{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T11:20:30Z","timestamp":1769167230619,"version":"3.49.0"},"reference-count":35,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2015,9,11]],"date-time":"2015-09-11T00:00:00Z","timestamp":1441929600000},"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>Inertial sensors are increasingly being used to recognize and classify physical activities in a variety of applications. For monitoring and fitness applications, it is crucial to develop methods able to segment each activity cycle, e.g., a gait cycle, so that the successive classification step may be more accurate. To increase detection accuracy,  pre-processing is often used, with a concurrent increase in computational cost. In this paper, the effect of pre-processing operations on the detection and classification of locomotion activities was investigated, to check whether the presence of pre-processing significantly contributes to an increase in accuracy. The pre-processing stages evaluated in this study were inclination correction and de-noising. Level walking, step ascending, descending and running were monitored by using a shank-mounted inertial sensor. Raw and filtered segments, obtained from a modified version of a rule-based gait detection algorithm optimized for sequential processing, were processed to extract time and frequency-based features for physical activity classification through a support vector machine classifier. The proposed method accurately detected &gt;99% gait cycles from raw data and produced &gt;98% accuracy on these segmented gait cycles. Pre-processing did not substantially increase classification accuracy, thus highlighting the possibility of reducing the amount of pre-processing for real-time applications.<\/jats:p>","DOI":"10.3390\/s150923095","type":"journal-article","created":{"date-parts":[[2015,9,15]],"date-time":"2015-09-15T03:46:33Z","timestamp":1442288793000},"page":"23095-23109","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Pre-Processing Effect on the Accuracy of Event-Based Activity Segmentation and Classification through Inertial Sensors"],"prefix":"10.3390","volume":"15","author":[{"given":"Benish","family":"Fida","sequence":"first","affiliation":[{"name":"Department of Engineering, University of Roma Tre, Via Vito Volterra, 62, Rome 00146, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ivan","family":"Bernabucci","sequence":"additional","affiliation":[{"name":"Department of Engineering, University of Roma Tre, Via Vito Volterra, 62, Rome 00146, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daniele","family":"Bibbo","sequence":"additional","affiliation":[{"name":"Department of Engineering, University of Roma Tre, Via Vito Volterra, 62, Rome 00146, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Silvia","family":"Conforto","sequence":"additional","affiliation":[{"name":"Department of Engineering, University of Roma Tre, Via Vito Volterra, 62, Rome 00146, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3380-6994","authenticated-orcid":false,"given":"Maurizio","family":"Schmid","sequence":"additional","affiliation":[{"name":"Department of Engineering, University of Roma Tre, Via Vito Volterra, 62, Rome 00146, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2015,9,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"10146","DOI":"10.3390\/s140610146","article-title":"Fusion of Smartphone Motion Sensors for Physical Activity Recognition","volume":"14","author":"Shoaib","year":"2014","journal-title":"Sensors"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1109\/TITB.2012.2223823","article-title":"Comparing supervised learning techniques on the task of physical activity recognition","volume":"17","author":"Dalton","year":"2013","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"446","DOI":"10.1016\/j.simpat.2009.09.002","article-title":"Mobile health monitoring system based on activity recognition using accelerometer","volume":"18","author":"Hong","year":"2010","journal-title":"Simul Model. Pract. Theory"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1038\/oby.2003.7","article-title":"Measurement of human daily physical activity","volume":"11","author":"Zhang","year":"2003","journal-title":"Obes. Res."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"887","DOI":"10.1007\/s00779-011-0403-3","article-title":"A single tri-axial accelerometer-based real-time personal life log system capable of human activity recognition and exercise information generation","volume":"15","author":"Lee","year":"2011","journal-title":"Pers. Ubiquit Comput."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.gaitpost.2013.06.005","article-title":"Activity classification in users of ankle foot orthoses","volume":"39","author":"Archer","year":"2013","journal-title":"Gait Posture"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1016\/j.medengphy.2014.11.008","article-title":"Activity classification in persons with stroke based on frequency features","volume":"37","author":"Laudanski","year":"2015","journal-title":"Med. Eng. Phys."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"705","DOI":"10.1016\/j.medengphy.2015.04.005","article-title":"Varying behavior of different window sizes on the classification of static and dynamic physical activities from a single accelerometer","volume":"37","author":"Fida","year":"2015","journal-title":"Med. Eng. Phys."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"5683","DOI":"10.3390\/s100605683","article-title":"Gait Event Detection on Level Ground and Incline Walking Using a Rate Gyroscope","volume":"10","author":"Catalfamo","year":"2010","journal-title":"Sensors"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1251","DOI":"10.1007\/s11517-010-0692-0","article-title":"An adaptive gyroscope-based algorithm for temporal gait analysis","volume":"48","author":"Greene","year":"2010","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1434","DOI":"10.1109\/TBME.2004.827933","article-title":"Gait assessment in Parkinson\u2019s disease: Toward an ambulatory system for long-term monitoring","volume":"51","author":"Salarian","year":"2004","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"689","DOI":"10.1016\/S0021-9290(02)00008-8","article-title":"Spatio-temporal parameters of gait measured by an ambulatory system using miniature gyroscopes","volume":"35","author":"Aminian","year":"2002","journal-title":"J. Biomech."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Chen, M., Yan, J., and Xu, Y. (2009, January 10\u201315). Gait Pattern Classification with Integrated Shoes. Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems, St. Louis, MO, USA.","DOI":"10.1109\/IROS.2009.5354111"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1007\/s11517-008-0327-x","article-title":"Support vector machine for classification of walking conditions using miniature kinematic sensors","volume":"46","author":"Lau","year":"2008","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1016\/j.gaitpost.2004.08.008","article-title":"Stair climbing detection during daily physical activity using a miniature gyroscope","volume":"22","author":"Coley","year":"2005","journal-title":"Gait Posture"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"5470","DOI":"10.3390\/s140305470","article-title":"Gait Event Detection during Stair Walking Using a Rate Gyroscope","volume":"14","author":"Formento","year":"2014","journal-title":"Sensors"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"585","DOI":"10.1016\/j.neucom.2014.08.016","article-title":"A new strategy for parameter optimization to improve phase-dependent locomotion mode recognition","volume":"149","author":"Chen","year":"2015","journal-title":"Neurocomputing"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1073","DOI":"10.1109\/TIM.2012.2236792","article-title":"Continuous hidden markov model for pedestrian activity classification and gait analysis","volume":"62","author":"Panahandeh","year":"2013","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1289","DOI":"10.1016\/j.patcog.2014.10.012","article-title":"Similar gait action recognition using an inertial sensor","volume":"48","author":"Ngo","year":"2015","journal-title":"Pattern Recognit."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Azhar, M.A., Gouwanda, D., and Gopalai, A.A. (2014, January 1\u20134). Devemlopment of an Intelligent Real-Time Heuristic-Based Algorithm to Identify Human Gait Events. Proceedings of the IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), Valencia, Spain.","DOI":"10.1109\/BHI.2014.6864429"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"5163","DOI":"10.3390\/s150305163","article-title":"Low energy physical activity recognition system on smartphones","volume":"15","author":"Morillo","year":"2015","journal-title":"Sensors"},{"key":"ref_22","first-page":"1","article-title":"Physical Activity Recognition Utilizing the Built-In Kinematic Sensors of a Smartphone","volume":"2013","author":"He","year":"2013","journal-title":"Int. J. Distrib. Sens. Netw."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"450","DOI":"10.1016\/j.procs.2014.07.009","article-title":"A Study on Human Activity Recognition Using Accelerometer Data from Smartphones","volume":"34","author":"Bayat","year":"2014","journal-title":"Proced. Comput. Sci."},{"key":"ref_24","unstructured":"Fraccaro, P., Coyle, L.D.J., and O\u2019Sullivan, D. (2014, January 24\u201327). Real-world Gyroscope-based Gait Event Detection and Gait Feature Extraction. Proceedings of the Sixth International Conference on eHealth, Telemedicine and Social Medicine, Barcelona, Spain."},{"key":"ref_25","first-page":"1","article-title":"Activity Recognition on Smartphones via Sensor-Fusion and KDA-Based SVMs","volume":"2014","author":"Khan","year":"2014","journal-title":"Int. J. Distrib. Sens. Netw."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/S1350-4533(99)00030-2","article-title":"A practical gait analysis system using gyroscopes","volume":"21","author":"Tong","year":"1999","journal-title":"Med. Eng. Phys."},{"key":"ref_27","unstructured":"Mantyjarvi, J., Himberg, J., and Seppanen, T. (2001, January 7\u201310). Recognizing Human Motion with Multiple Acceleration Sensors. Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, Tucson, AZ, USA."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Maurer, U., Smailagic, A., Siewiorek, D.P., and Deisher, M. (2006, January 3\u20135). Activity Recognition and Monitoring Using Multiple Sensors on Different Body Positions. Proceedings of the International Workshop on Wearable and Implantable Body Sensor Networks, Cambridge, MA, USA.","DOI":"10.21236\/ADA534437"},{"key":"ref_29","unstructured":"Lovell, N.H., Wang, N., Ambikairajah, E., and Celler, B.G. (2007, January 23\u201326). Accelerometry Based Classification of Walking Patterns Using Time-frequency Analysis. Proceedings of the 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Lyon, France."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Wang, J.-H., Ding, J.-J., Chen, Y., and Chen, H.-H. (2012, January 2\u20135). Real Time Accelerometer-Based Gait Recognition Using Adaptive Windowed Wavelet Transforms. Proceedings of the IEEE Asia Pacific Conference on Circuits and Systems (APCCAS), Kaohsiung, Taiwan.","DOI":"10.1109\/APCCAS.2012.6419104"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1023\/A:1012487302797","article-title":"Gene selection for cancer classification using support vector machines","volume":"46","author":"Guyon","year":"2002","journal-title":"Mach. Learn."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Fida, B., Bernabucci, I., Bibbo, D., Conforto, S., Proto, A., and Schmid, M. (2014, January 23\u201325). The Effect of Window Length on the Classification of Dynamic Activities through a Single Accelerometer. Proceedings of the IASTED International Conference Biomedical Engineering (BioMed), Zurich, Switerzland.","DOI":"10.2316\/P.2014.818-046"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1002\/int.21568","article-title":"Assessing Gait Patterns of Healthy Adults Climbing Stairs Employing Machine Learning Techniques","volume":"28","author":"Chan","year":"2013","journal-title":"Int. J. Intell. Syst."},{"key":"ref_34","unstructured":"Kre\u00dfel, U.H.-G. (1999). Advances in kernel Methods, MIT Press."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Witten, I.H., and Frank, E. (2002). Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations, Morgan Kaufmann.","DOI":"10.1145\/507338.507355"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/15\/9\/23095\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T20:48:23Z","timestamp":1760215703000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/15\/9\/23095"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015,9,11]]},"references-count":35,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2015,9]]}},"alternative-id":["s150923095"],"URL":"https:\/\/doi.org\/10.3390\/s150923095","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2015,9,11]]}}}