Long-time span acoustic activity analysis from far-field sensors in smart homes

Smart homes for the aging population have recently started attracting the attention of the research community. One of the problems of interest is this of monitoring the activities of daily living (ADLs) of the elderly, in order to help identify critical problems, aiming to improve their protection and general well-being. In this paper, we report on our initial attempts to recognize such activities, based on input from networks of far-field microphones distributed inside the home. We propose two approaches to the problem: The first models the entire activity, which typically covers long time spans, with a single statistical model, for example a hidden Markov model (HMM), a Gaussian mixture model (GMM), or GMM supervectors in conjunction with support vector machines (SVMs). The second is a two-step approach: It first performs acoustic event detection (AED) to locate distinctive events, characteristic of the ADLs, and it is subsequently followed by a postprocessing stage that employs ...

• Type: Conference
• Year: 2009
• Where: ICASSP
• Authors: Jing Huang, Xiaodan Zhuang, Vit Libal, Gerasimos Potamianos
• Keywords: ADL Classification, Gmm Supervector, ICASSP 2009, Signal Processing, Smart Home