Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China.
University of Science and Technology of China, Hefei 230026, China.
Sensors (Basel). 2021 Jan 20;21(3):692. doi: 10.3390/s21030692.
Human activity recognition (HAR) is essential in many health-related fields. A variety of technologies based on different sensors have been developed for HAR. Among them, fusion from heterogeneous wearable sensors has been developed as it is portable, non-interventional and accurate for HAR. To be applied in real-time use with limited resources, the activity recognition system must be compact and reliable. This requirement can be achieved by feature selection (FS). By eliminating irrelevant and redundant features, the system burden is reduced with good classification performance (CP). This manuscript proposes a two-stage genetic algorithm-based feature selection algorithm with a fixed activation number (GFSFAN), which is implemented on the datasets with a variety of time, frequency and time-frequency domain features extracted from the collected raw time series of nine activities of daily living (ADL). Six classifiers are used to evaluate the effects of selected feature subsets from different FS algorithms on HAR performance. The results indicate that GFSFAN can achieve good CP with a small size. A sensor-to-segment coordinate calibration algorithm and lower-limb joint angle estimation algorithm are introduced. Experiments on the effect of the calibration and the introduction of joint angle on HAR shows that both of them can improve the CP.
人体活动识别（HAR）在许多与健康相关的领域中至关重要。已经开发出了各种基于不同传感器的技术来进行 HAR。其中，基于异构可穿戴传感器的融合已得到发展，因为它便携、非介入且对 HAR 准确。为了在资源有限的情况下实时应用，活动识别系统必须紧凑可靠。这一要求可以通过特征选择（FS）来实现。通过消除不相关和冗余的特征，可以在保持良好分类性能（CP）的同时减轻系统负担。本文提出了一种基于两阶段遗传算法的具有固定激活数的特征选择算法（GFSFAN），该算法在从九项日常生活活动（ADL）的原始时间序列中提取的各种时间、频率和时频域特征的数据集上进行了实现。使用六种分类器来评估从不同 FS 算法中选择的特征子集对 HAR 性能的影响。结果表明，GFSFAN 可以实现小尺寸的良好 CP。介绍了一种传感器到段坐标校准算法和下肢关节角度估计算法。校准和关节角度引入对 HAR 效果的实验表明，这两者都可以提高 CP。
