Fall Detection from Audios with Audio Transformers

TL;DR

This paper introduces a privacy-preserving, non-intrusive fall detection system using audio transformers on a mobile robot, achieving high accuracy in bathroom environments and adaptable to other indoor settings.

Contribution

It presents a novel audio transformer-based fall detection method deployed on a robot, addressing privacy and usability issues of existing wearable solutions.

Findings

Achieved 86.73% accuracy in bathroom fall detection

Demonstrated scalability to other indoor environments

Provided a non-wearable, privacy-preserving solution

Abstract

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.