Occupational diseases are still common. Annually, approximately 4000 (former) employees die in the Netherlands and a multiple of that figure become ill as a result of being exposed to hazardous substances during their working lives. It is important to obtain quantitative information on when, where and why these exposures occur in order to prevent it.
Please contact Eelco Kuijpers
New technologies and developments, such as sensors and digitalisation, make it possible to determine exposure in high resolution in time and place to make these data available in (near) real time. We are working on the Virtual Occupational Hygiene Assistant to bring personalised and automated preventive measures within reach.
Examples of these include early warning of unhealthy working conditions, possibly combined with automatic actions like switching on extractors or turning off machines. But also sustainable behavioural change among employees through new insights into when, where and why exposures occur and how this may be affected by behaviour.
Sensors offer new possibilities to collect more personal data more often, making them suitable for more data driven and more personalised prevention of occupational diseases. Sensors measure for example every 5 seconds, providing an exposure profile over the working day instead of an average value which is now common practice for regulatory purposes. In addition, the measurement results from sensors are immediately available digitally, enabling immediate action and automated responses. The use of new technologies therefore allows for a transition from exposure monitoring to more active exposure management.
TNO is keen to work together with stakeholders such as occupational hygienists, employees and employers, sensor developers, IT developers etc. on the development of the Virtual Occupational Hygiene Assistant to prevent occupational diseases. We are working on the following technologies or applications:
TNO often uses commercially available sensors, but when good measurement methods are lacking, we develop these ourselves. An example of this is a portable particulate sensor co-developed with Casella that is especially for the workplace and which measures the mass instead of the numbers of particles. Another example is the development of a crystalline silica sensor.
TNO is investigating how these new technologies can be used optimally on the shop floor. Within the international collaboration in the field of occupational hygiene, with HSE and NIOSH, we focus on the application of sensors and answer practical questions such as which sensor is most suitable for measuring a specific exposure, how do we determine the validity of the obtained data and what new information can we extract from all these new data.
For this application we are developing a data infrastructure (EXCITE) for research and prototyping purposes that continuously stores, visualises and analyses the collected sensor data and can feed back the obtained information directly to the employee and/or employer. Because the context is important for an exposure, we also apply other techniques (such as indoor location determination and video) to make individual behaviour more measurable and link it to the exposure pattern.
So far, we have applied sensors and various technologies for context to end users in the building and construction sector, transport and logistics, welding shops, wood processing industry, and bakeries.
In addition to visualising exposure across the day or directly linking an excess exposure to a warning, we also want to answer more complex exposure questions. Where do the exposures mainly take place, and under which conditions for which worker? That is why we develop models and data analysis techniques for sensor data that allow us to model exposure in both time and space.
For example, for making ''heatmaps'' that show the concentration in space and time. Or by developing models that can identify causes of changes in exposure during the day. We are also investigating whether sensor data can be of value in further improving current exposure models such as the Advanced REACH Tool (ART), which is mainly used to estimate time-weighted average exposures for regulatory purposes.
These models will provide a much more detailed picture of where, when and why increased exposure occurs, allowing for more targeted exposure prevention.
Sensor data as such are not very informative to workers occupational hygienist. The data needs to be translated into actionable information. We developed a data infrastructure (EXCITE) that can capture sensor data in real time, process the data into required information and provide this information as feedback to the user. EXCITE is a research and prototyping platform, meaning that it can be used to develop algorithms (workflows) and experiment with them in different ways of providing feedback. EXCITE is a flexible modular system that in principle can combine sensor data with other types of data and model outputs. Therefore, the system is continuously being expanded. The system is designed to increase transparency and reusability of algorithms is promoted.
Sensors collect data, which often relate to people. The introduction of sensors in the workplace can therefore have an impact on ethical values, for example on health/welfare, the right to self-determination, privacy, trust, legitimacy and responsibility. During the development of these technologies we therefore involve end users at an early stage.
Specifically for the workplace, we have developed information sheets that describe how sensors can be applied and for what purposes. The information sheets show advantages and disadvantages of these applications and address possible ethical issues that may arise during the introduction of sensors in the workplace.
Our development of the Virtual Occupational Hygiene Assistant is part of the working life exposome programme where we chart all the exposures during a person’s working life and translate them into (early) health effects.