The problem of contactless accurate monitoring of vital signs of patients in psychiatric settings is addressed in this work. Most state-of-the-art radar-based approaches rely on measurements acquired at short distances with the sensor positioned directly in front of the subject’s chest, typically within 1 m, to ensure a strong line-of-sight signal. In contrast, we investigate a substantially more challenging and clinically safer configuration, where multiple frequency-modulated continuous wave (FMCW) radars are mounted at ceiling height in a tilted position, remaining completely out of reach and outside the direct line of sight of patients. An experimental dataset involving 30 participants performing seven activities was collected using two of these ceiling-mounted radars and one reference radar mounted at hip height. A new processing pipeline is proposed with automatic range bin selection and fast autoregressive (AR) spectral estimation, including an approach that leverages all chirps per frame to improve performances with short observation window lengths. Despite the unfavorable sensing geometry and increased radar-to-subject distance, the results demonstrate that ceiling-mounted radars achieve mean absolute respiration rate errors comparable to the hip height reference radar. The best configuration yields a mean absolute error (MAE) of 2.18 respirations per minute (rpm) for participants in a sitting position at distances exceeding 4 m from the ceiling-mounted radars. Moreover, the proposed AR-based method significantly improves estimation accuracy for short windows of 3.1 s, achieving a MAE below 4.3 rpm for all radars and participant positions.

The problem of reconstructing 3D signatures of human activities for monitoring and classification is considered in this work. A method based on data fusion from distributed MIMO (multiple-input multiple-output) radar nodes is developed in order to generate 3D intensity maps and related voxel-wise velocity vectors. The proposed method was evaluated with a dataset collected using three 60 GHz radars and including 7 activities performed by 30 participants. The results show that both static postures and dynamic activities can be captured effectively: consecutive phases of activities/movements can be identified by combining spatial intensity and velocity vectors, and the participant can be localized in the area under test. Furthermore, initial promising classification results of 98.3% macro F1-score are demonstrated in a three-class problem using the proposed 3D intensity maps and velocity vectors as inputs to a Convolutional Neural Network classifier.