  Falls present a significant global public health challenge, especially in
today's aging society, underscoring the importance of developing an effective
fall detection system. Non-invasive radio-frequency (RF) based fall detection
has garnered substantial attention due to its wide coverage and
privacy-preserving nature. Existing RF-based fall detection systems approach
falls as an activity classification problem, assuming that human falls
introduce reproducible patterns to the RF signals. However, we argue that falls
are inherently accidental, making their impact uncontrollable and
unforeseeable. We propose a fundamentally different approach to fall detection
by shifting the focus from directly identifying hard-to-quantify falls to
recognizing normal, repeatable human activities, thus treating falls as
abnormal activities outside the normal activity distribution. We introduce a
self-supervised incremental learning system incorporating FallNet, a deep
neural network that employs unsupervised learning techniques. Our real-time
fall detection system prototype leverages WiFi Channel State Information (CSI)
sensing data and has been extensively tested with 16 human subjects.
