Centre of Health Informatics and Technology, The Mærsk Mc-Kinney Møller Institute, University of Southern Denmark, 5230 Odense, Denmark.
Sensors (Basel). 2023 Jan 6;23(2):679. doi: 10.3390/s23020679.
Walking ability of elderly individuals, who suffer from walking difficulties, is limited, which restricts their mobility independence. The physical health and well-being of the elderly population are affected by their level of physical activity. Therefore, monitoring daily activities can help improve the quality of life. This becomes especially a huge challenge for those, who suffer from dementia and Alzheimer's disease. Thus, it is of great importance for personnel in care homes/rehabilitation centers to monitor their daily activities and progress. Unlike normal subjects, it is required to place the sensor on the back of this group of patients, which makes it even more challenging to detect walking from other activities. With the latest advancements in the field of health sensing and sensor technology, a huge amount of accelerometer data can be easily collected. In this study, a Machine Learning (ML) based algorithm was developed to analyze the accelerometer data collected from patients with walking difficulties, who live in one of the municipalities in Denmark. The ML algorithm is capable of accurately classifying the walking activity of these individuals with different walking abnormalities. Various statistical, temporal, and spectral features were extracted from the time series data collected using an accelerometer sensor placed on the back of the participants. The back sensor placement is desirable in patients with dementia and Alzheimer's disease since they may remove visible sensors to them due to the nature of their diseases. Then, an evolutionary optimization algorithm called Particle Swarm Optimization (PSO) was used to select a subset of features to be used in the classification step. Four different ML classifiers such as k-Nearest Neighbors (kNN), Random Forest (RF), Stacking Classifier (Stack), and Extreme Gradient Boosting (XGB) were trained and compared on an accelerometry dataset consisting of 20 participants. These models were evaluated using the leave-one-group-out cross-validation (LOGO-CV) technique. The Stack model achieved the best performance with average sensitivity, positive predictive values (precision), F-score, and accuracy of 86.85%, 93.25%, 88.81%, and 93.32%, respectively, to classify walking episodes. In general, the empirical results confirmed that the proposed models are capable of classifying the walking episodes despite the challenging sensor placement on the back of the patients, who suffer from walking disabilities.
老年人的步行能力有限，这限制了他们的行动独立性。老年人的身体健康和幸福感受到其身体活动水平的影响。因此，监测日常活动有助于提高生活质量。对于那些患有痴呆症和阿尔茨海默病的人来说，这尤其具有挑战性。因此，养老院/康复中心的工作人员对他们的日常活动和进展进行监测是非常重要的。与正常受试者不同，需要将传感器放置在这群患者的背部，这使得从其他活动中检测步行变得更加具有挑战性。随着健康传感和传感器技术领域的最新进展，可以轻松收集大量的加速度计数据。在这项研究中，开发了一种基于机器学习 (ML) 的算法来分析来自丹麦一个市的有行走困难的患者的加速度计数据。该 ML 算法能够准确地对这些有不同行走异常的个体的行走活动进行分类。从放置在参与者背部的加速度计传感器收集的时间序列数据中提取了各种统计、时间和频谱特征。在患有痴呆症和阿尔茨海默病的患者中，希望采用背部传感器放置，因为由于疾病的性质，他们可能会移除对他们可见的传感器。然后，使用称为粒子群优化 (PSO) 的进化优化算法来选择要在分类步骤中使用的特征子集。在由 20 名参与者组成的加速度计数据集上训练并比较了四种不同的 ML 分类器，例如 k-最近邻 (kNN)、随机森林 (RF)、堆叠分类器 (Stack) 和极端梯度提升 (XGB)。这些模型使用留一分组交叉验证 (LOGO-CV) 技术进行评估。Stack 模型的性能最佳，平均灵敏度、阳性预测值 (精度)、F 分数和准确率分别为 86.85%、93.25%、88.81%和 93.32%，用于分类行走发作。总的来说，实证结果证实，即使在患者背部有挑战性的传感器放置的情况下，所提出的模型也能够对行走发作进行分类，这些患者患有行走障碍。
