An EMG-based eating behaviour monitoring system with haptic feedback to promote mindful eating

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 need for 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 such a system in combination with real time wristband haptic feedback to facilitate mindful eating. Data collected from 16 participants were used to develop an algorithm for detecting chewing and swallowing. We extracted 18 features from EMG and presented those features to different classifiers. We demonstrated that eating behaviour can be automatically assessed accurately using the EMG-extracted features and a Support Vector Machine (SVM): F1-Score=0.94 for chewing classification, and F1-Score=0.86 for swallowing classification. Based on this algorithm, we developed a system to enable participants to self-moderate their chewing behaviour using haptic feedback. An experiment study was carried out with 20 additional participants showing that participants exhibited a lower rate of chewing when haptic feedback delivered in forms of wristband vibration was used compared to a baseline and non-haptic condition (F (2,38)=58.243, p<0.001). These findings may have major implications for research in eating behaviour, providing key new insights into the impacts of automatic chewing detection and haptic feedback systems on moderating eating behaviour with the aim to improve health outcomes

An EMG-based eating behaviour monitoring system with haptic feedback to promote mindful eating

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

An EMG-based Eating Behaviour Monitoring System with Haptic Feedback to Promote Mindful Eating

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