School of Engineering, University of Kent, Canterbury, CT2 7NZ, UK.
School of Computing, University of Kent, Canterbury, CT2 7NF, UK.
Comput Biol Med. 2022 Oct;149:106068. doi: 10.1016/j.compbiomed.2022.106068. Epub 2022 Aug 30.
Mindless eating, or the lack of awareness of the food we are consuming, has been linked to health problems attributed to unhealthy eating behaviour, including obesity. Traditional approaches used to moderate eating behaviour often rely on inaccurate self-logging, manual observations or bulky equipment. Overall, there is a clear unmet clinical need to develop an intelligent and lightweight system which can automatically monitor eating behaviour and provide feedback. In this paper, we investigate: i) the development of an automated system for detecting eating behaviour using wearable Electromyography (EMG) sensors, and ii) the application of the proposed system combined with real-time wristband haptic feedback to facilitate mindful eating. For this, the collected data from 16 participants were used to develop an algorithm for detecting chewing and swallowing. We extracted 18 features from EMG which were presented to different classifiers, to develop a system enabling participants to self-moderate their chewing behaviour using haptic feedback. An additional experimental study was conducted with 20 further participants to evaluate the effectiveness of eating monitoring and haptic interface in promoting mindful eating. We used a standard validation scheme with a leave-one-participant-out to assess model performance using standard metrics (F1-score). The proposed algorithm automatically assessed eating behaviour accurately using the EMG-extracted features and a Support Vector Machine (SVM): F1-Score = 0.95 for chewing classification, and F1-Score = 0.87 for swallowing classification. The experimental study showed that participants exhibited a lower rate of chewing when haptic feedback was delivered in the form of wristband vibration, compared to a baseline and non-haptic condition (F (2,38) = 58.243, p < .001). These findings may have major implications for research in eating behaviour, providing key insights into the impact of automatic chewing detection and haptic feedback systems on moderating eating behaviour towards improving health outcomes.
无意识进食，或缺乏对所摄入食物的意识，与不健康的饮食行为导致的健康问题有关，包括肥胖。用于调节饮食行为的传统方法通常依赖于不准确的自我记录、手动观察或笨重的设备。总体而言，显然需要开发一种智能且轻便的系统，以自动监测饮食行为并提供反馈。在本文中，我们研究了：i）使用可穿戴肌电图（EMG）传感器开发用于检测饮食行为的自动化系统，以及 ii）将所提出的系统与实时腕带触觉反馈相结合，以促进正念饮食。为此，我们使用 16 名参与者的采集数据来开发用于检测咀嚼和吞咽的算法。我们从 EMG 中提取了 18 个特征，并将其呈现给不同的分类器，以开发一种系统，使参与者能够使用触觉反馈来自我调节咀嚼行为。我们还进行了一项额外的实验研究，其中有 20 名进一步的参与者评估了饮食监测和触觉接口在促进正念饮食方面的有效性。我们使用标准的验证方案，采用留一参与者法（leave-one-participant-out）使用标准指标（F1 分数）评估模型性能。该算法使用从 EMG 中提取的特征和支持向量机（SVM）自动准确地评估饮食行为：用于咀嚼分类的 F1 分数= 0.95，用于吞咽分类的 F1 分数= 0.87。实验研究表明，与基线和非触觉条件相比，当腕带振动形式提供触觉反馈时，参与者的咀嚼速度较低（F (2,38) = 58.243，p < 0.001）。这些发现可能对饮食行为研究具有重大意义，为自动咀嚼检测和触觉反馈系统对调节饮食行为以改善健康结果的影响提供了关键见解。
