{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T18:11:39Z","timestamp":1771611099041,"version":"3.50.1"},"reference-count":53,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2018,11,1]],"date-time":"2018-11-01T00:00:00Z","timestamp":1541030400000},"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":"<jats:p>Recently, modern smartphones equipped with a variety of embedded-sensors, such as accelerometers and gyroscopes, have been used as an alternative platform for human activity recognition (HAR), since they are cost-effective, unobtrusive and they facilitate real-time applications. However, the majority of the related works have proposed a position-dependent HAR, i.e., the target subject has to fix the smartphone in a pre-defined position. Few studies have tackled the problem of position-independent HAR. They have tackled the problem either using handcrafted features that are less influenced by the position of the smartphone or by building a position-aware HAR. The performance of these studies still needs more improvement to produce a reliable smartphone-based HAR. Thus, in this paper, we propose a deep convolution neural network model that provides a robust position-independent HAR system. We build and evaluate the performance of the proposed model using the RealWorld HAR public dataset. We find that our deep learning proposed model increases the overall performance compared to the state-of-the-art traditional machine learning method from 84% to 88% for position-independent HAR. In addition, the position detection performance of our model improves superiorly from 89% to 98%. Finally, the recognition time of the proposed model is evaluated in order to validate the applicability of the model for real-time applications.<\/jats:p>","DOI":"10.3390\/s18113726","type":"journal-article","created":{"date-parts":[[2018,11,1]],"date-time":"2018-11-01T11:31:47Z","timestamp":1541071907000},"page":"3726","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":59,"title":["A Robust Deep Learning Approach for Position-Independent Smartphone-Based Human Activity Recognition"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9149-6384","authenticated-orcid":false,"given":"Bandar","family":"Almaslukh","sequence":"first","affiliation":[{"name":"Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abdel Monim","family":"Artoli","sequence":"additional","affiliation":[{"name":"Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jalal","family":"Al-Muhtadi","sequence":"additional","affiliation":[{"name":"Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,11,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Ranasinghe, S., Al Machot, F., and Mayr, H.C. (2016). A review on applications of activity recognition systems with regard to performance and evaluation. Int. J. Distrib. Sens. Netw., 12.","DOI":"10.1177\/1550147716665520"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1109\/MCOM.2015.7010512","article-title":"A smart communication architecture for ambient assisted living","volume":"53","author":"Lloret","year":"2015","journal-title":"IEEE Commun. Mag."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s12652-015-0294-7","article-title":"Ambient and smartphone sensor assisted ADL recognition in multi-inhabitant smart environments","volume":"7","author":"Roy","year":"2016","journal-title":"J. Ambient Intell. Humaniz. Comput."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"527","DOI":"10.1109\/TKDE.2010.148","article-title":"Discovering activities to recognize and track in a smart environment","volume":"23","author":"Rashidi","year":"2011","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1662","DOI":"10.1016\/j.eswa.2012.09.004","article-title":"Elderly activities recognition and classification for applications in assisted living","volume":"40","author":"Chernbumroong","year":"2013","journal-title":"Expert Syst. Appl."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Li, Q., Stankovic, J.A., Hanson, M.A., Barth, A.T., Lach, J., and Zhou, G. (2009, January 3\u20135). Accurate, fast fall detection using gyroscopes and accelerometer-derived posture information. Proceedings of the Sixth International Workshop on Wearable and Implantable Body Sensor Networks (BSN 2009), Berkeley, CA, USA.","DOI":"10.1109\/BSN.2009.46"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1016\/j.gaitpost.2008.01.003","article-title":"Comparison of low-complexity fall detection algorithms for body attached accelerometers","volume":"28","author":"Kangas","year":"2008","journal-title":"Gait Posture"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1016\/j.medengphy.2006.12.001","article-title":"A threshold-based fall-detection algorithm using a bi-axial gyroscope sensor","volume":"30","author":"Bourke","year":"2008","journal-title":"Med. Eng. Phys."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"372","DOI":"10.1109\/TMM.2008.917403","article-title":"Video-based human movement analysis and its application to surveillance systems","volume":"10","author":"Hsieh","year":"2008","journal-title":"IEEE Trans. Multimed."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Fookes, C., Denman, S., Lakemond, R., Ryan, D., Sridharan, S., and Piccardi, M. (2010, January 4\u20137). Semi-supervised intelligent surveillance system for secure environments. Proceedings of the 2010 IEEE International Symposium on Industrial Electronics (ISIE), Bari, Italy.","DOI":"10.1109\/ISIE.2010.5636922"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Czabke, A., Marsch, S., and Lueth, T.C. (2011, January 23\u201326). Accelerometer based real-time activity analysis on a microcontroller. Proceedings of the 2011 5th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth), Dublin, Ireland.","DOI":"10.4108\/icst.pervasivehealth.2011.245984"},{"key":"ref_12","first-page":"1295","article-title":"Energy efficient smartphone-based activity recognition using fixed-point arithmetic","volume":"19","author":"Anguita","year":"2013","journal-title":"J. Univ. Comput. Sci."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1145\/1964897.1964918","article-title":"Activity recognition using cell phone accelerometers","volume":"12","author":"Kwapisz","year":"2011","journal-title":"ACM SigKDD Explor. Newsl."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1609","DOI":"10.3233\/JIFS-169699","article-title":"A robust convolutional neural network for online smartphone-based human activity recognition","volume":"35","author":"Almaslukh","year":"2018","journal-title":"J. Intell. Fuzzy Syst."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"915","DOI":"10.1016\/j.asoc.2017.09.027","article-title":"Real-time human activity recognition from accelerometer data using Convolutional Neural Networks","volume":"62","author":"Ignatov","year":"2018","journal-title":"Appl. Soft Comput."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Zhang, M., and Sawchuk, A.A. (2012, January 5\u20138). USC-HAD: A daily activity dataset for ubiquitous activity recognition using wearable sensors. Proceedings of the 2012 ACM Conference on Ubiquitous Computing, Pittsburgh, PA, USA.","DOI":"10.1145\/2370216.2370438"},{"key":"ref_17","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 (ESANN), Bruges, Belgium."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1007\/s00779-017-1007-3","article-title":"A novel orientation-and location-independent activity recognition method","volume":"21","author":"Shi","year":"2017","journal-title":"Pers. Ubiquitous Comput."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1016\/j.pmcj.2017.01.008","article-title":"Position-aware activity recognition with wearable devices","volume":"38","author":"Sztyler","year":"2017","journal-title":"Pervasive Mob. Comput."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Siirtola, P., and R\u00f6ning, J. (2013, January 16\u201319). Ready-to-use activity recognition for smartphones. Proceedings of the 2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM), Singapore.","DOI":"10.1109\/CIDM.2013.6597218"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Anjum, A., and Ilyas, M.U. (2013, January 11\u201314). Activity recognition using smartphone sensors. Proceedings of the 2013 IEEE 10th Consumer Communications and Networking Conference (CCNC), Las Vegas, NV, USA.","DOI":"10.1109\/CCNC.2013.6488584"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.jneumeth.2013.09.015","article-title":"Hand, belt, pocket or bag: Practical activity tracking with mobile phones","volume":"231","author":"Antos","year":"2014","journal-title":"J. Neurosci. Methods"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"13099","DOI":"10.3390\/s131013099","article-title":"Exploratory data analysis of acceleration signals to select light-weight and accurate features for real-time activity recognition on smartphones","volume":"13","author":"Khan","year":"2013","journal-title":"Sensors"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"675","DOI":"10.1007\/s00779-012-0515-4","article-title":"Activity logging using lightweight classification techniques in mobile devices","volume":"17","author":"Bernardos","year":"2013","journal-title":"Pers. Ubiquitous Comput."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Sztyler, T., and Stuckenschmidt, H. (2016, January 14\u201319). On-body localization of wearable devices: An investigation of position-aware activity recognition. Proceedings of the 2016 IEEE International Conference on Pervasive Computing and Communications (PerCom), Sydney, Australia.","DOI":"10.1109\/PERCOM.2016.7456521"},{"key":"ref_26","unstructured":"Nham, B., Siangliulue, K., and Yeung, S. (0208, October 10). Predicting mode of transport from iphone accelerometer data. Available online: http:\/\/cs229.stanford.edu\/proj2008\/NhamSiangliulueYeung-PredictingModeOfTransportFromIphone~AccelerometerData.pdf."},{"key":"ref_27","first-page":"1995","article-title":"Convolutional networks for images, speech, and time series","volume":"3361","author":"LeCun","year":"1995","journal-title":"Handb. Brain Theory Neural Netw."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1126\/science.1127647","article-title":"Reducing the dimensionality of data with neural networks","volume":"313","author":"Hinton","year":"2006","journal-title":"Science"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1527","DOI":"10.1162\/neco.2006.18.7.1527","article-title":"A fast learning algorithm for deep belief nets","volume":"18","author":"Hinton","year":"2006","journal-title":"Neural Comput."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Zeng, M., Nguyen, L.T., Yu, B., Mengshoel, O.J., Zhu, J., Wu, P., and Zhang, J. (2014, January 6\u20137). Convolutional neural networks for human activity recognition using mobile sensors. Proceedings of the 6th International Conference on Mobile Computing, Applications and Services, Austin, TX, USA.","DOI":"10.4108\/icst.mobicase.2014.257786"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1016\/j.eswa.2016.04.032","article-title":"Human activity recognition with smartphone sensors using deep learning neural networks","volume":"59","author":"Ronao","year":"2016","journal-title":"Expert Syst. Appl."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1109\/JBHI.2016.2633287","article-title":"A deep learning approach to on-node sensor data analytics for mobile or wearable devices","volume":"21","author":"Ravi","year":"2017","journal-title":"IEEE J. Bbiomed. Health Inf."},{"key":"ref_34","unstructured":"Yang, J., Nguyen, M.N., San, P.P., Li, X., and Krishnaswamy, S. (2015, January 25\u201331). Deep Convolutional Neural Networks on Multichannel Time Series for Human Activity Recognition. Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, Buenos Aires, Argentina."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Yao, S., Hu, S., Zhao, Y., Zhang, A., and Abdelzaher, T. (2017, January 3\u20137). Deepsense: A unified deep learning framework for time-series mobile sensing data processing. Proceedings of the 26th International Conference on World Wide Web, Perth, Australia.","DOI":"10.1145\/3038912.3052577"},{"key":"ref_36","first-page":"99","article-title":"Unsupervised feature learning for human activity recognition using smartphone sensors","volume":"Volume 8891","author":"Prasath","year":"2014","journal-title":"Mining Intelligence and Knowledge Exploration, Lecture Notes in Computer Science"},{"key":"ref_37","first-page":"160","article-title":"An effective deep autoencoder approach for online smartphone-based human activity recognition","volume":"17","author":"Almaslukh","year":"2017","journal-title":"Int. J. Comput. Sci. Netw. Secur."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Radu, V., Lane, N.D., Bhattacharya, S., Mascolo, C., Marina, M.K., and Kawsar, F. (2016, January 12\u201316). Towards multimodal deep learning for activity recognition on mobile devices. Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct, Heidelberg, Germany.","DOI":"10.1145\/2968219.2971461"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1007\/s10015-017-0422-x","article-title":"Deep recurrent neural network for mobile human activity recognition with high throughput","volume":"23","author":"Inoue","year":"2018","journal-title":"Artif. Life Robot."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Chen, Y., Zhong, K., Zhang, J., Sun, Q., and Zhao, X. (2016, January 24\u201325). Lstm networks for mobile human activity recognition. Proceedings of the 2016 International Conference on Artificial Intelligence: Technologies and Applications, Bangkok, Thailand.","DOI":"10.2991\/icaita-16.2016.13"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Ord\u00f3\u00f1ez, F.J., and Roggen, D. (2016). Deep convolutional and lstm recurrent neural networks for multimodal wearable activity recognition. Sensors, 16.","DOI":"10.3390\/s16010115"},{"key":"ref_42","unstructured":"Alsheikh, M.A., Selim, A., Niyato, D., Doyle, L., Lin, S., and Tan, H.P. (2016, January 12\u201313). Deep Activity Recognition Models with Triaxial Accelerometers. Proceedings of the 30th AAAI Conference on Artificial Intelligence Artificial Intelligence Applied to Assistive Technologies and Smart Environments, Phoenix, AZ, USA."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1038\/323533a0","article-title":"Learning representations by back-propagating errors","volume":"323","author":"Rumelhart","year":"1986","journal-title":"Nature"},{"key":"ref_44","unstructured":"Hijazi, S., Kumar, R., and Rowen, C. (2015). Using Convolutional Neural Networks for Image Recognition, Cadence Design Systems Inc."},{"key":"ref_45","unstructured":"Kohavi, R. (1995, January 20\u201325). A study of cross-validation and bootstrap for accuracy estimation and model selection. Proceedings of the 1995 International Joint Conference on AI Palais de Congres, Montreal, QC, Canada."},{"key":"ref_46","unstructured":"Ruder, S. (arXiv, 2016). An overview of gradient descent optimization algorithms, arXiv."},{"key":"ref_47","unstructured":"Kingma, D.P., and Ba, J. (arXiv, 2014). Adam: A method for stochastic optimization, arXiv."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Bao, L., and Intille, S.S. (2004, January 21\u201323). Activity recognition from user-annotated acceleration data. Proceedings of the 2004 International Conference on Pervasive Computing, Linz\/Vienna, Austria.","DOI":"10.1007\/978-3-540-24646-6_1"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"320","DOI":"10.1109\/TBCAS.2011.2160540","article-title":"Sensor positioning for activity recognition using wearable accelerometers","volume":"5","author":"Atallah","year":"2011","journal-title":"IEEE Trans. Biomed. Circuits Syst."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1109\/MPRV.2014.73","article-title":"Sensor placement variations in wearable activity recognition","volume":"13","author":"Kunze","year":"2014","journal-title":"IEEE Pervasive Comput."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"25474","DOI":"10.3390\/s151025474","article-title":"Analysis of movement, orientation and rotation-based sensing for phone placement recognition","volume":"15","author":"Incel","year":"2015","journal-title":"Sensors"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Fujinami, K. (2016). On-body smartphone localization with an accelerometer. Information, 7.","DOI":"10.3390\/info7020021"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/11\/3726\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:27:28Z","timestamp":1760196448000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/11\/3726"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,11,1]]},"references-count":53,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2018,11]]}},"alternative-id":["s18113726"],"URL":"https:\/\/doi.org\/10.3390\/s18113726","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,11,1]]}}}