An IoT solution that detects human physical activities to support health assessment based on sensors and supervised learning algorithms

At the moment, human physical activities can be detected by activity recognition systems to help with health status evaluation. One of the key issues in human activity recognition (HAR) systems is cost, speed, and accuracy. With the advancement of mobile devices and increasingly sophisticated microprocessors, along with the integration of various sensors including accelerometers, human activity recognition has become easier and faster. However, the accuracy of activity classification based on accelerometer data depends on many different factors, which can lead to potential deviations in results. Through various experiments, we have found that selecting optimal features is crucial for the performance of classification. In this paper, we propose a simple feature set that yields very promising results with an accuracy of up to 99%.