Vuong Nhu Khue, Liu Yong, Chan Syin, Lau Chiew Tong, Chen Zhenghua, Wu Min, Li Xiaoli
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:5563-5566. doi: 10.1109/EMBC44109.2020.9175472.
Wandering pattern classification is important for early recognition of cognitive deterioration and other health conditions in people with dementia (PWD). In this paper, we leverage the orientation data available on mobile devices to recognize dementia-related wandering patterns. In particular, we propose to use deep learning (DL) with long short-term memory networks (LSTM) as classifiers for detecting travel patterns including direct, pacing, lapping and random. Experimental results on a real dataset collected from 14 subjects show that deep LSTM classifiers perform better than traditional machine learning (ML) classifiers. Our proposed method can thus be potentially used in healthcare applications for dementia related wandering monitoring and management.Clinical Relevance- This demonstrates the potential of using readily available yet non-privacy information to detect dementia-related wandering patterns with high accuracy.
游荡模式分类对于早期识别痴呆症患者（PWD）的认知衰退和其他健康状况至关重要。在本文中，我们利用移动设备上可用的方向数据来识别与痴呆症相关的游荡模式。具体而言，我们建议使用带有长短期记忆网络（LSTM）的深度学习（DL）作为分类器，以检测包括直接、踱步、绕圈和随机在内的出行模式。对从14名受试者收集的真实数据集进行的实验结果表明，深度LSTM分类器的性能优于传统机器学习（ML）分类器。因此，我们提出的方法有可能用于医疗保健应用中的痴呆症相关游荡监测和管理。临床相关性——这证明了利用现成但不涉及隐私的信息来高精度检测与痴呆症相关的游荡模式的潜力。
