Recent human activity recognition (HAR) methods, based on on-body inertial sensors, have achieved increasing performance; however, this is at the expense of longer CPU calculations and greater energy consumption. Therefore, these complex models might not be suitable for real-time prediction in mobile systems, e.g., in elder-care support and long-term health-monitoring systems. Here, we present a new method called RapidHARe for real-time human activity recognition based on modeling the distribution of a raw data in a half-second context window using dynamic Bayesian networks. Our method does not employ any dynamic-programming-based algorithms, which are notoriously slow for inference, nor does it employ feature extraction or selection methods. In our comparative tests, we show that RapidHARe is an extremely fast predictor, one and a half times faster than artificial neural networks (ANNs) methods, and more than eight times faster than recurrent neural networks (RNNs) and hidden Markov models (HMMs). Moreover, in performance, RapidHare achieves an F1 score of 94.27\% and accuracy of 98.94\%, and when compared to ANN, RNN, HMM, it reduces the F1-score error rate by 45\%, 65\%, and 63\% and the accuracy error rate by 41\%, 55\%, and 62\%, respectively. Therefore, RapidHARe is suitable for real-time recognition in mobile devices.

中文翻译：

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RapidHARe：一种可计算的廉价方法，可从可穿戴式传感器实时识别人类活动

基于人体惯性传感器的最新人类活动识别（HAR）方法已经实现了性能的提升。但是，这是以更长的CPU计算和更大的能耗为代价的。因此，这些复杂的模型可能不适用于移动系统中的实时预测，例如，在老年护理支持和长期健康监测系统中。在这里，我们基于动态贝叶斯网络在半秒上下文窗口中对原始数据的分布进行建模，提出了一种称为RapidHARe的实时人类活动识别新方法。我们的方法没有使用任何基于动态编程的算法，而这些算法的推理速度很慢，也没有使用特征提取或选择方法。在我们的对比测试中，我们表明RapidHARe是一个非常快速的预测指标，比人工神经网络（ANN）方法快一倍半，比循环神经网络（RNN）和隐马尔可夫模型（HMM）快八倍以上。此外，在性能方面，RapidHare的F1得分达到94.27％，准确度达到98.94％，与ANN，RNN，HMM相比，它的F1得分错误率降低了45％，65％和63。 \％，准确度错误率分别降低41 \％，55 \％和62 \％。因此，RapidHARe适用于移动设备中的实时识别。和63％，准确度错误率分别为41％，55％和62％。因此，RapidHARe适用于移动设备中的实时识别。和63％，准确度错误率分别为41％，55％和62％。因此，RapidHARe适用于移动设备中的实时识别。