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Deep convolutional neural network classifier for travel patterns using binary sensors

机译：使用二进制传感器的行驶模式的深度卷积神经网络分类器

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摘要

The early detection of dementia is crucial in independent life style of elderly people. Main intention of this study is to propose device-free non-privacy invasive Deep Convolutional Neural Network classifier (DCNN) for Martino-Saltzman's (MS) travel patterns of elderly people living alone using open dataset collected by binary (passive infrared) sensors. Travel patterns are classified as direct, pacing, lapping, or random according to MS model. MS travel pattern is highly related with person's cognitive state, thus can be used to detect early stage of dementia. The dataset was collected by monitoring a cognitively normal elderly resident by wireless passive infrared sensors for 21 months. First, over 70000 travel episodes are extracted from the dataset and classified by MS travel pattern classifier algorithm for the ground truth. Later, 12000 episodes (3000 for each pattern) were randomly selected from the total episodes to compose training and testing dataset. Finally, DCNN performance was compared with three other classical machine-learning classifiers. The Random Forest and DCNN yielded the best classification accuracies of 94.48% and 97.84%, respectively. Thus, the proposed DCNN classifier can be used to infer dementia through travel pattern matching.
机译：老年痴呆症的早期发现对于老年人的独立生活方式至关重要。这项研究的主要目的是使用二进制（无源红外）传感器收集的开放数据集，为Martino-Saltzman（MS）独自生活的老年人的旅行模式提出一种无设备的非隐私侵入性深层卷积神经网络分类器（DCNN）。根据MS模型，出行方式分为直接，起搏，研磨或随机。 MS旅行模式与人的认知状态高度相关，因此可用于检测痴呆的早期阶段。该数据集是通过使用无线无源红外传感器监视居住在认知正常的老年人中21个月而收集的。首先，从数据集中提取了7万多个旅行情节，并通过MS旅行模式分类器算法对地面真相进行了分类。之后，从总情节中随机选择12000个情节（每个模式3000个）以构成训练和测试数据集。最后，将DCNN的性能与其他三个经典的机器学习分类器进行了比较。随机森林和DCNN的最佳分类精度分别为94.48％和97.84％。因此，提出的DCNN分类器可用于通过旅行模式匹配来推断痴呆。

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