AES E-Library

AES E-Library

A Floor Acoustic Sensor for Fall Classification

The interest in assistive technologies for supporting people at home is constantly increasing, both in academia and industry. In this context the authors propose a fall classification system based on an innovative acoustic sensor that operates similarly to stethoscopes and captures the acoustic waves transmitted through the floor. The sensor is designed to minimize the impact of aerial sounds in recordings, thus allowing a more focused acoustic description of fall events. In this preliminary work, the audio signals acquired by means of the sensor are processed by a fall recognition algorithm based on Mel-Frequency Cepstral Coefficients, Supervectors, and Support Vector Machines to discriminate among different types of fall events. The performance of the algorithm has been evaluated against a specific audio corpus comprising falls of persons and of common objects. The results show the effectiveness of the approach.

AES Convention: Paper Number:
Publication Date:

Click to purchase paper as a non-member or login as an AES member. If your company or school subscribes to the E-Library then switch to the institutional version. If you are not an AES member and would like to subscribe to the E-Library then Join the AES!

This paper costs $33 for non-members and is free for AES members and E-Library subscribers.

Learn more about the AES E-Library

E-Library Location:

Start a discussion about this paper!

AES - Audio Engineering Society