School of Information Science and Engineering, Shenyang Ligong University, Shenyang 110180, China.
Science and Technology Development Corporation, Shenyang Ligong University, Shenyang 110180, China.
Sensors (Basel). 2023 Apr 4;23(7):3726. doi: 10.3390/s23073726.
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 (). Three major categories of sound are discriminated in terms of signal duration. These are persistent background noise (), non-impulsive long sounds (), and impulsive sound (). 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 () recognition approach is proposed that classifies atomic audible actions (s), then computes pre-classified portions of atomic s energy in one 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.
开发物联网应用的一个关键目标是自动检测和识别日常生活活动（ADL）。越来越多的智能手机用户愿意分享各种内置传感器捕捉的数据。在这项工作中，处理智能手机检测到的声音。我们提出了一种分层识别系统，通过检测和识别复杂音频环境中发生的某些声音来识别 ADL（）。根据信号持续时间，区分出三大类声音。分别是持续背景噪声（）、非脉冲长音（）和脉冲音（）。我们首先以情境感知的方式分析音频信号，然后将日常生活声音（SDL）映射到 ADL。我们提出了一种新的分层可听事件（）识别方法，该方法对原子可听动作（s）进行分类，然后计算一个会话中原子 s 能量的预分类部分，最后将最大似然 ADL 标签标记为结果。我们的实验表明，所提出的分层方法在识别 SDL 方面非常有效，因此，对于其他已知的基线系统，它在检测 ADL 方面也具有出色的性能。
