Predict OH&S Incidents with Machine Vision


The use of machine vision and predictive analytics to predict and prevent Occupational Health and Safety (OH&S) incidents is the next big step in developing a proactive program to reduce cost by injury prevention and unforeseen equipment maintenance.


Through integration of digital vision, image pattern recognition, and sensors, identify deviation from required safety standards in personnel, machine operation, and other protocols precursory to most incidents.  This capability significantly improves employee safety and protection.


Advancement in machine vision enables full automation of intervention in an industrial setting. This means fewer incidents and ‘prevention’ instead of ‘reporting’.


data requirements

Industry and Company Standards

  • PPE requirements data
  • Industry and regulatory standards

Environmental Data

  • Data from sensors, telemetry, IoT and IoT Edge devices
  • Weather events and forecasts, seasonality, temperature

Records and Incident Reports

  • Downtime history data relating to PPE non-compliance
  • Log data from security checks and PPE checks
  • Contributing factors to incidents


Train Artificial Intelligence to identify certain events and key features from video feeds in real-time.


Live stream data to enable alerting of safety infractions in real-time.


Use machine learning and predictive analytics to combine features from video, IoT data, and HR data to identify opportunities to mitigate safety risks.


Identify environmental variables increase the risk of incidents.


Video Indexing Sample


AI-powered Visual Monitoring 

to ensure safety compliance

Effective Management of 

critical risk factor causing accidents

Reduced OH&S Incidents 

through predictive analytics

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