Improving automatic sound-based fall detection using iVAT clustering and GA-based feature selection

Falls represent an important health problem for older adults. This issue continues to generate interest in the research and development of fall detection systems. In previous work we proposed an acoustic fall detection system (acoustic-FADE) that employs an 8-microphone circular array to automatically detect falls. Acoustic-FADE has achieved encouraging results: 100% detection at 3% false alarm rate in laboratory tests. In this paper, we use a dataset from previous work to investigate how to further improve AFADE performance. To analyze the relationship between fall and non-fall signatures we used the improved visual assessment of tendency (iVAT) clustering algorithm in conjunction with a nearest neighbor based distance to find the most challenging false alarms. Then, we employed a genetic algorithm (GA) framework to perform feature selection and find the mel-frequency cepstral coefficients (MFCC) that improve the classification performance. We found that using only three MFCC coefficients (1, 28, 29) instead of our previous choice (1,2,3,4,5,6) improves the classification performance.