{"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":"2026-07-16T13:47:15","modified_gmt":"2026-07-16T11:47:15","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":"

Introduction<\/h2>\n

Device sensors provide real-time insights into what persons are doing (walking, running, driving, etc.).Knowing users’ activity allows, for instance, to interact with them through an app.<\/p>\n

You can apply machine learning to detect activities by reading and processing sensor data in this regard.<\/p>\n

Healthcare professionals can test this methodology by downloading Neural Designer<\/p>\n

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Download<\/p>\n

Contents<\/h2>\n


\n1. Objectives
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\n2. Benefits
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\n3. Approach
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\n4. Conclusions
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1. Objectives<\/h2>\n

Human Activity Recognition (HAR) classifies a person\u2019s actions from sensor measurements.<\/p>\n

With the rise of the Internet of Things, devices like smartwatches, heart rate monitors, or smartphones easily capture this data.<\/p>\n

Feature extraction is usually performed with a fixed-length sliding window, requiring parameters such as window size and shift.<\/p>\n