Nowadays, wearable technology can enhance physical human life-log routines by shifting goals from merely counting steps to tackling significant healthcare challenges. Such wearable technology modules have presented opportunities to acquire important information about human activities in real-life environments. The purpose of this paper is to report on recent developments and to project future advances regarding wearable sensor systems for the sustainable monitoring and recording of human life-logs. On the basis of this survey, we propose a model that is designed to retrieve better information during physical activities in indoor and outdoor environments in order to improve the quality of life and to reduce risks. This model uses a fusion of both statistical and non-statistical features for the recognition of different activity patterns using wearable inertial sensors, i.e., triaxial accelerometers, gyroscopes and magnetometers. These features include signal magnitude, positive/negative peaks and position direction to explore signal orientation changes, position differentiation, temporal variation and optimal changes among coordinates. These features are processed by a genetic algorithm for the selection and classification of inertial signals to learn and recognize abnormal human movement. Our model was experimentally evaluated on four benchmark datasets: Intelligent Media Wearable Smart Home Activities (IM-WSHA), a self-annotated physical activities dataset, Wireless Sensor Data Mining (WISDM) with different sporting patterns from an IM-SB dataset and an SMotion dataset with different physical activities. Experimental results show that the proposed feature extraction strategy outperformed others, achieving an improved recognition accuracy of 81.92%, 95.37%, 90.17%, 94.58%, respectively, when IM-WSHA, WISDM, IM-SB and SMotion datasets were applied.

中文翻译：

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身体生活日志活动检测系统中加速度计和陀螺仪测量的研究

如今，可穿戴技术可以通过将目标从仅计算步骤转变为应对重大医疗保健挑战来增强人类的生活日志程序。此类可穿戴技术模块为获取有关现实环境中人类活动的重要信息提供了机会。本文的目的是报告有关可穿戴传感器系统的最新进展，并预测未来的发展，以可持续监测和记录人类生活日志。在此调查的基础上，我们提出了一个旨在在室内和室外环境中进行体育活动期间检索更好信息的模型，以改善生活质量并降低风险。该模型使用统计和非统计特征的融合，通过可穿戴惯性传感器（即三轴加速度计，陀螺仪和磁力计）识别不同的活动模式。这些功能包括信号幅度，正/负峰值和位置方向，以探索信号方向的变化，位置差异，时间变化和坐标之间的最佳变化。这些特征由遗传算法处理，用于惯性信号的选择和分类，以学习和识别异常的人体运动。我们的模型在以下四个基准数据集中进行了实验评估：智能媒体可穿戴式智能家居活动（IM-WSHA），自我注释的体育活动数据集，来自IM-SB数据集和SMotion数据集具有不同体育活动的具有不同运动模式的无线传感器数据挖掘（WISDM）。实验结果表明，在使用IM-WSHA，WISDM，IM-SB和SMotion数据集的情况下，所提出的特征提取策略性能优于其他方法，分别达到了81.92％，95.37％，90.17％，94.58％的识别精度。