Digital Occupational Health: Opportunities and Challenges for the Workplace.

This paper discusses the role of the workplace in digital occupational health as part of an increasingly digitalized working life. Digital occupational health can be considered to consist of at least the following digitalized components: a) occupational health services and data, b) human resource data, c) group-level field data collected from the work environment and employees, and d) individual-level field data collected for personal use only. These data and related processes form a basis for so called data-driven management of occupational health and safety. To collect such data and keep it updated, it is important to pay attention to: a) worker acceptance, b) user friendliness, c) data validity, integrity, and protection, d) adequate resources, and e) ethical and effective use of the data. The current literature suggests that there are promising mobile and wearable devices and eHealth solutions to support worker health. To use them effectively, it is good to pay attention to the implementation process in the workplace. Ultimately, trust and collaboration among all parties are the cornerstones for gaining benefits from digital occupational health.

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.