{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T14:17:47Z","timestamp":1773843467295,"version":"3.50.1"},"reference-count":44,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,1,16]],"date-time":"2023-01-16T00:00:00Z","timestamp":1673827200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Italian Ministry for University and Research","award":["ARS01_00345"],"award-info":[{"award-number":["ARS01_00345"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Embedded hardware systems, such as wearable devices, are widely used for health status monitoring of ageing people to improve their well-being. In this context, it becomes increasingly important to develop portable, easy-to-use, compact, and energy-efficient hardware-software platforms, to enhance the level of usability and promote their deployment. With this purpose an automatic tri-axial accelerometer-based system for postural recognition has been developed, useful in detecting potential inappropriate behavioral habits for the elderly. Systems in the literature and on the market for this type of analysis mostly use personal computers with high computing resources, which are not easily portable and have high power consumption. To overcome these limitations, a real-time posture recognition Machine Learning algorithm was developed and optimized that could perform highly on platforms with low computational capacity and power consumption. The software was integrated and tested on two low-cost embedded platform (Raspberry Pi 4 and Odroid N2+). The experimentation stage was performed on various Machine Learning pre-trained classifiers using data of seven elderly users. The preliminary results showed an activity classification accuracy of about 98% for the four analyzed postures (Standing, Sitting, Bending, and Lying down), with similar accuracy and a computational load as the state-of-the-art classifiers running on personal computers.<\/jats:p>","DOI":"10.3390\/s23021039","type":"journal-article","created":{"date-parts":[[2023,1,17]],"date-time":"2023-01-17T01:41:20Z","timestamp":1673919680000},"page":"1039","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Human Postures Recognition by Accelerometer Sensor and ML Architecture Integrated in Embedded Platforms: Benchmarking and Performance Evaluation"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8970-3313","authenticated-orcid":false,"given":"Alessandro","family":"Leone","sequence":"first","affiliation":[{"name":"National Research Council of Italy, Institute for Microelectronics and Microsystems, 73100 Lecce, Italy"}]},{"given":"Gabriele","family":"Rescio","sequence":"additional","affiliation":[{"name":"National Research Council of Italy, Institute for Microelectronics and Microsystems, 73100 Lecce, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0318-8347","authenticated-orcid":false,"given":"Andrea","family":"Caroppo","sequence":"additional","affiliation":[{"name":"National Research Council of Italy, Institute for Microelectronics and Microsystems, 73100 Lecce, Italy"}]},{"given":"Pietro","family":"Siciliano","sequence":"additional","affiliation":[{"name":"National Research Council of Italy, Institute for Microelectronics and Microsystems, 73100 Lecce, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5716-5824","authenticated-orcid":false,"given":"Andrea","family":"Manni","sequence":"additional","affiliation":[{"name":"National Research Council of Italy, Institute for Microelectronics and Microsystems, 73100 Lecce, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,16]]},"reference":[{"key":"ref_1","unstructured":"(2022, November 21). Available online: https:\/\/ec.europa.eu\/eurostat\/statistics-explained\/index.php?title=Population_structure_and_ageing#The_share_of_elderly_people_continues_to_increase\/."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"626","DOI":"10.1002\/tee.23580","article-title":"Biosensors and Chemical Sensors for Healthcare Monitoring: A Review","volume":"17","author":"Arakawa","year":"2022","journal-title":"IEEJ Trans. Electr. Electron. Eng."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"De Pascali, C., Francioso, L., Giampetruzzi, L., Rescio, G., Signore, M.A., Leone, A., and Siciliano, P. (2021). Modeling, Fabrication and Integration of Wearable Smart Sensors in a Monitoring Platform for Diabetic Patients. Sensors, 21.","DOI":"10.3390\/s21051847"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"8882378","DOI":"10.1155\/2020\/8882378","article-title":"IoT healthcare: Design of smart and cost-effective sleep quality monitoring system","volume":"2020","author":"Saleem","year":"2020","journal-title":"J. Sensors"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"171","DOI":"10.3389\/fnins.2018.00171","article-title":"Human postural control","volume":"12","author":"Ivanenko","year":"2018","journal-title":"Front. Neurosci."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Reinecke, S., Weisman, G., and Pope, M.H. (2020). Effects of Body Position and Centre of Gravity on Tolerance of Seated Postures. Hard Facts about Soft Machines, CRC Press.","DOI":"10.1201\/9781003069461-18"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Iazzi, A., Rziza, M., and Thami, R.O.H. (2018, January 21\u201324). Fall Detection based on Posture Analysis and Support Vector Machine. Proceedings of the 4th IEEE International Conference on Advanced Technologies for Signal and Image Processing (ATSIP), Sousse, Tunisia.","DOI":"10.1109\/ATSIP.2018.8364462"},{"key":"ref_8","unstructured":"Liu, J., Chen, X., Chen, S., Liu, X., Wang, Y., and Chen, L. (May, January 29). Tagsheet: Sleeping posture recognition with an unobtrusive passive tag matrix. Proceedings of the IEEE International Conference on Computer Communications, Paris, France."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Mallare, J.C.T.M., Pineda, D.F.G., Trinidad, G.M., Serafica, R.D., Villanueva, J.B.K., Dela Cruz, A.R., Vicerra, R.R.P., Serrano, K.K.D., and Roxas, E.A. (2017, January 1\u20133). Sitting posture assessment using computer vision. Proceedings of the 2017 IEEE 9th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM), Manila, Philippines.","DOI":"10.1109\/HNICEM.2017.8269473"},{"key":"ref_10","first-page":"1","article-title":"Automobile driver posture monitoring systems: A review","volume":"2","author":"Wang","year":"2019","journal-title":"China J. Highw. Transp."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2008\/476151","article-title":"Human posture tracking and classification through stereo vision and 3d model matching","volume":"2008","author":"Pellegrini","year":"2007","journal-title":"EURASIP J. Image Video Process."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1016\/j.imavis.2012.11.003","article-title":"3D head tracking for fall detection using a single calibrated camera","volume":"31","author":"Rougier","year":"2013","journal-title":"Image Vis. Comput."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1550147717707418","DOI":"10.1177\/1550147717707418","article-title":"Fall detection via human posture representation and support vector machine","volume":"13","author":"Fan","year":"2017","journal-title":"Int. J. Distrib. Sens. Netw."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Iazzi, A., Rziza, M., and Oulad Haj Thami, R. (2021). Fall Detection System-Based Posture-Recognition for Indoor Environments. J. Imaging, 7.","DOI":"10.3390\/jimaging7030042"},{"key":"ref_15","first-page":"749","article-title":"Human sit down position detection using data classification and dimensionality reduction","volume":"2","author":"Jaramillo","year":"2017","journal-title":"Adv. Sci."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Gupta, R., Gupta, S.H., Agarwal, A., Choudhary, P., Bansal, N., and Sen, S. (2020, January 13\u201315). A Wearable Multisensor Posture Detection System. Proceedings of the 2020 4th International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, India.","DOI":"10.1109\/ICICCS48265.2020.9121082"},{"key":"ref_17","first-page":"135","article-title":"Wearable Posture Identification System for Good Siting Position","volume":"10","author":"Lim","year":"2018","journal-title":"J. Telecommun. Electron. Comput. Eng."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Gupta, J., Gupta, N., Kumar, M., Duggal, R., and Rodrigues, J.J. (2021, January 7\u201311). Collection and Classification of Human Posture Data using Wearable Sensors. Proceedings of the 2021 IEEE Global Communications Conference (GLOBECOM), Madrid, Spain.","DOI":"10.1109\/GLOBECOM46510.2021.9685755"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"108252","DOI":"10.1016\/j.measurement.2020.108252","article-title":"Wearable human motion posture capture and medical health monitoring based on wireless sensor networks","volume":"166","author":"Gao","year":"2020","journal-title":"Measurement"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Fridriksdottir, E., and Bonomi, A.G. (2020). Accelerometer-Based Human Activity Recognition for Patient Monitoring Using a Deep Neural Network. Sensors, 20.","DOI":"10.3390\/s20226424"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Kale, H., Mandke, P., Mahajan, H., and Deshpande, V. (2018, January 14\u201315). Human posture recognition using artificial neural networks. Proceedings of the 2018 IEEE 8th International Advance Computing Conference (IACC), Greater Noida, India.","DOI":"10.1109\/IADCC.2018.8692143"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"112900","DOI":"10.1016\/j.sna.2021.112900","article-title":"A portable sitting posture monitoring system based on a pressure sensor array and machine learning","volume":"331","author":"Ran","year":"2021","journal-title":"Sensors Actuators A Phys."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Gjoreski, H., Lustrek, M., and Gams, M. (2011, January 25\u201328). Accelerometer placement for posture recognition and fall detection. Proceedings of the 2011 Seventh International Conference on Intelligent Environments, Nottingham, UK.","DOI":"10.1109\/IE.2011.11"},{"key":"ref_24","unstructured":"(2022, December 10). Available online: https:\/\/shimmersensing.com\/."},{"key":"ref_25","unstructured":"Aiello, G., Certa, A., Abusohyon, I., Longo, F., and Padovano, A. (2021, January 7\u20139). Machine Learning Approach towards Real Time Assessment of Hand-Arm Vibration Risk. Proceedings of the 17th IFAC Symposium on Information Control Problems in Manufacturing INCOM 2021, Budapest, Hungary."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Yan, S., Zhang, Y., Qiu, S., and Liu, L. (2022). Research on the Efficiency of Working Status Based on Wearable Devices in Different Light Environments. Micromachines, 13.","DOI":"10.3390\/mi13091410"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Sinha, V.K.K., Patro, K.K.K., P\u0142awiak, P., and Prakash, A.J.J. (2021). Smartphone-Based Human Sitting Behaviors Recognition Using Inertial Sensor. Sensors, 21.","DOI":"10.3390\/s21196652"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Donisi, L., Cesarelli, G., Pisani, N., Ponsiglione, A.M., Ricciardi, C., and Capodaglio, E. (2022). Wearable Sensors and Artificial Intelligence for Physical Ergonomics: A Systematic Review of Literature. Diagnostics, 12.","DOI":"10.3390\/diagnostics12123048"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Rescio, G., Leone, A., and Siciliano, P. (2013, January 13\u201314). Support Vector Machine for tri-axial accelerometer-based fall detector. Proceedings of the 5th IEEE International Workshop on Advances in Sensors and Interfaces (IWASI), Bari, Italy.","DOI":"10.1109\/IWASI.2013.6576096"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Muthukrishnan, R., and Rohini, R. (2016, January 24\u201324). LASSO: A feature selection technique in predictive modeling for machine learning. Proceedings of the 2016 IEEE international conference on advances in computer applications (ICACA), Tamilnadu, India.","DOI":"10.1109\/ICACA.2016.7887916"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2000","journal-title":"Mach. Learn"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10115-007-0114-2","article-title":"Top 10 algorithms in data mining","volume":"14","author":"Wu","year":"2008","journal-title":"Knowl. Inf. Syst."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1007\/BF00153759","article-title":"Instance-based learning algorithms","volume":"6","author":"Aha","year":"1991","journal-title":"Mach. Learn."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1774","DOI":"10.1109\/TNNLS.2017.2673241","article-title":"Efficient kNN classification with different numbers of nearest neighbors","volume":"5","author":"Zhang","year":"2018","journal-title":"IEEE Trans. Neural. Netw. Learn. Syst."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1016\/j.cpc.2018.02.018","article-title":"Optimizing event selection with the random grid search","volume":"228","author":"Bhat","year":"2018","journal-title":"Comput. Phys. Commun."},{"key":"ref_36","unstructured":"(2022, December 06). Available online: https:\/\/www.raspberrypi.com\/products\/raspberry-pi-4-model-b\/specifications\/."},{"key":"ref_37","unstructured":"(2022, December 06). Available online: https:\/\/www.odroid.co.uk\/index.php?route=product\/product&path=246_239&product_id=868."},{"key":"ref_38","unstructured":"(2022, December 06). Available online: https:\/\/www.lenovo.com\/it\/it\/desktops-and-all-in-ones\/thinkcentre\/m-series-sff\/ThinkCentre-M70s-Gen-3-Intel\/p\/LEN102C0010."},{"key":"ref_39","unstructured":"(2022, November 16). Available online: https:\/\/github.com\/seemoo-lab\/pyshimmer."},{"key":"ref_40","unstructured":"Hastie, T., Tibshirani, R., and Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer Science & Business Media."},{"key":"ref_41","unstructured":"(2022, November 18). Available online: https:\/\/www.manualslib.com\/products\/Ruideng-Um25c-10243666.html."},{"key":"ref_42","unstructured":"(2022, November 18). Available online: https:\/\/play.google.com\/store\/apps\/details?id=com.ruidenggoogle.bluetooth&hl=en_US&gl=US."},{"key":"ref_43","unstructured":"(2022, November 18). Available online: https:\/\/apps.apple.com\/us\/app\/um-meter\/id1439150213."},{"key":"ref_44","unstructured":"(2022, December 12). Available online: https:\/\/up-shop.org\/up-board-series.html."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/2\/1039\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:07:38Z","timestamp":1760119658000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/2\/1039"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,16]]},"references-count":44,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2023,1]]}},"alternative-id":["s23021039"],"URL":"https:\/\/doi.org\/10.3390\/s23021039","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,16]]}}}