Abstract
More than a third of elderly fall each year in the United States. It has been shown that the longer the lie on the floor, the poorer is the outcome of the medical intervention.
To reduce delay of the medical intervention, we have developed an acoustic fall detection system (acoustic-FADE) that automatically detects a fall and reports it promptly to the caregiver.
Acoustic-FADE consists of a circular microphone array that captures the sounds in a room. When a sound is detected, acoustic-FADE locates the source, enhances the signal, and classifies it as “fall” or “non fall.”
The sound source is located using the steered response power with phase transform technique, which has been shown to be robust under noisy environments and resilient to reverberation effects.
Signal enhancement is performed by the beam forming technique based on the estimated sound source location.
Height information is used to increase the specificity. The Mel-frequency cepstral coefficient features computed from the enhanced signal are utilized in the classification process.
We have evaluated the performance of acoustic-FADE using simulated fall and non fall sounds performed by three stunt actors trained to behave like elderly under different environmental conditions.
Using a dataset consisting of 120 falls and 120 non falls, the acoustic-FADE achieves 100% sensitivity at a specificity of 97%