ETH Zurich, Department of Information Technology and Electrical Engineering, 8092 Zurich, Switzerland.
Sensors (Basel). 2019 Feb 10;19(3):714. doi: 10.3390/s19030714.
Activity recognition using off-the-shelf smartwatches is an important problem in humanactivity recognition. In this paper, we present an end-to-end deep learning approach, able to provideprobability distributions over activities from raw sensor data. We apply our methods to 10 complexfull-body exercises typical in CrossFit, and achieve a classification accuracy of 99.96%. We additionallyshow that the same neural network used for exercise recognition can also be used in repetitioncounting. To the best of our knowledge, our approach to repetition counting is novel and performswell, counting correctly within an error of 1 repetitions in 91% of the performed sets.
使用现成的智能手表进行活动识别是人体活动识别中的一个重要问题。在本文中，我们提出了一种端到端的深度学习方法，能够从原始传感器数据中提供活动的概率分布。我们将我们的方法应用于 10 种典型的 CrossFit 中的复杂全身运动，并实现了 99.96%的分类准确率。我们还表明，用于运动识别的相同神经网络也可用于重复计数。据我们所知，我们的重复计数方法是新颖的，并且在 91%的执行集中，其计数误差在 1 次以内。
