  Motion-based fall detection systems are concerned with detecting falls from
vulnerable users, which is typically performed by classifying measurements from
a body-worn inertial measurement unit (IMU) using machine learning. Such
systems, however, necessitate the collection of high-resolution measurements
that may violate users' privacy, such as revealing their gait, activities of
daily living (ADLs), and relative position using dead reckoning. In this paper,
we investigate the application of multi-party computation (MPC) to IMU-based
fall detection for protecting device measurement confidentiality. Our system is
evaluated in a cloud-based setting that precludes parties from learning the
underlying data using multiple, disparate cloud instances deployed in three
geographical configurations. Using a publicly-available dataset, we demonstrate
that MPC-based fall detection from IMU measurements is practical while
achieving state-of-the-art error rates. In the best case, our system executes
in 365.2 milliseconds, which falls well within the required time window for
on-device data acquisition (750ms).
