School of Electronic and Electrical Engineering, College of IT Engineering, Kyungpook National University, 80 Daehakro, Bukgu, Daegu 41566, Republic of Korea.
Sensors (Basel). 2024 Oct 4;24(19):6435. doi: 10.3390/s24196435.
To create an effective Ambient Assisted Living (AAL) system that supports the daily activities of patients or the elderly, it is crucial to accurately detect and differentiate user actions to determine the necessary assistance. Traditional intrusive methods, such as wearable or object-attached devices, can interfere with the natural behavior of patients and may lead to resistance. Furthermore, non-intrusive systems that rely on video or sound data processed by servers or the cloud can generate excessive data traffic and raise concerns about the security of personal information. In this study, we developed an edge-based real-time system for detecting Activities of Daily Living (ADL) using ambient noise. Additionally, we introduced an online post-processing method to enhance classification performance and extract activity events from noisy sound in resource-constrained environments. The system, tested with data collected in a living space, achieved high accuracy in classifying ADL-related behaviors in continuous events and successfully generated user activity logs from time-series sound data, enabling further analyses such as ADL assessments. Future work will focus on enhancing detection accuracy and expanding the range of detectable behaviors by integrating the activity logs generated in this study with additional data sources beyond sound.
为了创建一个有效的环境辅助生活（AAL）系统，支持患者或老年人的日常活动，准确检测和区分用户行为以确定所需的帮助至关重要。传统的侵入式方法，如可穿戴或物体附着设备，可能会干扰患者的自然行为，并可能导致抵触。此外，依赖服务器或云处理的视频或声音数据的非侵入式系统会产生过多的数据流量，并引发对个人信息安全的担忧。在这项研究中，我们开发了一种基于边缘的实时系统，用于使用环境噪声检测日常生活活动（ADL）。此外，我们引入了一种在线后处理方法，以增强分类性能并从资源受限环境中的嘈杂声音中提取活动事件。该系统在生活空间中收集的数据上进行了测试，在连续事件中对 ADL 相关行为的分类达到了很高的准确性，并成功地从时间序列声音数据生成了用户活动日志，从而可以进行进一步的分析，如 ADL 评估。未来的工作将重点关注通过将本研究中生成的活动日志与声音以外的其他数据源集成来提高检测准确性和扩展可检测行为的范围。
