A Real-Time IoT System and ML algorithms: A Comparative Study

Wearable sensors are frequently used for monitoring physical activities and medical conditions. A variety of sensors are used, such as the accelerometer (ACC), gyroscope (GYR), and magnetometer (MAG), which are often embedded in Inertial Measurement Units (IMU). Data collected from these sensors can be used to identify context or physical activities through contextaware learning methods that apply a variety of learning algorithms. Implementing a real-time system (RTS) that serves a specific application like fall detection or heart condition, for example, is challenging as response time must be within a certain interval. This response time depends on the speed of data collection and transmission as well as the prediction time. Even though a lot of research was done in this area, to the best of our knowledge there is no comparative study based on the criteria we are using here. In this paper, we propose an Edge-based RTS for health-related applications and conduct a comparative study of several Machine Learning Algorithms (MLA) according to four criteria: source of data, sampling period, prediction time (PT), and success rates (SR). Even though MLA demonstrate different behavior toward these criteria, our simulation results showed that it is possible to implement a RTS that can identify or predict accurately physical activities within acceptable time constraints which are application dependant. Simulations were performed on wearable sensors' data that we collected from 24 participants practicing five different physical activities. Our simulation results showed that the Decision Tree algorithm, with SR of 97.93% and PT of 0.17 seconds, outperformed all other algorithms.