Wearable sensors in the smart home environment have been actively developed as assistive systems to detect behavioral anomalies. Smart wearable devices incorprated into daily life can respond immediately to anomalies and process and dispatch important information in real-time. Artificially intelligent technology monitoring of the user’s daily activities and smart home ambience is especially useful in telehealthcare. In this paper, we propose a behavioral activity recognition framework which uses inertial devices (accelerometer and gyroscope) for activity detection within the home environment via multi-fused features and a reweighted genetic algorithm. The procedure begins with the segmentation and framing of data to enable efficient processing of useful information. Features are then extracted and transformed into a matrix. Finally, biogeography-based optimization and a reweighted genetic algorithm are used for the optimization and classification of extracted features. For evaluation, we used the “leave-one-out” cross validation scheme. The results outperformed existing state-of-the-art methods, achieving higher recognition accuracy rates of 88%, 88.75%, and 93.33% compared with CMU-Multi-Modal Activity, WISDM, and IMSB datasets respectively.

中文翻译：

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在智能家居环境中使用加速度计和陀螺仪远程监控日常活动

智能家居环境中的可穿戴传感器已被积极开发为检测行为异常的辅助系统。融入日常生活的智能可穿戴设备可以立即响应异常并实时处理和发送重要信息。对用户日常活动和智能家居氛围的人工智能技术监控在远程医疗中尤其有用。在本文中，我们提出了一种行为活动识别框架，该框架使用惯性设备（加速度计和陀螺仪）通过多重融合特征和重新加权的遗传算法在家庭环境中进行活动检测。该过程从数据的分割和框架开始，以实现对有用信息的有效处理。然后提取特征并将其转换为矩阵。最后，基于生物地理学的优化和重新加权的遗传算法用于对提取的特征进行优化和分类。为了评估，我们使用了“留一法”交叉验证方案。结果优于现有的最先进方法，与 CMU-Multi-Modal Activity、WISDM 和 IMSB 数据集相比，识别准确率分别提高了 88%、88.75% 和 93.33%。