Hardware Deployable Edge AI Solution for Posture Classification Using mmWave Radar and Low- Computational Machine Learning Model - Pratap Singh, Yash; Gupta, Aham; Chaudhary, Devansh; Wajid, Mohd; Srivastava, Abhishek; Mahajan, Pranjal (IEEE sensors journal, 2024)
Identifying correct human postures is crucial in areas, such as patient care, in hospitals. However, the traditional vision-based methods widely used for this purpose raise privacy concerns for the subject, and the other wearable sensor-based approaches are impractical for real-world scenarios. In this article, we propose a contactless, privacy-conscious, and memory-efficient posture classification system based on a millimeter-wave (mmWave) radar. This system utilizes 3-D point-cloud data captured using Texas Instrument's IWR1843BOOST frequency-modulated continuous-wave (FMCW) radar module to classify the posture of the subject. Two types of datasets are extracted from these radar data: 1) image dataset derived from the isometric view of the point-cloud data and 2) spatial coordinates dataset also extracted from the point-cloud data. A low-computational tiny machine learning (TinyML) model is employed on the datasets for efficient implementation on embedded hardware, Raspberry Pi 3 B+. The proposed model's parameters were quantized to 8 bits (int8), which accurately classify four postures, i.e., standing, sitting, lying, and bending, with an accuracy of 98.97% for the image data. However, to make it more computationally efficient, the int8 quantized TinyML model was trained on the spatial coordinates dataset, giving an accuracy of 96.12%. This highlights the efficiency and effectiveness of our proposed lightweight model that can be deployed on edge devices for real-world applications.