mm-EmoIoT: Decoding Emotions from Upper Body Gestures via mm-wave Sensing for Smart Home Applications

This paper introduces a novel mm-wave radar-based system for detecting upper-body emotional gestures using micro-Doppler(m-D) signatures and motion profiling to enable emotion-driven IoT device control in smart homes. The system recognizes five key gestures,single thumbs up, double thumbs up, single thumbs down, double thumbs down, and the OK gesture, each mapped to emotional states such as approval, disapproval, and strong emotional responses. By leveraging m-D features and motion profiling, the system captures both gestures’ dynamic and temporal characteristics to infer the corresponding emotional context with high precision. Motion profiling is used to analyze gesture velocity, acceleration, and duration, providing insights into each motion’s intensity and emotional significance. Combined with m-D data, the system achieves robust emotion detection, even under challenging conditions such as low-light conditions or occlusions. The proposed framework is computationally efficient, has a complexity of O(N log N + M ) and delivers an average accuracy in emotion recognition. The system demonstrates low latency (ms) and seamless integration with IoT devices, enabling real-time smart home automation based on emotional cues.