A low-cost real-time IoT human activity recognition system based on wearable sensor and the supervised learning algorithms

Activity recognition systems can detect human physical activities to support the assessment of health conditions. Among approaches of activity recognition systems were researched and implemented, the wearable systems based on accelerometers and machine learning classifiers offer one of the most viable solutions. These systems are cheap, comfortable, easy to use, with high recognition accuracy. The major challenge in this classification problem is required directly performed in a low-performance microcontroller. In this manuscript, an optimal time frame of an activity, a feature set, and a simple machine learning model were proposed to build a low-cost and responsive recognition system in real-time. The proposed device was verified on both public data and our experiment data. An excellent recognition rate resulted in 99.2% on the recorded dataset for four critical daily activities (standing, sitting, running, and walking).