The Quest to Prevent Workload Risks
Traditional workload assessment tools based on subjective methods provide a good starting point to implement general preventive measures.
However, the working environment and each worker’s physiology, perception of workload, and ability to cope with workload demands is unique.
Consequently, even the best preventive measures can become obsolete very fast, and most organizations struggle to reduce workload risks that can increase worker turnover, accidents and absenteeism.
The Science of Workload
The insights and recommendations from our proprietary Workload Analytics are created by combining deep knowledge from multidisciplinary experts and validated scientific evidence on the fields of biomedical engineering, artificial intelligence, occupational health & safety, and production engineering.
Artificial Intelligence:
based on methodologies validated by
+10 years of research in Germany
Safety, Health and Operations:
thresholds and recommendations based
on ISO standards for work environments
Biosignals from Wearables:
high data quality based on top sensors
and biomedical engineering processes
