Workplace Safety Monitoring

Workplace Safety Monitoring in mining uses data-driven systems to continuously track people, equipment, and environmental conditions to prevent incidents before they occur. Instead of relying mainly on periodic inspections and after‑the‑fact reports, these applications aggregate streams from sensors, wearables, cameras, and operational systems, then flag hazardous situations, unsafe behaviors, or deteriorating conditions in real time. This matters in mining and other high‑risk industries because even small lapses can lead to severe injuries, fatalities, and major operational disruptions. By automating hazard detection, standardizing safety insights across sites, and providing early warnings to supervisors and workers, these systems support a zero‑harm objective, improve regulatory compliance, and help build a more consistent safety culture globally.

The Problem

You can’t see every hazard underground until it becomes an incident on your report

Organizations face these key challenges:

1

Supervisors can’t monitor every heading, shaft, and vehicle in real time

2

Near-misses and unsafe behaviors go unreported until a serious incident occurs

3

Safety data is scattered across systems and sites, making trend analysis slow and manual

4

Inspections and audits are periodic snapshots, not continuous visibility into changing conditions

Impact When Solved

Fewer incidents and near-missesReal-time, site-wide hazard visibilityStronger compliance and audit readiness

The Shift

Before AI~85% Manual

Human Does

  • Conduct periodic safety inspections and walkarounds
  • Manually review incident and near-miss reports
  • Monitor CCTV feeds and radios for issues during shifts
  • Investigate accidents after they occur and implement corrective actions

Automation

  • Basic rule-based alarms from fixed sensors (e.g., gas thresholds, equipment faults)
  • Simple logging of sensor readings without advanced analysis
With AI~75% Automated

Human Does

  • Respond to prioritized alerts and intervene in high-risk situations
  • Handle complex judgment calls, regulatory decisions, and incident investigations
  • Design and refine safety policies, procedures, and training based on AI insights

AI Handles

  • Continuously monitor video, sensor, wearable, and operational data for unsafe conditions and behaviors
  • Detect anomalies and predict high-risk situations before they escalate
  • Generate real-time alerts, recommendations, and automated equipment shutdowns where appropriate
  • Aggregate and analyze safety data across sites to surface trends, hotspots, and leading indicators

Solution Spectrum

Four implementation paths from quick automation wins to enterprise-grade platforms. Choose based on your timeline, budget, and team capacity.

1

Quick Win

Rule-Based Safety Alert Dashboard

Typical Timeline:Days

A threshold-based monitoring and alerting system that consolidates existing sensor, SCADA, and basic camera analytics into a single safety dashboard. It uses configurable rules on gas levels, vibration, location beacons, and simple PPE detection from cloud vision APIs to trigger alerts. This validates data availability, alert workflows, and user adoption without heavy ML investment.

Architecture

Rendering architecture...

Key Challenges

  • Ensuring reliable connectivity from underground sensors and cameras to the central system.
  • Managing alert fatigue from overly sensitive thresholds.
  • Aligning rules with site-specific safety procedures and regulations.
  • Gaining trust from supervisors who are used to manual monitoring.

Vendors at This Level

Inductive AutomationKongsberg Digital

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Market Intelligence

Technologies

Technologies commonly used in Workplace Safety Monitoring implementations:

Real-World Use Cases