Human activity recognition (HAR) can be exploited to great benefits in many applications, including elder care, health care, rehabilitation, entertainment, and monitoring. Many existing techniques, such as deep learning, have been developed for specific activity recognition, but little for the recognition of the transitions between activities. This work proposes a deep learning based scheme that can recognize both specific activities and the transitions between two different activities of short duration and low frequency for health care applications. In this work, we first build a deep convolutional neural network (CNN) for extracting features from the data collected by sensors. Then, the long short-term memory (LTSM) network is used to capture long-term dependencies between two actions to further improve the HAR identification rate. By combing CNN and LSTM, a wearable sensor based model is proposed that can accurately recognize activities and their transitions. The experimental results show that the proposed approach can help improve the recognition rate up to 95.87% and the recognition rate for transitions higher than 80%, which are better than those of most existing similar models over the open HAPT dataset.

中文翻译：

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基于可穿戴传感器的人类活动识别的混合深度学习技术

人类活动识别（HAR）可以在许多应用中得到极大利用，包括养老，保健，康复，娱乐和监视。已经开发出许多现有技术（例如深度学习）用于特定的活动识别，但是很少用于识别活动之间的转换。这项工作提出了一种基于深度学习的方案，该方案可以识别特定活动以及用于医疗保健应用的短期和低频两种不同活动之间的过渡。在这项工作中，我们首先建立一个深度卷积神经网络（CNN），用于从传感器收集的数据中提取特征。然后，使用长短期记忆（LTSM）网络捕获两个动作之间的长期依赖关系，以进一步提高HAR识别率。通过组合CNN和LSTM，提出了一种基于可穿戴传感器的模型，该模型可以准确识别活动及其过渡。实验结果表明，所提出的方法可以帮助提高识别率达95.87％，对过渡的识别率高于80％，优于开放HAPT数据集​​上大多数现有相似模型的识别率。