智能床中的身份和身份识别与深度多任务学习
Identity and Posture Recognition in Smart Beds with Deep Multitask Learning
摘要：
睡眠姿势分析广泛用于临床患者监测和睡眠研究。早期的研究表明，睡眠姿势对呼吸暂停和压力性溃疡等疾病的症状有很大影响。在这项研究中，我们提出了一个强大的深度学习模型，能够使用从商业压力映射系统获取的公开数据准确地检测受试者及其睡眠姿势。损失函数的组合用于同时区分对象和他们的睡眠姿势。实验结果表明，我们提出的方法可以识别患者和他们在床上的姿势，几乎没有错误的10倍交叉验证计划。此外，我们表明，我们的网络在三种最常见的睡眠姿势类别上的leave-one-subject-out验证过程中面对新受试者时，平均准确率高达99%。我们展示了组合成本函数对其参数的影响，并表明同时学习两个任务可以显着提高性能。最后，我们通过在我们数据集的增强图像上测试我们提出的管道来评估它。所提出的算法最终可用于临床和灵巧家庭环境中，作为与其他可用的自动化患者监护系统的互补工具。
英文摘要：
Sleep posture analysis is widely used for clinical patient monitoring and sleep studies. Earlier research has revealed that sleep posture highly influences symptoms of diseases such as apnea and pressure ulcers. In this study, we propose a robust deep learning model capable of accurately detecting subjects and their sleeping postures using the publicly available data acquired from a commercial pressure mapping system. A combination of loss functions is used to discriminate subjects and their sleeping postures simultaneously. The experimental results show that our proposed method can identify the patients and their in-bed posture with almost no errors in a 10-fold cross-validation scheme. Furthermore, we show that our network achieves an average accuracy of up to 99% when faced with new subjects in a leave-one-subject-out validation procedure on the three most common sleeping posture categories. We demonstrate the effects of the combined cost function over its parameter and show that learning both tasks simultaneously improves performance significantly. Finally, we evaluate our proposed pipeline by testing it over augmented images of our dataset. The proposed algorithm can ultimately be used in clinical and smart home environments as a complementary tool with other available automated patient monitoring systems.
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