  In this paper we present DISC, a dataset of millimeter-wave channel impulse
response measurements for integrated human activity sensing and communication.
This is the first dataset collected with a software-defined radio testbed that
transmits 60 GHz IEEE 802-11ay-compliant packets and estimates the channel
response including reflections of the signal on the moving body parts of
subjects moving in an indoor environment. The provided data contain the
contribution of 7 subjects performing 4 different activities. Differently from
available radar-based millimeter-wave sensing datasets, our measurements are
collected using both uniform packet transmission times and sparse traffic
patterns from real Wi-Fi deployments. Thanks to these unique characteristics,
DISC serves as a multi-purpose benchmarking tool for machine learning-based
human activity recognition, radio frequency gait analysis, and sparse sensing
algorithms for next-generation integrated sensing and communication.
