Evaluating Incremental Machine Learning for Smart Home Adaptation with Embedded Systems

Sammanfattning: The combination of machine learning on embedded systems has quickly increased throughout the years. Subsets like TinyML have become an integral part of how embedded systems implement machine learning. The field has evolved quickly, and TinyOL is an emerging subset that redefines what is possible with embedded systems. This report presents a comparison of how a neural network that implements incremental online learning learns and adapts how to do simple tasks in home automation. The comparison is done with another system, mainly a proportional-integral-derivative (PID). The systems were tasked with controlling an LED lightning threshold based on feedback from the user. The systems were evaluated based on their mean absolute error (MAE) and accuracy in predicting the output of the LED lighting system. The MAE values of both systems were compared for different target outputs and threshold values, and the accuracy was calculated by comparing the number of successful iterations to the total number of iterations. The results show that the neural network has an accuracy of 50\% when a learning rate of 0.2 is used, 97.5\% when a learning rate of 0.5 is used, and 47.5\% when a learning rate of 1.0 is used. The PID control system had accuracy values of 45\% when using an adaption rate of 0.2, 47.5\% when using an adaption rate of 0.5, and 90\% when using an adaption rate of 1.0. The neural network also showcased a lower median MAE for every test conducted. The study provides insights into the effectiveness of different control systems and can inform the development of similar systems in the future.