  Fall detection for the elderly is a well-researched problem with several
proposed solutions, including wearable and non-wearable techniques. While the
existing techniques have excellent detection rates, their adoption by the
target population is lacking due to the need for wearing devices and user
privacy concerns. Our paper provides a novel, non-wearable, non-intrusive, and
scalable solution for fall detection, deployed on an autonomous mobile robot
equipped with a microphone. The proposed method uses ambient sound input
recorded in people's homes. We specifically target the bathroom environment as
it is highly prone to falls and where existing techniques cannot be deployed
without jeopardizing user privacy. The present work develops a solution based
on a Transformer architecture that takes noisy sound input from bathrooms and
classifies it into fall/no-fall class with an accuracy of 0.8673. Further, the
proposed approach is extendable to other indoor environments, besides bathrooms
and is suitable for deploying in elderly homes, hospitals, and rehabilitation
facilities without requiring the user to wear any device or be constantly
"watched" by the sensors.
