Performance of machine learning classifiers for indoor person localization with capacitive sensors

Accurate tagless indoor person localization is important for several applications, such as assisted living and health monitoring. Machine learning (ML) classifiers can effectively mitigate sensor data variability and noise due to deployment-specific environmental conditions. In this paper, we use experimental data from a capacitive sensor-based indoor human localization system in a 3 m × 3 m room to comparatively analyze the performance of Weka collection ML classifiers. We compare the localization performance of the algorithms, its variation with the training set size, and the algorithm resource requirements for both training and inferring. The results show a large variance between algorithms, with the best accuracy, precision, and recall exceeding 93% and 0.05 m average localization error.