1007\/s12652-020-02865-4#citeas.","DOI":"10.1007\/s12652-020-02865-4"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"68985","DOI":"10.1109\/ACCESS.2021.3078184","article-title":"iSPLInception: An Inception-ResNet Deep Learning Architecture for Human Activity Recognition","volume":"9","author":"Ronald","year":"2021","journal-title":"IEEE Access"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"3992","DOI":"10.1109\/TIM.2019.2945467","article-title":"Smartphone sensor-based human activity recognition using feature fusion and maximum full a posteriori","volume":"69","author":"Chen","year":"2020","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"D\u2019Angelo, G., and Palmieri, F. (2021, July 08). Enhancing COVID-19 Tracking Apps with Human Activity Recognition Using a Deep Convolutional Neural Network and HAR-Images. Available online: https:\/\/link.springer.com\/article\/10.1007\/s00521-021-05913-y.","DOI":"10.1007\/s00521-021-05913-y"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Khan, M.B., Zhang, Z., Li, L., Zhao, W., Hababi, M.A.M.A., Yang, X., and Abbasi, Q.H. (2020). A Systematic Review of Non-Contact Sensing for Developing a Platform to Contain COVID-19. Micromachines, 11.","DOI":"10.3390\/mi11100912"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"133190","DOI":"10.1109\/ACCESS.2019.2940729","article-title":"Smartphone and Smartwatch-Based Biometrics Using Activities of Daily Living","volume":"7","author":"Weiss","year":"2019","journal-title":"IEEE Access"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1016\/j.ipm.2009.03.002","article-title":"A systematic analysis of performance measures for classification tasks","volume":"45","author":"Sokolova","year":"2009","journal-title":"Inf. Process. Manag."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1007\/978-981-13-2354-6_27","article-title":"A brief survey on random forest ensembles in classification model","volume":"Volume 56","author":"Shaik","year":"2019","journal-title":"Lecture Notes in Networks and Systems"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"370","DOI":"10.2307\/2344614","article-title":"Generalized Linear Models","volume":"135","author":"Nelder","year":"1972","journal-title":"J. R. Stat. Soc. Ser. A"},{"key":"ref_47","unstructured":"Terry-Jack, M. (2021, June 15). Deep Learning: Feed Forward Neural Networks (FFNNs). Medium.com. Available online: https:\/\/medium.com\/@b.terryjack\/introduction-to-deep-learning-feed-forward-neural-networks-ffnns-a-k-a-c688d83a309d."},{"key":"ref_48","unstructured":"Candel, A., Le Dell, E., Parmar, V., and Arora, A. (2018). Deep Learning With H2O., H2O.ai Inc."}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/14\/8\/245\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:47:08Z","timestamp":1760165228000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/14\/8\/245"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,19]]},"references-count":48,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2021,8]]}},"alternative-id":["a14080245"],"URL":"https:\/\/doi.org\/10.3390\/a14080245","relation":{},"ISSN":["1999-4893"],"issn-type":[{"type":"electronic","value":"1999-4893"}],"subject":[],"published":{"date-parts":[[2021,8,19]]}}}