dr. John Bolte
John graduated from Utrecht University as an Earthquake Seismologist. In 2003, he earned his PhD from the Delft University of Technology, specialising in Acoustic Imaging. Since 2002, he has been working as a scientific project manager for The Netherlands National Institute for Public Health and the Environment (RIVM). He has written, for example, a national guideline for employee protection from electromagnetic fields. At the RIVM, he earned years of experience in the development and application of wearable measuring tools. John has received numerous research grants from ZonMw and RIVM (> 1.25 million euros). He graduated from the VU University Amsterdam in 2011 with a degree in Environmental Epidemiology and was appointed a lector at The Hague University of Applied Sciences in 2016.+31 (0) 15 - 260 63 86
The awarded RAAK MKB SCOUT Project for greenhouse measurement is along these lines. In the field of wearables there is a link with TNO and Health, Nutrition and Sports Dietetics in addition to wearables for environmental exposure, the development of wearables for measuring objective behaviour, health and biological functions will also be examined. We also have a partnership with Statistics Netherlands and Utrecht University to replace subjective questionnaire measurements with low-cost objective observations methods using sensors. These topics will be further developed in the coming academic year by a TNO employee seconded to us. We will also look at the sensitivity of specific groups of people to certain exposures, for example examining the relationship between electromagnetic fields and health. In collaboration with Medux/Digital Angel, we are also developing the Internet of Medical Things to monitor behaviour, food intake, activities and exposure of the elderly. A further cooperation agreement is underway with the Haaglanden Municipal Health Service in the field of remote CO monitoring in houses above Sisha lounges.
Ensuring Machine Health by means of timely and smart maintenance guarantees not only safety, but also an optimal production capacity. Small sensors and embedded systems provide real-time information about the wearing and lifespan of machines. Prediction models based on this sensor data over a longer time span and across multiple machines can be used to select the optimal maintenance moment. Efficiency is increased by weighing the costs due to malfunctions in the production process against the maintenance costs. Sensors and prediction models also play an important role in inspection systems and early warning systems for workers and the environment. Environmental safety is a prerequisite for carrying out high-risk business activities, e.g. in the chemical and metal industries. Safety is ensured by carrying out preventive maintenance and taking measures to reduce risks to the environment and the worker. In addition to process incidents, occupational safety also covers long-term exposure of workers, for example in the field of biological agents such as microbes and moulds, physical agents such as noise, air quality and radiation, chemical agents such as in the concentration of potentially toxic and explosive substances, and other risks which are not directly detectable.
In the ongoing 'Measuring at the workplace’ project, involving road workers from the Leeuwenstein Group and builders from Heijmans and in collaboration with TNO, ArboUnie and RIVM, multiple exposures are measured using wearables (location, temperature, humidity, UV, noise and fine dust). The Eminent Doctoral Research is looking for optimal information provision about non-observable exposure risks among blue-collar workers.
The LearningLab Urbinn on the development of self-propelled vehicles brings together a series of projects such as the Twizzy and the self-propelled bin. In the Smart Wheelchair project, different sensors, such as Lidar, ultrasonic and 3D vision, are combined to create maps of static and dynamic objects, determine routes via algorithms and then drive. In the requested SIA RAAK MKB project 'Let's move IT', an integrated fleet management system will be examined. Within this project, the HHS will develop autonomous navigation systems that can operate safely in dynamic environments, for example where people and other mobile robots are involved.