  In this paper we propose mmFall - a novel fall detection system, which
comprises of (i) the emerging millimeter-wave (mmWave) radar sensor to collect
the human body's point cloud along with the body centroid, and (ii) a
variational recurrent autoencoder (VRAE) to compute the anomaly level of the
body motion based on the acquired point cloud. A fall is claimed to have
occurred when the spike in anomaly level and the drop in centroid height occur
simultaneously. The mmWave radar sensor provides several advantages, such as
privacycompliance and high-sensitivity to motion, over the traditional sensing
modalities. However, (i) randomness in radar point cloud data and (ii)
difficulties in fall collection/labeling in the traditional supervised fall
detection approaches are the two main challenges. To overcome the randomness in
radar data, the proposed VRAE uses variational inference, a probabilistic
approach rather than the traditional deterministic approach, to infer the
posterior probability of the body's latent motion state at each frame, followed
by a recurrent neural network (RNN) to learn the temporal features of the
motion over multiple frames. Moreover, to circumvent the difficulties in fall
data collection/labeling, the VRAE is built upon an autoencoder architecture in
a semi-supervised approach, and trained on only normal activities of daily
living (ADL) such that in the inference stage the VRAE will generate a spike in
the anomaly level once an abnormal motion, such as fall, occurs. During the
experiment, we implemented the VRAE along with two other baselines, and tested
on the dataset collected in an apartment. The receiver operating characteristic
(ROC) curve indicates that our proposed model outperforms the other two
baselines, and achieves 98% detection out of 50 falls at the expense of just 2
false alarms.
