The problem of estimating breathing rates and detecting apnea events with radars located at an elevated and tilted position is considered in this paper. This is particularly relevant in psychiatric clinics, where radars (or other sensors) must be installed out of reach of patients. In this work, a feasibility study is presented, using an experimental setup with two 60 GHz Frequency-Modulated Continuous Wave (FMCW) radars placed at 2.7 m height at a tilted angle towards the participants, and one radar at 1 m height looking straight to the participants, who are sitting and lying on the floor. A new comprehensive dataset with 30 participants and 7 activities was collected with this setup. Using phase extraction and filtering, the work presents an apnea detection probability of up to 90 % with an elevated radar, and comparable mean error rates for breathing estimation of below 2 respirations per minute (rpm) for all radars. The results show that the respiration data and apnea detection from all radar positions are comparable. This proves the feasibility of the proposed radar deployment positions, benefiting application fields such as psychiatric care.

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.