PDF(Pubmed)
Abstract：
BACKGROUND: The accuracy of movement determination software in current activity trackers is insufficient for scientific applications, which are also not open-source.
OBJECTIVE: To address this issue, we developed an accurate, trainable, and open-source smartphone-based activity-tracking toolbox that consists of an Android app (HumanActivityRecorder) and 2 different deep learning algorithms that can be adapted to new behaviors.
METHODS: We employed a semisupervised deep learning approach to identify the different classes of activity based on accelerometry and gyroscope data, using both our own data and open competition data.
RESULTS: Our approach is robust against variation in sampling rate and sensor dimensional input and achieved an accuracy of around 87% in classifying 6 different behaviors on both our own recorded data and the MotionSense data. However, if the dimension-adaptive neural architecture model is tested on our own data, the accuracy drops to 26%, which demonstrates the superiority of our algorithm, which performs at 63% on the MotionSense data used to train the dimension-adaptive neural architecture model.
CONCLUSIONS: HumanActivityRecorder is a versatile, retrainable, open-source, and accurate toolbox that is continually tested on new data. This enables researchers to adapt to the behavior being measured and achieve repeatability in scientific studies.
OBJECTIVE: To address this issue, we developed an accurate, trainable, and open-source smartphone-based activity-tracking toolbox that consists of an Android app (HumanActivityRecorder) and 2 different deep learning algorithms that can be adapted to new behaviors.
METHODS: We employed a semisupervised deep learning approach to identify the different classes of activity based on accelerometry and gyroscope data, using both our own data and open competition data.
RESULTS: Our approach is robust against variation in sampling rate and sensor dimensional input and achieved an accuracy of around 87% in classifying 6 different behaviors on both our own recorded data and the MotionSense data. However, if the dimension-adaptive neural architecture model is tested on our own data, the accuracy drops to 26%, which demonstrates the superiority of our algorithm, which performs at 63% on the MotionSense data used to train the dimension-adaptive neural architecture model.
CONCLUSIONS: HumanActivityRecorder is a versatile, retrainable, open-source, and accurate toolbox that is continually tested on new data. This enables researchers to adapt to the behavior being measured and achieve repeatability in scientific studies.
摘要：
背景：当前活动跟踪器中的运动确定软件的准确性不足以用于科学应用，它们也不是开源的。
目标：为了解决这个问题，我们开发了一种精确的，可训练,以及基于智能手机的开源活动跟踪工具箱，该工具箱由一个Android应用程序（HumanActivityRecorder）和2种可以适应新行为的不同深度学习算法组成。
方法：我们采用了一种半监督深度学习方法，基于加速度测量和陀螺仪数据来识别不同类别的活动。使用我们自己的数据和开放的竞争数据。
结果：我们的方法对采样率和传感器尺寸输入的变化具有鲁棒性，在对我们自己记录的数据和MotionSense数据的6种不同行为进行分类时，准确率约为87％。然而,如果在我们自己的数据上测试维度自适应神经架构模型，准确率下降到26%，这证明了我们算法的优越性，它对用于训练维度自适应神经架构模型的MotionSense数据的执行率为63%。
结论：HumanActivityRecorder是一种多功能，可重新训练,开源,和精确的工具箱，不断测试新的数据。这使研究人员能够适应被测量的行为，并在科学研究中实现可重复性。
目标：为了解决这个问题，我们开发了一种精确的，可训练,以及基于智能手机的开源活动跟踪工具箱，该工具箱由一个Android应用程序（HumanActivityRecorder）和2种可以适应新行为的不同深度学习算法组成。
方法：我们采用了一种半监督深度学习方法，基于加速度测量和陀螺仪数据来识别不同类别的活动。使用我们自己的数据和开放的竞争数据。
结果：我们的方法对采样率和传感器尺寸输入的变化具有鲁棒性，在对我们自己记录的数据和MotionSense数据的6种不同行为进行分类时，准确率约为87％。然而,如果在我们自己的数据上测试维度自适应神经架构模型，准确率下降到26%，这证明了我们算法的优越性，它对用于训练维度自适应神经架构模型的MotionSense数据的执行率为63%。
结论：HumanActivityRecorder是一种多功能，可重新训练,开源,和精确的工具箱，不断测试新的数据。这使研究人员能够适应被测量的行为，并在科学研究中实现可重复性。
