# Privacy-Enhancing Fall Detection from Remote Sensor Data Using Multi-Party Computation

## TL;DR

This paper presents a privacy-preserving fall detection system using multi-party computation on IMU data, achieving real-time performance and state-of-the-art accuracy in a cloud-based setting.

## Contribution

It introduces a novel application of multi-party computation for privacy-preserving fall detection from IMU sensors, demonstrating practicality and high accuracy.

## Key findings

- MPC-based fall detection is practical with real-time performance.
- The system achieves state-of-the-art error rates.
- Execution time is within the required 750ms window.

## Abstract

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).