Case Study

AI Based Fall Detection

Fall detection refers to the use of technology to automatically identify when a person has fallen or experienced a sudden change in posture, and it is commonly used in safety-critical contexts. The primary goal of fall detection systems is to promptly alert relevant personnel when a fall occurs, especially in situations where the affected individual may be
unable to call for help.

Different types of Falls occurred

Falls from Heights

Workers falling from elevated surfaces like ladders, scaffolds, or platforms.
Major cause of injuries in construction, maintenance, and manufacturing industries.

Slips, Trips, and Falls on the Same Level

Result from insufficient traction or striking objects.
Can lead to falls on the same level.
Prevention involves good housekeeping, anti-slip materials, and maintenance.

Falls into Holes or Openings

Injuries occur when workers step into uncovered floor openings, manholes, or machinery pits.
Preventive measures include barriers, covers, or guardrails.

Falls from Vehicles or Equipment

Workers falling during operation or entry/exit of vehicles, machinery, or equipment.
Prevention requires proper training, safety features, and adherence to vehicle protocols.

Falls on Stairs and Ladders

Result from losing balance, missing steps, or unsafe conditions.
Prevention involves proper maintenance, adequate lighting, and employee training.

How visual analytics help in Fall detection?

Enhanced Visibility

Video analytics provides visual data, enhancing the visibility of workers on elevated surfaces, such as ladders, scaffolds, or platforms.

Pattern Recognition

Video analytics algorithms can recognize patterns associated with falls, aiding in the detection of workers falling from heights.

Immediate Alerting

Video analytics enables real-time monitoring, triggering immediate alerts when a fall is detected, especially in critical areas prone to accidents.

Verification and Confirmation

Video footage serves as a verification tool, offering visual confirmation of falls from heights, reducing false alarms and ensuring accurate incident reporting.

Contextual Analysis

Video analytics can analyze the context of slips, trips, and falls on the same level, considering factors like insufficient traction or striking objects, to improve prevention strategies.

Identifying Hazardous Areas

Video analytics can recognize areas with uncovered floor openings, manholes, or machinery pits, helping identify potential hazards and enabling preventive measures.

Customized Alerts

Video analytics systems can be customized to generate specific alerts for different types of falls, allowing tailored responses based on the nature of the incident.

Behavioural Analysis

Video analytics aids in behavioural analysis, helping identify signs of losing balance, missing steps, or unsafe conditions on stairs and ladders.

Integration with Safety Protocols

Video analytics can be integrated with safety protocols, enhancing prevention measures by ensuring proper training and adherence to safety features and vehicle protocols.

Monitoring Vehicle and Equipment Zones

Video analytics extends to monitoring zones where workers interact with vehicles, machinery, or equipment, facilitating the timely identification of falls during operations.

In summary, video analytics significantly contributes to fall detection by leveraging visual data to enhance monitoring, provide context, and facilitate quick response and prevention strategies in various workplace scenarios, including falls from heights, slips, trips, and falls on the same level, and incidents on stairs and ladders.

Attendance Monitoring Through Facial Recognition

Feb 28, 2024


Feb 28, 2024

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