{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T18:36:11Z","timestamp":1774722971760,"version":"3.50.1"},"reference-count":43,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2019,9,20]],"date-time":"2019-09-20T00:00:00Z","timestamp":1568937600000},"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>This paper presents two approaches to assess the effect of the number of inertial sensors and their location placements on recognition of human postures and activities. Inertial and Magnetic Measurement Units (IMMUs)\u2014which consist of a triad of three-axis accelerometer, three-axis gyroscope, and three-axis magnetometer sensors\u2014are used in this work. Five IMMUs are initially used and attached to different body segments. Placements of up to three IMMUs are then considered: back, left foot, and left thigh. The subspace k-nearest neighbors (KNN) classifier is used to achieve the supervised learning process and the recognition task. In a first approach, we feed raw data from three-axis accelerometer and three-axis gyroscope into the classifier without any filtering or pre-processing, unlike what is usually reported in the state-of-the-art where statistical features were computed instead. Results show the efficiency of this method for the recognition of the studied activities and postures. With the proposed algorithm, more than 80% of the activities and postures are correctly classified using one IMMU, placed on the lower back, left thigh, or left foot location, and more than 90% when combining all three placements. In a second approach, we extract attitude, in term of quaternion, from IMMUs in order to more precisely achieve the recognition process. The obtained accuracy results are compared to those obtained when only raw data is exploited. Results show that the use of attitude significantly improves the performance of the classifier, especially for certain specific activities. In that case, it was further shown that using a smaller number of features, with quaternion, in the recognition process leads to a lower computation time and better accuracy.<\/jats:p>","DOI":"10.3390\/s19194058","type":"journal-article","created":{"date-parts":[[2019,9,20]],"date-time":"2019-09-20T10:48:14Z","timestamp":1568976494000},"page":"4058","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Human Activities and Postures Recognition: From Inertial Measurements to Quaternion-Based Approaches"],"prefix":"10.3390","volume":"19","author":[{"given":"Makia","family":"Zmitri","sequence":"first","affiliation":[{"name":"GIPSA-Lab, Department of Automatic Control, University Grenoble Alpes, 38000 Grenoble, France"},{"name":"AGEIS, Univ. Grenoble Alpes, 38000 Grenoble, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8398-7183","authenticated-orcid":false,"given":"Hassen","family":"Fourati","sequence":"additional","affiliation":[{"name":"GIPSA-Lab, Department of Automatic Control, University Grenoble Alpes, 38000 Grenoble, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3773-393X","authenticated-orcid":false,"given":"Nicolas","family":"Vuillerme","sequence":"additional","affiliation":[{"name":"AGEIS, Univ. Grenoble Alpes, 38000 Grenoble, France"},{"name":"Institut Universitaire de France, 75231 Paris, France"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,9,20]]},"reference":[{"key":"ref_1","first-page":"93","article-title":"Human Activity Recognition Supported on Indoor Localization: A Systematic Review","volume":"249","year":"2018","journal-title":"Stud. Health Technol. Inf."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Fu, B., Kirchbuchner, F., Kuijper, A., Braun, A., and Vaithyalingam Gangatharan, D. (2018). Fitness Activity Recognition on Smartphones Using Doppler Measurements. Informatics, 5.","DOI":"10.3390\/informatics5020024"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Sebestyen, G., Stoica, I., and Hangan, A. (2016, January 8\u201310). Human activity recognition and monitoring for elderly people. Proceedings of the IEEE 12th International Conference on Intelligent Computer Communication and Processing (ICCP), Cluj-Napoca, Romania.","DOI":"10.1109\/ICCP.2016.7737171"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Taha, A., Zayed, H., Khalifa, M.E., and El-Horbarty, E.S. (2015, January 12\u201315). Human Activity Recognition for Surveillance Applications. Proceedings of the ICIT 2015 The 7th International Conference on Information Technology, Amman, Jordan.","DOI":"10.15849\/icit.2015.0103"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Khattak, A.M., Hung, D.V., Truc, P.T.H., Hung, L.X., Guan, D., Pervez, Z., Han, M., Lee, S., and Lee, Y.K. (2010, January 1\u20133). Context-aware Human Activity Recognition and decision making. Proceedings of the 12th IEEE International Conference on e-Health Networking, Applications and Services, Lyon, France.","DOI":"10.1109\/HEALTH.2010.5556585"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Jia, Y. (2009, January 1\u20133). Diatetic and exercise therapy against diabetes mellitus. Proceedings of the 2009 2th International Conference on Intelligent Networks and Intelligent Systems, Tianjin, China.","DOI":"10.1109\/ICINIS.2009.177"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1109\/MNET.2010.5510920","article-title":"G-sense: A scalable architecture for global sensing and monitorin","volume":"24","author":"Perez","year":"2010","journal-title":"IEEE Netw."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1473","DOI":"10.1109\/TCSVT.2008.2005594","article-title":"Machine recognition of human activities: A survey","volume":"18","author":"Turaga","year":"2008","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_9","unstructured":"Bayat, A., Pomplun, M., and Tran, D.A. (2014, January 17\u201320). A study on human activity recognition using accelerometer data from smartphones. Proceedings of the 11th International Conference on Mobile Systems and Pervasive Computing, Ontorio, ON, Canada."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Ayu, M.A. (2011, January 20\u201323). Recognizing user activity based on accelerometer data from a mobile phone. Proceedings of the IEEE Symposium on Computers & Informatics, Kuala Lumpur, Malaysia.","DOI":"10.1109\/ISCI.2011.5958987"},{"key":"ref_11","first-page":"146","article-title":"Activity recognition using k-nearest neighbor algorithm on smartphone with tri-axial accelerometer","volume":"1","author":"Kaghyan","year":"2012","journal-title":"Inf. Models Anal."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Ponce, H. (2016). A Novel Wearable Sensor-Based Human Activity Recognition Approach Using Artificial Hydrocarbon Networks. Sensors, 16.","DOI":"10.3390\/s16071033"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"31314","DOI":"10.3390\/s151229858","article-title":"Physical Human Activity Recognition Using Wearable Sensors","volume":"15","author":"Attal","year":"2015","journal-title":"Sensors"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Soro, A., Brunner, G., Tanner, S., and Wattenhofer, R. (2019). Recognition and Repetition Counting for Complex Physical Exercises with Deep Learning. Sensors, 19.","DOI":"10.3390\/s19030714"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2059","DOI":"10.3390\/s150102059","article-title":"A survey of online activity recognition using mobile phones","volume":"15","author":"Shoaib","year":"2015","journal-title":"Sensors"},{"key":"ref_16","unstructured":"Cruz-Silva, N. (2013, January 9\u201312). Features Selection for Human Activity Recognition with iPhone Inertial Sensors. Proceedings of the 16th Portuguese Conference on Artificial Inteligence, EPIA 2013, Angra do Hero\u00edsmo, Portugal."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Sang, V., Yano, S., and Kondo, T. (2018). On-Body Sensor Positions Hierarchical Classification. Sensors, 18.","DOI":"10.3390\/s18113612"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1154","DOI":"10.3390\/s100201154","article-title":"Machine learning methods for classifying human physical activity from on-body accelerometers","volume":"10","author":"Mannini","year":"2010","journal-title":"Sensors"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Taborri, J., Palermo, E., and Rossi, S. (2019). Automatic Detection of Faults in Race Walking: A Comparative Analysis of Machine-Learning Algorithms Fed with Inertial Sensor Data. Sensors, 19.","DOI":"10.3390\/s19061461"},{"key":"ref_20","unstructured":"Mandong, A.M., and Munir, U. (2018, January 26\u201328). Smartphone Based Activity Recognition using K-Nearest Neighbor Algorithm. Proceedings of the International Conference on Engineering Technologies, Konya, Turkey."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Janidarmian, M., Roshan Fekr, A., Radecka, K., and Zilic, Z. (2017). A Comprehensive Analysis on Wearable Acceleration Sensors in Human Activity Recognition. Sensors, 17.","DOI":"10.3390\/s17030529"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"342","DOI":"10.1109\/TCYB.2013.2255271","article-title":"Occlusion handling via random subspace classifiers for human detection","volume":"44","author":"Amores","year":"2014","journal-title":"IEEE Trans. Cybern."},{"key":"ref_23","first-page":"1","article-title":"Human Activity Recognition in AAL Environments Using Random Projections","volume":"2016","author":"Vasiljevas","year":"2016","journal-title":"Comput. Math. Methods Med."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1109\/TCE.2018.2859625","article-title":"A Super Fast Attitude Determination Algorithm with Accelerometer and Magnetometer","volume":"64","author":"Wu","year":"2018","journal-title":"IEEE Trans. Consum. Electron."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1016\/j.pmcj.2018.03.004","article-title":"Attitude estimation for indoor navigation and augmented reality with smartphones","volume":"46","author":"Michel","year":"2018","journal-title":"Pervasive Mob. Comput."},{"key":"ref_26","unstructured":"Gait Up (2018, April 20). Startup for Fast and Accurate Motion Analysis. Available online: https:\/\/gaitup.com\/."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Kuipers, B.K. (1998). Quaternions and Rotation Sequences, Princeton University Press.","DOI":"10.1515\/9780691211701"},{"key":"ref_28","unstructured":"(2018, April 23). Rotations in Three-Dimensions: Euler Angles and Rotation Matrices. Available online: http:\/\/danceswithcode.net\/engineeringnotes\/rotations_in_3d\/rotations_in_3d_part1.html."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"409","DOI":"10.1137\/1007077","article-title":"A least squares estimate of satellite attitude","volume":"7","author":"Wahba","year":"1965","journal-title":"SIAM Rev."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1350","DOI":"10.2514\/3.2555","article-title":"A passive system for determining the attitude of a satellite","volume":"2","author":"Black","year":"1964","journal-title":"AIAA J."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"70","DOI":"10.2514\/3.19717","article-title":"Three-axis attitude determination from vector observations","volume":"4","author":"Shuster","year":"1981","journal-title":"J. Guid. Control. Dyn."},{"key":"ref_32","first-page":"1245","article-title":"Attitude determination using vector observations and the singular value decomposition","volume":"36","author":"Markley","year":"1988","journal-title":"J. Astronaut. Sci."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1109\/TAES.2006.1603413","article-title":"A Novel Quaternion Kalman Filter","volume":"42","author":"Choukroun","year":"2006","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Bernal-Polo, P., and Mart\u00ednez-Barber\u00e1, H. (2019). Kalman Filtering for Attitude Estimation with Quaternions and Concepts from Manifold Theory. Sensors, 19.","DOI":"10.3390\/s19010149"},{"key":"ref_35","unstructured":"Harada, T. (May, January 26). Portable absolute orientation estimation device with wireless network under accelerated situation. Proceedings of the International Conference on Robotics and Automation, New Orleans, LA, USA."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1109\/TIM.2014.2335912","article-title":"Heterogeneous Data Fusion Algorithm for Pedestrian Navigation via Foot-Mounted Inertial Measurement Unit and Complementary Filter","volume":"64","author":"Fourati","year":"2015","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"712","DOI":"10.1016\/j.conengprac.2010.01.012","article-title":"Design and implementation of a low-cost observer based attitude and heading reference system","volume":"18","author":"Martin","year":"2010","journal-title":"Control. Eng. Pract."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1007\/BF03546284","article-title":"Quaternions attitude estimation using vector observations","volume":"48","author":"Markley","year":"2000","journal-title":"J. Astronaut. Sci."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Michel, T. (2017, January 13\u201317). On attitude estimation with smartphones. Proceedings of the IEEE International Conference on Pervasive Computing and Communications, Kona, HI, USA.","DOI":"10.1109\/PERCOM.2017.7917873"},{"key":"ref_40","unstructured":"NOAA (2018, April 20). The World Magnetic Model, Available online: http:\/\/www.ngdc.noaa.gov."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1080\/00031305.1992.10475879","article-title":"An introduction to kernel and nearest-neighbor nonparametric regression","volume":"46","author":"Altman","year":"1992","journal-title":"Am. Stat."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"832","DOI":"10.1109\/34.709601","article-title":"The random subspace method for constructing decision forests","volume":"20","author":"Ho","year":"1998","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_43","unstructured":"Madgwick, S.O.H. (2010). An. Efficient Orientation Filter for Inertial and Inertial\/Magnetic Sensor Arrays, University of Bristol. Report x-io."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/19\/4058\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:22:18Z","timestamp":1760188938000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/19\/4058"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,9,20]]},"references-count":43,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2019,10]]}},"alternative-id":["s19194058"],"URL":"https:\/\/doi.org\/10.3390\/s19194058","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,9,20]]}}}