Accumulated data and AI models analyse per-worker risk scores and per-zone/process risk levels to predict accident patterns in advance.
SenseZero AI Risk Prediction analyses data from wearables, BLE beacons, and sensors through an 8-axis confidence engine to calculate real-time per-worker and per-zone risk scores (0-100), then routes immediate action requests to managers via a P0-P4 priority system. It replaces experience-based guesswork with data-driven proactive accident prevention.
How it works
A 3-stage AI pipeline from data collection through to corrective action.
Data from site devices and the app is collected continuously in 60-minute windows.
Not a single number — a weighted judgement across eight axes.
Each level has a response target, with repeat alerts so nothing is missed.
Key features
Comprehensively analyses each individual's biometric data, behavioural patterns, and PPE compliance history to calculate a real-time risk score of 0–100.
Analyses accident history by zone, hazard-zone entry frequency, and environmental data to generate a process-level risk map, and reports risk patterns and trend changes by time of day, zone and worker group.
Incidents are classified into five priority levels so managers are asked to act immediately, and AI analyses the root cause of each accident type to warn of similar patterns before they recur.
Confidence engine
Not a simple number — a comprehensive weighted analysis across 8 axes that produces an accurate risk score.
| Axis | Weight |
|---|---|
| Data completeness | 20% |
| Data quality | 10% |
| PPE compliance | 20% |
| Zone risk level | 15% |
| Beacon detection count | 10% |
| Beacon dwell time | 10% |
| Biometric composite | 5% |
| Trend analysis | 10% |
Priority
Automatically classifies incidents into 5 priority levels based on risk severity to support efficient response.
| Level | Category | Response target | Triggers |
|---|---|---|---|
| P0 | Emergency response | Immediate on-site dispatch | SOS · fall detection |
| P1 | Biometric risk | Confirm within 5 min | Abnormal heart rate · stress |
| P2 | Zone risk | Act within 15 min | Repeated hazard-zone entry |
| P3 | PPE repeat | Training within 1 hour | Repeated PPE non-compliance |
| P4 | Trend watch | Daily review | Minor anomaly trend |
Risk scores are calculated as soon as beacon and wearable data arrives; trend and recurrence reports typically yield actionable insights after 2–4 weeks of data.
SenseZero wearable and beacon data is the primary input; integration with external systems can be customised via API.
Reports are generated alongside priority alerts for follow-up review and recurrence prevention.
Accident prevention through early discovery of risk patterns
Improvement in proactive accident prevention response rate
Safety decisions based on data, not experience alone
Heart rate and activity data from wearable smartwatches, BLE beacon zone entry/exit history, PPE compliance status, and environmental sensor readings are collected over a 60-minute rolling window and fed into the 8-axis weighted confidence engine.
Risk is automatically classified into five levels from P0 (SOS or fall detection, immediate on-site dispatch) to P4 (minor anomaly trend, daily review). Each level has a defined SLA so managers receive repeat alerts until the situation is acknowledged.
The current AI risk prediction engine primarily uses SenseZero wearable and beacon data. Custom integration with external systems is available via API; specific requirements can be scoped after a demo session.
Risk scores are calculated as soon as beacon and wearable data begins arriving. Trend analysis and recurrence prevention reports typically yield actionable insights after 2-4 weeks of data accumulation.
We will propose the optimal solution combination tailored to your industry, site scale, and management goals.
