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Human activity recognition provides support to healthcare by monitoring daily activities of patients and elderly people to detect critical events such as fall, change in behaviour, physical fitness, etc. Monitoring of human activities is commonly tackled by various technologies such as video cameras and wearable sensors. However, the use of such sensors shows various fundamental deficiencies and privacy issues. In contrast, radars ensure comfort by their contact-less and non-intrusive properties. Moreover, radar sensors are unaffected by low-light conditions, can sense through concealing objects and also maintain privacy.

We investigate a novel approach towards automatic indoor human activity recognition for patient activities in a hospital room, using deep neural networks for processing high-dimensional radar data.


To facilitate further research, we will release the Patient Activity Recognition with Radar sensors (PARrad) data set, which is a subset of the original data set. The original data sets were constructed using two Frequency Modulated Continuous Wave (FMCW) radars with a center frequency of 77GHz and 60GHz, in two different types of environments: a synthetic hospital room (Homelab) and a real-life hospital room (Hospital). In total the data sets contain 21569 activities, subdivided into 8210 Homelab activities and 13359 Hospital activities. Our datasets thus contain a total of 22 hours of effectively annotated activity data distributed over 14 original classes.


A PARrad data set is coming soon