Abstract
One of the key objectives in developing IoT applications is to automatically detect and identify human activities of daily living (ADLs). Mobile phone users are becoming more accepting of sharing data captured by various built-in sensors. Sounds detected by smartphones are processed in this work. We present a hierarchical identification system to recognize ADLs by detecting and identifying certain sounds taking place in a complex audio situation (AS). Three major categories of sound are discriminated in terms of signal duration. These are persistent background noise (PBN), non-impulsive long sounds (NILS), and impulsive sound (IS). We first analyze audio signals in a situation-aware manner and then map the sounds of daily living (SDLs) to ADLs. A new hierarchical audible event (AE) recognition approach is proposed that classifies atomic audible actions (AAs), then computes pre-classified portions of atomic AAs energy in one AE session, and finally marks the maximum-likelihood ADL label as the outcome. Our experiments demonstrate that the proposed hierarchical methodology is effective in recognizing SDLs and, thus, also in detecting ADLs with a remarkable performance for other known baseline systems.