论文标题
老年人流浪的卷积神经网络在室内场景中
Convolutional Neural Network for Elderly Wandering Prediction in Indoor Scenarios
论文作者
论文摘要
这项工作提出了一种方法，可以从房屋周围的非侵入性室内传感器中收集的路径数据中检测阿尔茨海默氏症患者的流浪活动。由于缺乏足够的数据，我们使用自己的开发应用程序手动生成了220条路径的数据集。文献中的流浪模式通常通过视觉特征（例如循环或随机运动）来识别，因此我们的数据集被转换为图像并增强。神经网络模型上使用了卷积层，因为它们倾向于找到良好的结果查找模式，尤其是在图像上。用生成的数据训练了卷积神经网络模型，并在我们的10个样本验证slice上获得了75％的F1分数（精度和召回之间的关系），召回60％，精度为100％
This work proposes a way to detect the wandering activity of Alzheimer's patients from path data collected from non-intrusive indoor sensors around the house. Due to the lack of adequate data, we've manually generated a dataset of 220 paths using our own developed application. Wandering patterns in the literature are normally identified by visual features (such as loops or random movement), thus our dataset was transformed into images and augmented. Convolutional layers were used on the neural network model since they tend to have good results finding patterns, especially on images. The Convolutional Neural Network model was trained with the generated data and achieved an f1 score (relation between precision and recall) of 75%, recall of 60%, and precision of 100% on our 10 sample validation slice
