Human Activity Recognition (HAR) has attracted significant attention due to its wide range of applications, including ambient assisted living, healthcare monitoring, and smart environments. Traditional wearable sensor-based systems often suffer from user discomfort, compliance issues, and limited robustness. Video-based approaches, while informative, raise privacy concerns and struggle in low-light conditions or at extended ranges. This thesis introduces a Frequency-Modulated Continuous Wave (FMCW) radar-based framework for both multi-class human activity recognition and fall detection, leveraging a 60 GHz radar and multi-dimensional feature maps—namely Range-Doppler, Range-Azimuth, and Range-Elevation. Unlike conventional methods that treat these maps as images, this work directly inputs the multi-dimensional data into learning models, preserving the spatial and temporal structure of the radar signals.