Agentic Workflow for Worker Location and Man-Down Alerts

This workflow automates the most critical safety response: detecting a worker in distress and mobilizing help. It eliminates the reliance on a coworker witnessing a fall or a victim being able to press a panic button. By fusing accelerometer and biometric data from smart wearables with UWB/GPS positioning, the system identifies man-down events autonomously. The operational upside is measured in saved lives and reduced long-tail liability, turning passive monitoring into an active guardian system that operates 24/7 across indoor and outdoor environments, integrated with site telemetry.

Implementation requires robust indoor/outdoor positioning stacks (UWB beacons, GPS), low-latency edge processing for initial alert filtering, and a central orchestrator like LangGraph to manage the confirmation logic—escalating high-confidence alerts instantly while routing ambiguous signals for camera review or a voice-check via helmet comms. Critical controls include configurable sensitivity thresholds, mandatory human-in-the-loop gates for certain alert types, and immutable logging to a system like ServiceNow or Procore for audit and incident reconstruction. The architecture must be fault-tolerant, as communication blackspots on site are a practical constraint.

This workflow automates the detection of worker falls or loss of consciousness using accelerometer and biometric data from smart PPE, triggering a multi-stage emergency response in under 30 seconds. It eliminates the critical delay of manual reporting, directly reducing the risk of fatal outcomes from unattended injuries. The operational upside comes from integrating real-time UWB/GPS location, panic button signals, and victim vitals to dispatch onsite first responders with precise coordinates and context, while simultaneously notifying external EMS.

Implementation requires a robust data pipeline from edge wearables (like Hexoskin or SiteSense) through a site mesh network to a central orchestrator built on frameworks like LangGraph or Temporal. Critical controls include false-positive filtering via movement patterns, mandatory human acknowledgment of alerts, and audit logging for compliance. The architecture must integrate with existing safety platforms (Procore, Autodesk Build) and support offline fallback modes to maintain functionality in GPS-denied or network-limited environments like tunnels or basements.

The operational bottleneck is the critical delay between a worker's fall and the arrival of first aid. This workflow automates detection via wearable accelerometer/GPS/UWB, eliminating reliance on manual observation. Savings come from preventing severe injury or fatality, reducing insurance premiums, and avoiding costly project stoppages. The architecture must fuse real-time telemetry from devices like SmartCap or Triax with site positioning systems, triggering an immediate, location-aware alert cascade.

Implementation begins with a pilot zone, integrating wearables with a central orchestrator (e.g., LangGraph) for agentic triage. Controls include a mandatory human-in-the-loop confirmation gate for ambiguous signals before full escalation. Monitoring via dashboards tracks alert latency, false-positive rates, and responder time. The final phase integrates with contractor management (Procore, Autodesk Build) and external EMS APIs for seamless handoff, ensuring audit-ready compliance logs.

Deploying a man-down system without robust governance introduces unacceptable risk. The architecture must enforce strict controls: alert validation logic to filter false positives from benign movements, multi-tier escalation paths (crew lead → site medic → EMS), and immutable audit logs for all triggers and actions. Integration with workforce management and access control systems ensures responder dispatch includes verified identity and location data, while API rate limits and circuit breakers protect downstream emergency systems from overload during a site-wide incident.

A phased rollout mitigates operational risk. Phase 1 deploys to a single high-risk crew with manual alert verification, testing integration with onsite radios and first-aid stations. Phase 2 expands to multiple crews, enabling automated alerts to designated responders while maintaining a human review gate for low-confidence signals. Phase 3 implements full automation with dynamic geofencing and direct API handoffs to municipal EMS, contingent on achieving >99% validation accuracy in controlled tests. Each phase includes defined KPIs for false-positive rates, response time improvement, and system uptime.

This workflow directly addresses the critical gap between a worker falling and emergency services arriving. It automates the detection of man-down events via accelerometers in smart PPE and precise location tracking using GPS/UWB beacons. The operational upside is a 60-80% reduction in emergency response latency, which directly correlates with improved survival rates and reduced liability. Savings come from preventing escalation into major incidents, lowering insurance premiums, and demonstrating a defensible duty of care through automated, auditable response logs.

Implementation requires a resilient edge architecture connecting wearables (like Spot-r or Blackline) to a central orchestrator (built with LangGraph or Temporal). The orchestrator must integrate with real-time location systems (from companies like Pozyx or Quuppa), site communication stacks (Motorola two-way radios, Mass Notification), and emergency CAD systems. Critical controls include panic-button overrides, false-positive filtering via heart-rate sensors, and human-in-the-loop escalation gates for ambiguous events before external dispatch. Rollout must be phased, starting with high-risk zones, and include rigorous testing of location accuracy in GPS-denied environments like basements or dense steel structures.