12 Unsafe Behaviours in Workplace That Cause Accidents and How AI Detects Them
- Shoyab Ali
- Oct 21, 2024
- 7 min read
Updated: Mar 31
In labor-intensive industries like construction and manufacturing, unsafe behaviors are unfortunately common, contributing to a significant percentage of workplace injuries and accidents.
Workers, often under pressure to meet deadlines, may take shortcuts or overlook safety measures, leading to serious consequences. Studies show that nearly 90% of workplace accidents are the result of unsafe behaviour in workplace, making behaviour-based safety management critical for protecting workers and maintaining operational efficiency.
Computer vision-based safety systems are revolutionizing how unsafe behaviours in workplaces are identified and prevented. By using advanced technologies like computer vision, AI video analytics, and Generative AI, these solutions can continuously monitor job sites, detect risky behaviors in real-time, and provide actionable insights for EHS teams to mitigate hazards before they escalate.
This proactive approach helps reduce workplace incidents and creates a safer working environment.
Why Unsafe Behaviours are Harder to Catch Than Unsafe Conditions?
Unlike a broken handrail or a chemical spill, unsafe behaviours are transient. A worker running across a wet floor, skipping a designated pathway or entering a restricted zone takes seconds. By the time a supervisor glances at the right screen, the moment has passed without record.
Human observers have cognitive limits. Sustained attention on monitoring screen drops sharply after 20 minutes. No single supervisor can meaningfully track more than a handful of camera feeds at once. Nigh shifts, high-traffic zones, and large site footprints create blind spots that no staffing level can fully resolve.
The result is that behaviour-based safety has historically been reactive. Incidents get logged after injury. Patterns surface only in retrospect. Corrective actions happen too late. The window between an unsafe act and its consequence is where the majority of workplace accidents originate, and it is precisely the window that traditional monitoring cannot reliably cover.
What Are the Most Common Unsafe Behaviors Detected at Workplaces?
Unsafe behaviors are often the root cause of many workplace accidents. Here are some common examples of unsafe actions that are often the reasons for concern for EHS managers:
Improper use of machinery: Workers operating equipment without following safety protocols or using the wrong tools can result in machinery malfunctions or personal injury.
Ignoring PPE requirements: Failing to wear mandatory personal protective equipment (PPE), such as helmets, gloves, or safety goggles, exposes workers to severe risks, especially in hazardous environments.
Unsafe lifting techniques: Lifting heavy objects without using proper techniques or equipment increases the risk of musculoskeletal injuries.
Fatigue detection: Workers showing signs of fatigue or drowsiness are more prone to making mistakes, which can lead to accidents, especially in high-risk environments like construction and manufacturing.
Unauthorized zone entry: Workers entering restricted or danger zones without proper clearance or equipment pose a serious safety risk.
Using mobile phones or distractions: Distracted behavior, such as using mobile phones or engaging in off-task activities, can lead to accidents, especially around heavy machinery or hazardous materials.
Unsafe handling of hazardous materials: Mishandling toxic or flammable substances without following proper safety procedures increases the risk of exposure, spills, or explosions.
Workers overcrowding in confined spaces: More workers than allowed in a confined space can lead to oxygen depletion or restricted movement, increasing risks during emergencies.
Running on-site: Workers running in hazardous areas increase the risk of tripping, falling, or colliding with equipment.
Improper handrail usage: Failing to use handrails while ascending or descending can lead to serious falls.
Pedestrian safety violations: Workers walking in restricted or high-risk areas, like near operating machinery or heavy vehicles.
Worker violence: Aggressive behaviour or conflicts between workers can escalate into violence, posing a threat to the safety of everyone on-site.
Why Human Observation Alone Cannot Prevent Unsafe Behaviour on Jobsites?
Site safety supervisors carry a wide operational mandate. Beyond monitoring, they are responsible for coordination, contractor management, documentation, compliance reporting, and much more. On a busy site, these responsibilities run simultaneously, and sustained observation of worker behavior across every zone competes with all of them.
This is not a question of capability or commitment. It is a question of capacity. No role that carries this breadth of responsibility can maintain the same standard of uninterrupted attention across every camera feed, every zone, and every shift. The gaps that result are not failures of individuals. They are the predictable outcome of asking human attention to do something it was never designed to do at scale.
As site complexity grows, those gaps widen. More workers, more zones, more shifts running in parallel, and the space between what is happening and what is being seen becomes harder to close through observation alone. The behaviours most likely to cause serious injury tend to occur quickly and in high-activity periods, exactly when attention is most divided. This is the gap that behaviour safety monitoring is designed to close.
How AI-based Unsafe Behaviour Detection Works at Jobsites?
Detection mechanisms vary by behaviour type. Here is how computer vision and AI-based behaviour monitoring handle each category:
1. Running, Handrail Non-Compliance, Pedestrian Pathway Breaches
Computer vision models trained on site-specific footage track body posture, movement speed, and spatial boundaries simultaneously. When a worker's movement pattern deviates from defined safe norms, such as crossing a restricted perimeter or descending stairs without using the handrail, the system flags the event within seconds and triggers an alert to the relevant supervisor.
2. PPE Non-Compliance
Video analytics scans each worker's body before they enter a defined zone, checking for the presence of required PPE, like hard hat, reflective vest, gloves, safety goggles, etc. The check runs continuously across the camera feed without requiring any action from the worker or supervisor.
3. Fatigue, Drowsiness, and Mobile Phone Use
Facial recognition and posture analysis models monitor micro-expressions, eye movement frequency, and head position in real time. Repeated patterns of drooping posture, reduced blinking, or device interaction near high-risk equipment trigger a alert before the behaviour reaches a point of incident.
4. Unauthorized Zone Entry and Worker Grouping
AI video analytics continuously monitor defined restricted zones through AI-enabled CCTV feeds, flagging any breach of a restricted perimeter the moment it occurs and routing an immediate alert to the relevant supervisor. For worker grouping, AI-powered workforce heatmaps track real-time worker density across zones, identifying abnormal congregation patterns before they create overcrowding risks or obstruct emergency access.
5. Smoking in Restricted Industrial Zones
Computer vision models detect the presence of cigarettes or visible smoke on live camera feeds, identifying the behavior and the location simultaneously. When a worker is detected smoking within a restricted or high-risk zone, such as near flammable materials, fuel storage, or active machinery, the system triggers an immediate alert and logs the incident with a timestamped visual record for follow-up.
What Happens to Unsafe Behaviour Data After It Is Detected: The Role of Behaviour Safety Monitoring
Catching an unsafe behaviour in the moment is only half the problem. The other half is what happens to that information afterward. A single alert, acted on and forgotten, does not make a site safer over time. It just makes one incident slightly less likely to escalate. This is where AI-based unsafe behaviour detection moves beyond simple alerting into genuine risk intelligence.
The real shift happens when detection data accumulates into a pattern. Which zones generate the most alerts. Which behaviours recur despite correction. Which shifts or teams consistently show higher risk signals. That aggregated picture is what moves safety management from reactive to genuinely predictive.
This is the function viAct Behaviour-Based Safety software is built around. Rather than treating each detection as an isolated event, it builds a behavioural risk profile across the site over time, giving EHS teams the visibility to intervene before patterns become incidents, and not after-the-fact.
FAQs
1. What are the common unsafe behaviours in workplaces detected by AI?
Common unsafe behaviours in workplaces include running in hazardous areas, improper handrail usage, and pedestrian violations. AI-powered computer vision systems help identify these actions in real time, enabling faster intervention and reducing the risk of injury escalation.
2. Can viAct AI detect unsafe behaviour patterns over time, or only in the moment?
Both. Real-time detection catches a behavior the moment it occurs. But the greater value is in pattern recognition across time. If the same worker repeatedly skips handrail usage on a specific stairwell, or if fatigue alerts spike on Friday night shifts, viAct’s AI surfaces these trends before they result in an incident. This shift from reactive flagging to predictive risk profiling is what separates viAct from basic monitoring to genuine safety management system.
3. Can viAct's AI-powered behaviour safety monitoring work with existing infrastructure?
Yes. viAct's behaviour safety monitoring integrates with existing IP cameras and CCTV infrastructure without requiring a full hardware overhaul. AI modules are deployed on top of existing feeds, meaning organizations can extend the capability of their current setup rather than replacing it entirely.
4. How does AI monitoring handle multiple sites or large workforces?
AI-powered monitoring scales across sites, shifts, and worker populations without a proportional increase in manpower. Behavioural data from multiple locations feeds into a centralized dashboard, giving EHS teams a unified view of risk across the entire operation. Alerts, heat maps, and pattern reports are accessible from a single platform regardless of how many sites are being monitored.
5. How does viAct ensure the privacy of workers during AI-based behaviour monitoring?
At viAct, worker privacy is a core design consideration, not an afterthought. viAct's behaviour safety monitoring operates on a privacy-by-design framework, meaning the system is built to detect unsafe actions and behavioural risks without retaining unnecessary personal data. Monitoring is focused on behaviour and zone compliance, not individual identification for surveillance purposes. Data access is role-restricted, and the platform adheres to GDPR and regional data privacy regulations, ensuring that safety and privacy are not treated as competing priorities.
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