Acoustic monitoring of daily activities based on hidden Markov model and multidimensional scaling

Abstract

Prolonging independence of seniors improves their quality of life and reduces caring costs. Monitoring health conditions and events increases the safety of the senior and is helpful for independent living. With the maturing of speech technology, it is now possible to monitor activities in daily living space via acoustic signals. The advantages include fewer disturbances and more privacy. This study aims to present an acoustic activated recognition framework to model the daily activity of seniors and provide quantitative evidence of physical functions and social interactivity for living support and the health-related quality of life assessment. Acoustic streams were recorded from designed scenarios within a living space. Fast acoustic pre-segmentation and transcription was implemented using the delta Bayesian information criterion. Hidden Markov model with a developed behavior grammar network was adopted to automatically recognize acoustic events. A Gaussian mixture model combined with multidimensional scaling was proposed for fast speaker diarization. Experimental results show high detection rates in both the recognition of acoustic events and speakers, revealing the feasibility of efficiently modeling daily activities and providing quantitative evidence of health condition and social connection. The case study also shows potential for activity monitoring in the course of caregiving and living independently.