An Elderly - Care System Based on Sound Analysis
Martin FreÅ¡er, Igor KoÅ¡ir, Violeta Mirchevska, Mitja LuÅ¡trek
Signals and Telecommunication Journal, Vol. 8, No. 2, 2019.
DOI: https://doi.org/10.6025/stj/2019/8/2/54-59
Link: http://www.dline.info/stj/fulltext/v8n2/stjv8n2_2.pdf
Abstract:
This paper proposes an elderly-care system, which uses a single sensing device installed in the userâ€™s home, primarily based on a microphone. We present preliminary results on human activity recognition from sound data. The recognition is based on 19 types of sound features, such as spectral centroid, zero crossings, Melfrequency cepstrum coefficients (MFCC) and linear predictive coding (LPC). We distinguished between 6 classes: sleep, exercise, work, eating, home chores and home leisure. We evaluated the recognition accuracy using 4 supervised learning algorithms. The highest accuracy, obtained using support vector machines, was 76%.