Department of Applied Science for Integrated System Engineering, Kyushu Institute of Technology, Kitakyushu 804-8550, Japan.
Department of Media Intelligent, Osaka University, Ibaraki 567-0047, Japan.
Sensors (Basel). 2020 Jul 8;20(14):3811. doi: 10.3390/s20143811.
Sensor-based human activity recognition has various applications in the arena of healthcare, elderly smart-home, sports, etc. There are numerous works in this field-to recognize various human activities from sensor data. However, those works are based on data patterns that are clean data and have almost no missing data, which is a genuine concern for real-life healthcare centers. Therefore, to address this problem, we explored the sensor-based activity recognition when some partial data were lost in a random pattern. In this paper, we propose a novel method to improve activity recognition while having missing data without any data recovery. For the missing data pattern, we considered data to be missing in a random pattern, which is a realistic missing pattern for sensor data collection. Initially, we created different percentages of random missing data only in the test data, while the training was performed on good quality data. In our proposed approach, we explicitly induce different percentages of missing data randomly in the raw sensor data to train the model with missing data. Learning with missing data reinforces the model to regulate missing data during the classification of various activities that have missing data in the test module. This approach demonstrates the plausibility of the machine learning model, as it can learn and predict from an identical domain. We exploited several time-series statistical features to extricate better features in order to comprehend various human activities. We explored both support vector machine and random forest as machine learning models for activity classification. We developed a synthetic dataset to empirically evaluate the performance and show that the method can effectively improve the recognition accuracy from 80.8% to 97.5%. Afterward, we tested our approach with activities from two challenging benchmark datasets: the human activity sensing consortium (HASC) dataset and single chest-mounted accelerometer dataset. We examined the method for different missing percentages, varied window sizes, and diverse window sliding widths. Our explorations demonstrated improved recognition performances even in the presence of missing data. The achieved results provide persuasive findings on sensor-based activity recognition in the presence of missing data.
基于传感器的人体活动识别在医疗保健、智能家居、运动等领域有广泛的应用。在这个领域，有许多工作致力于从传感器数据中识别各种人体活动。然而，这些工作都是基于干净数据的模式，几乎没有缺失数据，这是现实生活中医疗中心真正关心的问题。因此，为了解决这个问题，我们探索了在传感器数据中存在随机缺失模式时的基于传感器的活动识别。在本文中，我们提出了一种新颖的方法，在没有任何数据恢复的情况下，当部分数据丢失时，提高活动识别的性能。对于缺失数据模式，我们考虑数据以随机模式丢失，这是传感器数据采集的一种现实缺失模式。最初，我们仅在测试数据中创建不同百分比的随机缺失数据，而训练则在高质量数据上进行。在我们提出的方法中，我们明确地在原始传感器数据中随机引入不同百分比的缺失数据，以便使用缺失数据训练模型。使用缺失数据进行学习，可以增强模型在测试模块中对具有缺失数据的各种活动进行分类时对缺失数据的调节能力。这种方法证明了机器学习模型的可行性，因为它可以从相同的领域中学习和预测。我们利用了几个时间序列统计特征来提取更好的特征，以便理解各种人体活动。我们探索了支持向量机和随机森林作为活动分类的机器学习模型。我们开发了一个合成数据集来进行实证评估，结果表明该方法可以有效地将识别准确率从 80.8%提高到 97.5%。之后，我们在两个具有挑战性的基准数据集：人体活动感应联盟（HASC）数据集和单胸佩戴加速度计数据集上测试了我们的方法。我们研究了该方法在不同缺失百分比、不同窗口大小和不同窗口滑动宽度下的性能。我们的探索表明，即使在存在缺失数据的情况下，识别性能也得到了提高。所获得的结果为存在缺失数据的基于传感器的活动识别提供了有说服力的发现。
