Guardhat Analytics

• Environmental Safety
• • Response time to gas, temperature, radiation, particulate warnings
  • Time of response presented as a distribution, marking min (fastest response), max (slowest response), and median
  • Distribution of staff still in the warning zone by each minute after a call to evacuate has been issued (absolute value and percentages)
  • Monitor data point to ensure environmental compliance e.g., to maintain permits
• Fire warning
  • Same statistics as above, but given the importance of fire responsiveness, it should be a separate metric.
• Heat map of gas levels, temperature and other environmental measures/levels across a facility over time.
• Locational Safety
  • Number of violations and compliant entrance of Lock Out / Tag Out zones.
  • Absolute number of violations and compliant entrances.
  • Ratio of violations to compliant entrances (to serve as a score).
  • Violator histogram to see if specific people are driving violation activity.
  • Heat map of violation activity to identify if particular zones are more problematic than others
• Geofencing violations
  • Same statistics as above, but reported separately as Lock Out / Tag Out is a specific issue that needs to be called out specifically.
• MIA
  • Number of MIA events in a time period.
  • Percent of time MIA over total time Guardhat environment is active (e.g., sum of all MIA events is 20 hours over 100 hours, or 20% MIA).
  • Heat map of MIA events (to help identify potential wireless dead zones or unauthorized exit points for staff).
  • MIA histogram by user to see if specific people are driving MIA activity
• Moving equipment
  • Proximity to hazardous equipment indicating high-risk areas in the plant.
• Wearer Safety
  • Contract tracing report allowing identification of persons who have been in contact with a person of interest to prevent/detect virus spread.
  • Reports of warnings across all biometric data we are collecting (e.g., temperature, heart rate).
  • Number of warning events in a time period, with drill down by type (e.g., heart rate vs temp).
  • Heat map of biometric warning events, with drill-down by type (to help identify potential problem areas where bodies are under greater stress).
  • Biometric warning histogram by user with drill down by type to see if specific people may benefit from wellness intervention, are overworked, etc. (i.e., some health-based intervention or decision may be beneficial)
• Guardhat Not Worn violations
  • Number of Not Worn events in a time period.
  • Percent of time Not Worn over total time Guardhat environment is active.
  • Heat map of Not Worn events.
  • Not worn histogram by user to see if specific people are driving MIA activity.
• Violation of any other wearer-specific item we are tracking (e.g., chin strap, presence of RFID tagged additional safety equipment)
• Follow “Guardhat Not Worn” metrics above
• SOS activity
  • Number of emergency situations identified by user.
• Individual worker’s reports highlighting safety history/compliance violations/issues over time which could be integrated into employee appraisals (for the specific worker, or across direct reports of a supervisor/manager).
• Prescriptive guidance on the need to mitigate worker risks such as heat exhaustion, cold exposure, etc. given regulatory standards (e.g. rest time per time exposed to heat over a threshold).

• Pre-Event: relevant data to fully understand the circumstances leading to a claim from both the Guardhat of the injured party, or parties, and any Guardhat hard hats proximal to the injured party/parties.
• Event: specific data relevant to the claim, as well as any ancillary data captured which may expose factors not anticipated to be impactful to claims adjustment and fraud detection (e.g., gas levels leading to balance issues for a wearer who fell and was injured); data should be presented for both the injured party/parties and any proximal Guardhat wearers.
• Post-Event: data after the key event will help clarify response/mitigation/containment steps taken, time between injury and help/resolution, etc.; as with Pre-Event and Event data, we will need to present data for both those directly involved and any proximal Guardhat wearers.
• Fraud: it is unlikely that additional data would be needed for cases of fraud, but we should work with insurer SIU (Special Investigative Unit) teams to potentially develop fraud-detecting logic processing within the helmet itself or in the SCC to help proactively identify potential red flag claims.

• Heat map of user movement with the ability to drill down to specific wearers, roles, shifts over a defined time period.
• Animated map of user movement with the ability to drill down to specific wearers, roles, shifts over a defined time period.
• Heat map of idle time with the ability to drill down to specific wearers, roles, shifts over a defined time period.
• Heat map of user concentration with the ability to drill down to specific wearers, roles, shifts over a defined time period.
• Re-presentation of geofencing data to identify potentially inefficient movement patterns within the job site.
• When, where, and how helpline calls were initiated; provide targeted training for frequently occurring situations

• CEO dashboard of safety and productivity
• Staff meeting safety scorecard
• Plant/mine/refinery/jobsite manager safety dashboard
• Regulatory reports required by OSHA, EPA, etc.
• Reports to insurers on safety compliance for premium credits.
• Safety verification reports to certifying bodies, trade groups, etc.
• Compliance reports mandated by customers to meet contractual requirements

• Geography
• Industry
• Location size

Benchmark reports show an average score for a given metric for the customer against the distribution of average scores for all customers in the segment being compared.