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.

Cross team decisions AI driven

Context-rich Workload Metric​

High stress enviroments Workload transfer
Monitoring workers workload Workload transfer
Real-time workload analytics

External load

  • Physical demands at work
  • Mental demands at work
  • Other life challenges

ISO-based Workload Analytics

Workload insights and interventions based on ISO standards that measure the internal response to being exposed to some type of external load using biosignals from wearables, proprietary artificial intelligence algorithms and data from the operational context

Objective Workload Metrics

High stress enviroments

External load

  • Physical demands at work
  • Mental demands at work
  • Other life challenges
Workload transfer
Monitoring workers workload
Workload transfer
Real-time workload analytics

ISO-based Workload Analytics

We provide unique workload insights and recommendations based on ISO standards that measure the internal response to being exposed to some type of external load using biosignals from wearables and proprietary algorithms

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​

WearHealth platform