w-HAR: An Activity Recognition Dataset and Framework Using Low-Power Wearable Devices.
w-HAR: An Activity Recognition Dataset and Framework Using Low-Power Wearable Devices.
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DOI：
10.3390/s20185356
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发表时间：
2020-09-18
期刊：
影响因子：
--
通讯作者：
Ogras UY
中科院分区：
文献类型：
--
作者：
Bhat G;Tran N;Shill H;Ogras UY
人体活动识别（HAR）由于其在患者康复和运动障碍中的广泛应用而越来越受欢迎。HAR方法通常从收集所考虑活动的传感器数据开始，然后使用数据集开发算法。因此，HAR算法的成功取决于数据集的可用性和质量。HAR的大部分现有工作都使用来自可穿戴设备或智能手机上的惯性传感器的数据来设计HAR算法。然而，惯性传感器表现出高噪声，这使得难以分割数据并对活动进行分类。此外，现有的方法通常不公开其数据，这使得很难或不可能获得HAR方法的比较。为了解决这些问题，我们提出了可穿戴HAR（W-HAR），其中包含来自22个用户的7个活动的标记数据。我们的数据集的独特之处在于整合了来自惯性和可穿戴拉伸传感器的数据，从而提供了两种形式的活动信息。可穿戴拉伸传感器数据使我们能够创建可变长度的片段数据，并确保每个片段包含单个活动。我们还提供了一个HAR框架，使用w-HAR分类的活动。为此，我们首先进行设计空间探索，以选择用于活动分类的神经网络架构。然后，我们使用两个在线学习算法，以适应用户的数据不包括在设计时的分类。在w-HAR数据集上的实验表明，我们的框架达到了95%的准确率，而在线学习算法提高了高达40%的准确率。
Human activity recognition (HAR) is growing in popularity due to its wide-ranging applications in patient rehabilitation and movement disorders. HAR approaches typically start with collecting sensor data for the activities under consideration and then develop algorithms using the dataset. As such, the success of algorithms for HAR depends on the availability and quality of datasets. Most of the existing work on HAR uses data from inertial sensors on wearable devices or smartphones to design HAR algorithms. However, inertial sensors exhibit high noise that makes it difficult to segment the data and classify the activities. Furthermore, existing approaches typically do not make their data available publicly, which makes it difficult or impossible to obtain comparisons of HAR approaches. To address these issues, we present wearable HAR (w-HAR) which contains labeled data of seven activities from 22 users. Our dataset’s unique aspect is the integration of data from inertial and wearable stretch sensors, thus providing two modalities of activity information. The wearable stretch sensor data allows us to create variable-length segment data and ensure that each segment contains a single activity. We also provide a HAR framework to use w-HAR to classify the activities. To this end, we first perform a design space exploration to choose a neural network architecture for activity classification. Then, we use two online learning algorithms to adapt the classifier to users whose data are not included at design time. Experiments on the w-HAR dataset show that our framework achieves 95% accuracy while the online learning algorithms improve the accuracy by as much as 40%.
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DOI：
10.3390/s151229858
发表时间：
2015-12-11
期刊：
Sensors (Basel, Switzerland)
影响因子：
--
作者：
Attal F;Mohammed S;Dedabrishvili M;Chamroukhi F;Oukhellou L;Amirat Y
通讯作者：
Amirat Y
影响因子：
--
作者：
Liang, Nan-Ying;Huang, Guang-Bin;Sundararajan, N.
通讯作者：
Sundararajan, N.
影响因子：
8.7
作者：
Ignatov, Andrey
通讯作者：
Ignatov, Andrey
利用智能手机内置运动传感器进行身体活动识别
影响因子：
2.3
作者：
He, Yi;Li, Ye
通讯作者：
Li, Ye
影响因子：
3.8
作者：
Gyorbiro, Norbert;Fabian, Akos;Homanyi, Gergely
通讯作者：
Homanyi, Gergely