At-home exercise monitoring is vital to applications like rehabilitative care and physical therapy. In this work, we use millimeter-wave signal reflections to assess the exercise, where we classify the exercise type by designing a supervised deep learning model, and estimate the number of repetitions by leveraging phase information embedded in the reflections.

The proliferation of smart, ubiquitous devices has inspired many researchers to develop at-home personal documentation systems. One application of such systems is at-home exercise monitoring, which is important for remote healthcare and fitness regimens. This work explores a millimeter-wave (mmWave) wireless sensing based at-home exercise monitoring using commodity devices. We leverage the mmWave signals reflected off a person exercising and design a deep-learning network that uses a combination of CNN and LSTM to classify the activities. We evaluate the performance of our classifier extensively, using several input signal representations.

We spend one-third of our lives sleeping, and sleep quality plays an important role in our overall health. Sleep posture monitoring can help medical professionals prevent negative health outcomes associated with certain sleep postures. In this work, we propose using millimeter-wave wireless signals to classify the sleep posture using a supervised deep learning model and preliminarily evaluate the performance for 7 volunteers and 5 broad classes of postures.