{"id":22520,"date":"2026-07-16T13:30:21","date_gmt":"2026-07-16T11:30:21","guid":{"rendered":"https:\/\/www.neuraldesigner.com\/use-cases\/activity-recognition\/"},"modified":"2026-08-26T13:48:22","modified_gmt":"2026-08-26T11:48:22","slug":"activity-recognition","status":"publish","type":"page","link":"https:\/\/www.neuraldesigner.com\/use-cases\/activity-recognition\/","title":{"rendered":"Human activity recognition using machine learning"},"content":{"rendered":"<style>.ndb{width:100vw;margin-left:calc(50% - 50vw);background:#eeeeee;padding:22px 24px 14px;font-family:\"Outfit\",\"Roboto\",Arial,sans-serif;color:#1b2635}.ndb *{box-sizing:border-box}.ndb a{text-decoration:none}.ndb-wrap{width:min(100%,960px);margin:0 auto}.ndb-lead{font-size:18px;line-height:1.6;color:#3a4a5a;font-weight:300;margin:0 0 22px}.ndb-lead a{color:#2d799f;font-weight:600}.ndb-highlight{margin:0 0 36px;padding:28px 34px;border-radius:18px;background:linear-gradient(135deg,#56a1c8 0%,#245e80 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h3{color:#001233!important;font-size:16px!important;font-weight:600!important;margin:0!important;text-align:center!important;letter-spacing:.03em}body.page-child.parent-pageid-22498 main.site-main .ndb-wrap .ndb-card table td p img{width:52px!important;height:52px!important;max-width:52px!important;min-width:0!important;object-fit:contain!important;margin:0 auto!important;display:inline-block!important}main#content .ndb-card .nd-usecase-title-icon,main#content .ndb-card h3.nd-usecase-icon-title img{display:none!important}main#content .ndb-card h3.nd-usecase-icon-title{display:block!important}.ndb-card .ndb-subhead{color:#001233!important;font-size:19px!important;font-weight:600!important;margin:26px 0 8px!important;text-align:center!important}.ndb-card table:has(td h3) td p:not(:has(img)){font-size:14px!important;line-height:1.5!important;color:#51606f!important;margin:8px auto 0!important;text-align:center!important;max-width:none!important}<\/style><div class=\"ndb\"><div class=\"ndb-wrap\"><ul class=\"ndb-toc\"><li><a href=\"#introduction\">Introduction<\/a><\/li><li><a href=\"#objectives\">Objectives<\/a><\/li><li><a href=\"#benefits\">Benefits<\/a><\/li><li><a href=\"#approach\">Approach<\/a><\/li><li><a href=\"#conclusions\">Conclusions<\/a><\/li><li><a href=\"#data-sets\">Data sets<\/a><\/li><li><a href=\"#further-reading\">Further reading<\/a><\/li><\/ul><div class=\"ndb-card\" id=\"introduction\"><h2>Introduction<\/h2><p>Device sensors provide real-time insights into what persons are doing (walking, running, driving, etc.).Knowing users&#8217; activity allows, for instance, to interact with them through an app.<\/p>\n<p>You can apply machine learning to detect activities by reading and processing sensor data in this regard.<\/p>\n<p>Healthcare professionals can test this methodology by downloading Neural Designer<\/p>\n<p>Download<\/p>\n<\/div><div class=\"ndb-card\" id=\"objectives\"><h2>Objectives<\/h2><p>Human Activity Recognition (HAR) classifies a person\u2019s actions from sensor measurements.<\/p>\n<p>With the rise of the Internet of Things, devices like smartwatches, heart rate monitors, or smartphones easily capture this data.<\/p>\n<p>Feature extraction is usually performed with a fixed-length sliding window, requiring parameters such as window size and shift.<\/p>\n<ul>\n<li>Body acceleration.<\/li>\n<li>Gravity acceleration.<\/li>\n<li>Body angular speed.<\/li>\n<li>Body angular acceleration.<\/li>\n<li>Etc.<\/li>\n<\/ul>\n<p>The machine learning model used for activity recognition relies on top of the devices&#8217; available sensors.<\/p>\n<p>However, analyzing this data can be a big challenge. Indeed, human activities are complex, and there are differences between individuals.<\/p>\n<\/div><div class=\"ndb-card\" id=\"benefits\"><h2>Benefits<\/h2><h3>2.1. Monitor health<\/h3>\n<p>Analyze a person\u2019s activity by carefully processing the information collected from various wearable devices and sensors to continuously monitor their health.<\/p>\n<h3>2.2. Discover activity patterns<\/h3>\n<p>Identify the key variables and patterns from the collected data that accurately determine which activity a person is performing.<\/p>\n<h3>2.3. Detect activity<\/h3>\n<p>Build a predictive model that can accurately recognize a person\u2019s activity based on the signals received from multiple wearable devices and sensors over time.<\/p>\n<h3>2.3. Improve wellbeing<\/h3>\n<p>Use the analysis to design personalized exercise plans specifically aimed at improving the overall health and wellbeing of the individual.<\/p>\n<\/div><div class=\"ndb-card\" id=\"approach\"><h2>Approach<\/h2><p><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/neural-network\/\">Neural networks<\/a> are the perfect algorithms to determine a person&#8217;s physical activity. This is due to their ability to recognize the patterns behind the data.<\/p>\n<p>The following graph illustrates a neural network that classifies different activities using smartphone data.<\/p>\n<p><img fetchpriority=\"high\" decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/08\/activity-recognition-neural-network-small.webp\" sizes=\"(max-width: 601px) 100vw, 601px\" srcset=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/08\/activity-recognition-neural-network-small.webp 601w, https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/08\/activity-recognition-neural-network-small-300x96.webp 300w\" alt=\"\" width=\"601\" height=\"192\" \/><\/p>\n<\/div><div class=\"ndb-card ndb-card--accent\" id=\"conclusions\"><h2>Conclusions<\/h2><p>Human activity recognition has a wide range of uses because of its impact on wellbeing.<\/p>\n<p>It is becoming a fundamental tool in healthcare solutions such as preventing obesity or caring for elderly persons.<\/p>\n<\/div><div class=\"ndb-card\" id=\"data-sets\"><h2>Data sets<\/h2><ul>\n<li><a href=\"https:\/\/archive.ics.uci.edu\/ml\/datasets\/OPPORTUNITY+Activity+Recognition\">OPPORTUNITY Activity Recognition Data Set<\/a>.<\/li>\n<\/ul>\n<\/div><div class=\"ndb-card\" id=\"further-reading\"><h2>Further reading<\/h2><ul>\n<li><a href=\"https:\/\/www.sciencedirect.com\/science\/article\/abs\/pii\/S016786551830045X\">Deep learning for sensor-based activity recognition: A survey<\/a>.<\/li>\n<li><a href=\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0957417412010585\" rel=\"nofollow\">Chernbumroong, S., Cang, S., Atkins, A., &amp; Yu, H. (2013). Elderly activities recognition and classification for applications in assisted living. Expert Systems with Applications, 40(5), 1662-1674<\/a>.<\/li>\n<li><a href=\"https:\/\/dspace.mit.edu\/handle\/1721.1\/44913\" rel=\"nofollow\">Anguita, D., Ghio, A., Oneto, L., Parra, X., &amp; Reyes-Ortiz, J. L. (2013, April). A public domain dataset for human activity recognition using smartphones. In Esann<\/a>.<\/li>\n<li><a href=\"https:\/\/www.mdpi.com\/1424-8220\/10\/2\/1154\/htm\" rel=\"nofollow\">Mannini, A., &amp; Sabatini, A. M. (2010). Machine learning methods for classifying human physical activity from on-body accelerometers. Sensors, 10(2), 1154-1175<\/a>.<\/li>\n<\/ul>\n<\/div><\/div><\/div>","protected":false},"excerpt":{"rendered":"<p>IntroductionObjectivesBenefitsApproachConclusionsData setsFurther readingIntroductionDevice sensors provide real-time insights into what persons are doing (walking, running, driving, etc.).Knowing users&#8217; activity allows, for instance, to interact with them through an app. You can apply machine learning to detect activities by reading and processing sensor data in this regard. Healthcare professionals can test this methodology by downloading Neural Designer [&hellip;]<\/p>\n","protected":false},"author":152,"featured_media":2699,"parent":22498,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"footnotes":""},"class_list":["post-22520","page","type-page","status-publish","has-post-thumbnail","hentry"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.4 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Human activity recognition using machine learning - Neural Designer<\/title>\n<meta name=\"description\" content=\"Explore this Neural Designer use case for Human activity recognition using machine learning, including problem framing, data, evaluation and limitations.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.neuraldesigner.com\/use-cases\/activity-recognition\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Human activity recognition using machine learning - Neural Designer\" \/>\n<meta property=\"og:description\" content=\"IntroductionObjectivesBenefitsApproachConclusionsData setsFurther readingIntroductionDevice sensors provide real-time insights into what persons are doing (walking, running, driving, etc.).Knowing users&#8217; activity allows, for instance, to interact with them through an app. You can apply machine learning to detect activities by reading and processing sensor data in this regard. 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