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EnsemConvNet: a deep learning approach for human activity recognition using smartphone sensors for healthcare applications

机译：Ensemconvnet：使用智能手机传感器进行医疗保健应用的深度学习方法

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摘要

Human Activity Recognition (HAR) can be defined as the automatic prediction of the regular human activities performed in our day-to-day life, such as walking, running, cooking, performing office work, etc. It is truly beneficial in the field of medical care services, for example, personal health care assistants, old-age care services, maintaining patient records for future help, etc. Input data to a HAR system can be (a) videos or still images capturing human activities, or (b) time-series data of human body movements while performing the activities taken from sensors in the smart devices like accelerometer, gyroscope, etc. In this work, we mainly focus on the second category of the input data. Here, we propose an ensemble of three classification models, namely CNN-Net, Encoded-Net, and CNN-LSTM, which is named as EnsemConvNet. Each of these classification models is built upon simple ID Convolutional Neural Network (CNN) but differs in terms of the number of dense layers, kernel size used along with other key differences in the architecture. Each model accepts the time series data as a 2D matrix by taking a window of data at a time in order to infer information, which ultimately predicts the type of human activity. Classification outcome of the EnsemConvNet model is decided using various classifier combination methods that include majority voting, sum rule, product rule, and a score fusion approach called adaptive weighted approach. Three benchmark datasets, namely WISDM activity prediction, UniMiB SHAR, MobiAct, are used for evaluating our proposed model. We have compared our EnsemConvNet model with some existing deep learning models such as Multi Headed CNN, hybrid of CNN, and Long Short Term Memory (LSTM) models. The results obtained here establish the supremacy of the EnsemConvNet model over the other mentioned models.
机译：人类活动识别（Har）可以定义为在日常生活中进行的常规人类活动的自动预测，例如步行，跑步，烹饪，执行办公室工作等。它在领域真正有益医疗服务，例如，个人医疗保健助理，养老护理服务，维护患者记录的未来的帮助等。对HAR系统的输入数据可以是（a）视频或仍然图像捕获人类活动，或（b）人体运动的时间序列数据在执行从加速度计，陀螺等的智能设备中的传感器中采取的活动，我们主要关注第二类输入数据。在这里，我们提出了一个三个分类模型，即CNN-Net，编码网络和CNN-LSTM的集合，其被命名为EnsemConvnet。这些分类模型中的每一个都在简单的ID卷积神经网络（CNN）上构建，但在密集层的数量方面不同，内核大小以及架构中的其他关键差异。通过一次拍摄数据窗口以推断信息，每个模型将时间序列数据作为2D矩阵接受为2D矩阵。这最终预测人类活动的类型。 EnsemConvnet模型的分类结果是使用包含多数投票，总和规则，产品规则和称为自适应加权方法的分数融合方法的各种分类器组合方法。三个基准数据集，即WisDM活动预测，UNIMIB Shar，MobiAct，用于评估我们所提出的模型。我们将EnsemConvnet模型与一些现有的深度学习模型进行了比较，如多头CNN，CNN的混合动力和长短短期内存（LSTM）模型。这里获得的结果为其他提到的模型建立了EnsemConvnet模型的至高无上。

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