Multimedia Understanding Group, Information Processing Laboratory, Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece.
Innovative Use of Mobile Phones to Promote Physical Activity and Nutrition Across the Lifespan (the IMPACT) Research Group, Department of Biosciences and Nutrition, Karolinska Institutet, Stockholm, Sweden.
Sci Rep. 2021 Jan 15;11(1):1632. doi: 10.1038/s41598-020-80394-y.
Parkinson's disease (PD) is a neurodegenerative disorder with both motor and non-motor symptoms. Despite the progressive nature of PD, early diagnosis, tracking the disease's natural history and measuring the drug response are factors that play a major role in determining the quality of life of the affected individual. Apart from the common motor symptoms, i.e., tremor at rest, rigidity and bradykinesia, studies suggest that PD is associated with disturbances in eating behavior and energy intake. Specifically, PD is associated with drug-induced impulsive eating disorders such as binge eating, appetite-related non-motor issues such as weight loss and/or gain as well as dysphagia-factors that correlate with difficulties in completing day-to-day eating-related tasks. In this work we introduce Plate-to-Mouth (PtM), an indicator that relates with the time spent for the hand operating the utensil to transfer a quantity of food from the plate into the mouth during the course of a meal. We propose a two-step approach towards the objective calculation of PtM. Initially, we use the 3D acceleration and orientation velocity signals from an off-the-shelf smartwatch to detect the bite moments and upwards wrist micromovements that occur during a meal session. Afterwards, we process the upwards hand micromovements that appear prior to every detected bite during the meal in order to estimate the bite's PtM duration. Finally, we use a density-based scheme to estimate the PtM durations distribution and form the in-meal eating behavior profile of the subject. In the results section, we provide validation for every step of the process independently, as well as showcase our findings using a total of three datasets, one collected in a controlled clinical setting using standardized meals (with a total of 28 meal sessions from 7 Healthy Controls (HC) and 21 PD patients) and two collected in-the-wild under free living conditions (37 meals from 4 HC/10 PD patients and 629 meals from 3 HC/3 PD patients, respectively). Experimental results reveal an Area Under the Curve (AUC) of 0.748 for the clinical dataset and 0.775/1.000 for the in-the-wild datasets towards the classification of in-meal eating behavior profiles to the PD or HC group. This is the first work that attempts to use wearable Inertial Measurement Unit (IMU) sensor data, collected both in clinical and in-the-wild settings, towards the extraction of an objective eating behavior indicator for PD.
帕金森病（PD）是一种具有运动和非运动症状的神经退行性疾病。尽管 PD 具有进行性特征，但早期诊断、跟踪疾病的自然史和测量药物反应是决定受影响个体生活质量的主要因素。除了常见的运动症状，如静止时震颤、僵硬和运动迟缓外，研究表明 PD 与进食行为和能量摄入的紊乱有关。具体来说，PD 与药物引起的冲动性进食障碍有关，如暴食症，与食欲相关的非运动问题，如体重减轻和/或增加，以及吞咽困难，这些都与完成日常与进食相关的任务有关。在这项工作中，我们引入了Plate-to-Mouth（PtM），它与手操作餐具将食物从盘子转移到嘴里的时间有关。我们提出了一种两步法来客观地计算 PtM。首先，我们使用市售智能手表的 3D 加速度和方向速度信号来检测进餐过程中的咬口时刻和向上的手腕微运动。然后，我们处理进餐过程中每次检测到咬口之前出现的向上的手微运动，以估计咬口的 PtM 持续时间。最后，我们使用基于密度的方案来估计 PtM 持续时间分布，并形成受试者的进餐行为轮廓。在结果部分，我们独立验证了每个步骤的有效性，并使用总共三个数据集展示了我们的发现，其中一个数据集是在使用标准化膳食的受控临床环境中收集的（来自 7 名健康对照者（HC）和 21 名 PD 患者的 28 个餐次），另外两个数据集是在自然环境下收集的（来自 4 名 HC/10 名 PD 患者的 37 个餐次和 3 名 HC/3 名 PD 患者的 629 个餐次）。实验结果显示，临床数据集的曲线下面积（AUC）为 0.748，而自然环境数据集的 AUC 为 0.775/1.000，用于将进餐行为轮廓分类为 PD 或 HC 组。这是首次尝试使用可穿戴惯性测量单元（IMU）传感器数据，无论是在临床还是自然环境中收集，以提取 PD 的客观进食行为指标。
