Description
Background: Chronic diseases account for approximately 75% of total deaths worldwide. Wearable sensors and point-of-care technology can play a significant role in preventing, monitoring, and screening people for these diseases in daily living and working environments. Modern "smart" office spaces are currently optimized to reduce operational costs. However, they often neglect workers' needs based on personal health and environmental factors. Advancements in technology and electronics have enabled the development of various types of wearable sensors that can collect data such as activity, light exposure and air quality. Sensors that can interact and communicate with each other is called a "Cyber Physical System".We aim to develop a setup that integrates technologies in a Cyber Physical Human System (CPHS). This innovative system will simultaneously monitor an individual's health and the surrounding environment. By integrating technologies that measure human health parameters such as activity, sleepiness and social interaction, and environmental conditions like air quality, noise and light, we can optimize the workplace for worker satisfaction and performance.
The primary objective of this study is to create a CPHS capable of assessing multiple environmental factors alongside personal health metrics, ultimately enhancing the overall well-being of office workers.
Method: This study will employ an A-B-A design. Each phase will take 2 weeks, and will include 10 to 20 healthy volunteers. Individual data between phases can be compared to see the differences. The first A-phase will be the baseline phase, during which data will be collected to establish a baseline. The collected data will relate to both environmental and human parameters. Environmental parameters include temperature, humidity, CO2 levels, light, and sound exposure. Human parameters encompass social interaction, stress levels, sleepiness, perceived fatigue and amount of physical activity. The B-phase will be an intervention phase where the user receives suggestions based on the measurements. These suggestions may include changing the intensity and/or colour of the desk light; putting on headphones or moving to a quieter place in the office (based on sound data); going for a walk or standing up for a while (based on movement data); and/or opening a door or window (based on air quality data). Feedback can be automated or delivered as a notification, depending on the suggestion. The second A-phase follows the intervention phase and has no intervention—This phase assesses whether the intervention has led to lasting effects on subject behaviour or if it reverts to the baseline established in the first phase.
| Period | 30-Jan-2025 |
|---|---|
| Event title | 10th Dutch Biomedical Engineering Conference |
| Event type | Conference |
| Location | Egmond aan Zee, NetherlandsShow on map |
| Degree of Recognition | National |