Enhancing health monitoring in smart offices: a multi-layered digital twin approach
Ankush Manocha et al.
What the paper says
Sedentary nature of office work contributes to a range of physical health issues, including obesity, which can result from prolonged inactivity, and cardiovascular diseases, linked to heightened risk factors associated with a lack of movement. Furthermore, extended periods of sitting can lead to musculoskeletal disorders, causing discomfort and injuries related to poor posture and ergonomics. Collectively, these factors underscore the profound negative impact of sedentary behavior in the workplace on overall well-being. To address these issues, this study proposes a multi-layered digital twin (DT) system for remote healthcare monitoring in a smart office setting. The suggested approach thoroughly investigates various office-related actions in a DT environment, rating their criticality to estimate potential health consequences. By mining temporal instances of these events, a Physiological Risk Index (PRI) is derived, supporting a predictive healthcare framework capable of generating automated alerts during health emergencies. Furthermore, the time-based data module is designed to assist healthcare practitioners in making better decisions by providing precise information about significant occurrences. The system's usability and efficacy are demonstrated by testing it against two challenging datasets obtained from internet repositories. The findings indicate that the proposed approach is both efficient and effective in creating a comprehensive medical system.
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.