The primary objective of this thesis is to explore, develop, and evaluate solutions for building and training SNN-based models that are immediately compatible for deployment on state-of-the-art neuromorphic embedded systems. To extend the study to real use cases, this work specifically addresses the task of Human Activity Recognition (HAR). HAR involves the detection of human actions by analyzing motion data collected from sensors within the Inertial Measurement Unit (IMU) of smartphones and smartwatches. In particular, the thesis focuses on the revised version of the Wireless Sensor Data Mining (WISDM) dataset, comprising recordings from the accelerometer and gyroscope sensors embedded in IMUs of smartphones and smartwatches.