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) 是一种神经退行性疾病，具有运动和非运动症状。尽管 PD 具有渐进性，但早期诊断、追踪疾病的自然病程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，即休息时震颤、僵硬和运动迟缓，研究表明 PD 与饮食行为和能量摄入障碍有关。具体而言，PD 与药物诱发的冲动性饮食障碍有关，例如暴饮暴食、与食欲相关的非运动问题（例如体重减轻和/或增加）以及吞咽困难——这些因素与难以完成日常饮食有关-相关任务。在这项工作中，我们引入了板对口（PtM），一个指标，与在用餐过程中用手操作器具将一定量的食物从盘子转移到口中所花费的时间有关。我们提出了一种两步法来实现 PtM 的客观计算。最初，我们使用来自现成智能手表的 3D 加速度和方向速度信号来检测进餐期间发生的咬合时刻和手腕向上微动。之后，我们处理在用餐期间每次检测到的咬合之前出现的向上手部微运动，以估计咬合的 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特征。在结果部分，我们独立地为流程的每一步提供验证，以及使用总共三个数据集展示我们的发现，一个在使用标准化膳食的受控临床环境中收集（来自 7 名健康对照 (HC) 和 21 名 PD 患者的总共 28 次进餐）和两个在-在自由生活条件下野生（分别来自 4 名 HC/10 名 PD 患者的 37 顿饭和来自 3 名 HC/3 名 PD 患者的 629 顿饭）。实验结果显示临床数据集的曲线下面积 (AUC) 为 0.748，野外数据集的曲线下面积 (AUC) 为 0.775/1.000，用于将膳食中进食行为谱分类为 PD 或 HC 组。这是第一项尝试使用在临床和野外环境中收集的可穿戴惯性测量单元 (IMU) 传感器数据来提取 PD 的客观饮食行为指标的工作。一个在使用标准化膳食的受控临床环境中收集（来自 7 个健康对照 (HC) 和 21 个 PD 患者的总共 28 次进餐）和两个在自由生活条件下在野外收集（来自 4 个 HC/10 PD 患者和分别来自 3 名 HC/3 名 PD 患者的 629 顿饭）。实验结果显示临床数据集的曲线下面积 (AUC) 为 0.748，野外数据集的曲线下面积 (AUC) 为 0.775/1.000，用于将膳食中进食行为谱分类为 PD 或 HC 组。这是第一项尝试使用在临床和野外环境中收集的可穿戴惯性测量单元 (IMU) 传感器数据来提取 PD 的客观饮食行为指标的工作。一个在使用标准化膳食的受控临床环境中收集（来自 7 个健康对照 (HC) 和 21 个 PD 患者的总共 28 次进餐）和两个在自由生活条件下在野外收集（来自 4 个 HC/10 PD 患者和分别来自 3 名 HC/3 名 PD 患者的 629 顿饭）。实验结果显示临床数据集的曲线下面积 (AUC) 为 0.748，野外数据集的曲线下面积 (AUC) 为 0.775/1.000，用于将膳食中进食行为谱分类为 PD 或 HC 组。这是第一项尝试使用在临床和野外环境中收集的可穿戴惯性测量单元 (IMU) 传感器数据来提取 PD 的客观饮食行为指标的工作。