What PSA detection and fall detection mean
PSA detection and fall detection are two different applications of AI video analytics. PPE detection checks whether visible protective equipment such as a helmet or high-visibility vest is being worn in a defined zone. Fall detection assesses body position and movement to report a person who may have fallen or is lying on the ground.
Both functions can support safety processes, but do not replace risk assessment, safe machines, training, supervision and first aid organisation. Video AI is an additional signal line, not automatic evidence and not a sole protective measure.
Why occupational safety is a process problem
Occupational safety requires an assessment of specific activities and risks. International accident figures describe the extent of a problem, but do not replace an on-site risk assessment. Your own work processes, protective measures and emergency routes are crucial.
In production halls, logistics centres, on construction sites or in energy plants, those situations are particularly relevant in which rules are defined, but compliance with them is difficult to control in certain areas or an emergency can go unnoticed. This is exactly where event-based analysis can draw attention.
This is how PSA detection works
One PPE Detection combines object models with zone and time rules. The camera provides the image, the analysis detects, for example, a person, helmet or safety vest and checks whether the combination is present in a safety-critical zone.
- A person enters a defined danger zone.
- The analysis only checks the visible PSA characteristics prescribed there.
- A rule takes into account time windows, workspace and exceptions.
- If there is a deviation, a notice is generated with camera, location and time.
- A responsible person verifies the situation and responds according to the agreed process.
Detection quality depends on camera angle, distance, lighting, occlusion, colour and shape of the equipment. Gloves, protective goggles or hearing protection are visually more demanding than helmets and high-visibility vests. A model may therefore only be used for features that are reliably visible at the specific location.
This is how fall detection works
The Fall detection analyses body position and movement sequence. It can report when a person falls abruptly or remains on the ground for a defined period of time. This is particularly relevant in areas with lone work, limited visibility or long control routes.
An alarm must always be verified. Kneeling, performing maintenance, or intentionally lying down can resemble a fall situation. Conversely, occlusions or an unfavorable perspective can make an event invisible. That’s why zones, minimum duration and escalation levels are being tested in pilot operations.
The alarm workflow determines the benefit
Detection without a reaction process does not create security. Before operation, it must be determined:
- Who receives which alarm?
- What information is allowed to be displayed?
- How is an event verified?
- When will a first aider, a shift manager or the emergency services be informed?
- How is it documented that a report has been processed?
- How are false alarms evaluated and rules improved?
In the case of PPE deviations, a graduated reaction often makes sense: local notification, reporting to the shift manager and only escalation in repeated or particularly critical situations. In the event of a possible fall, however, every minute can be relevant.
Data protection in the workplace: clear boundaries
Video surveillance in the workplace is particularly sensitive. The FDPIC makes it clear that systems for the targeted monitoring of employees’ behaviour are prohibited. If an inspection is necessary for safety or production reasons, it must be proportionate and must not impair health or freedom of movement.
That means for PSA detection and fall detection:
- The security purpose and the zones covered are specified in writing.
- No permanent performance or behavioural evaluation of individual employees.
- No facial recognition if the security process does not require identification.
- Short events instead of unlimited full recording, as long as the purpose allows this.
- Transparent information and early involvement of employees and employee representatives.
- Few, role-based accesses and logged exports.
Pixelation is not a free pass either. It is a technical protection component within a documented and legal concept.
What a pilot project must demonstrate
A pilot should not only show that a model fundamentally recognises something. He has to prove that the entire chain works in everyday life.
- Scenario: Clearly describe protection rule, zone and response.
- Visibility: Check real camera images during day, night and typical occlusions.
- Test cases: Play through positive and negative examples in a controlled manner.
- Alarm quality: Document hits, missed events and false reports separately.
- Response time: Measure from event to confirmed action.
- Acceptance: Record feedback from affected teams.
- Readjustment: Customize zones, thresholds and flows.
Where technology reaches its limits
Video AI only recognises what is visible in the image. She sees no helmet behind a shelf, no fall outside of camera range, and no medical condition without a visible event. Smoke, dust, backlighting, protective clothing and changing production layouts affect image quality.
Even if an object is correctly identified, it does not say whether it is being worn in accordance with the standard. A helmet can be visible but not closed. A high-visibility vest can be concealed. That’s why specialist controls and safety culture remain indispensable.
Conclusion: Technology supports, people take responsibility
PSA detection and fall detection can make deviations visible earlier and save time in emergencies. However, their value comes from clear zones, realistic tests, a functioning alarm workflow and privacy-friendly boundaries.
The best solution is not the one that observes as much as possible. It detects a few, clearly defined security events and brings the right information to the right responsible person.
Visual AI for occupational safety
Learn how PPE, fall, hazard zones and alarm processes become an integrated safety architecture.
PSA detection and fall detection
Compact answers about function, limits and data protection.
What does a PSA analysis detect?
It checks visible features such as helmets or safety vests in a defined zone. Which equipment can be reliably identified must be tested on site.
Does analysis replace security controls?
No. It supplements instruction, safe work equipment, specialist controls and supervision, but does not replace them.
How does video AI detect a fall?
It assesses body position and movement and can report if a person falls abruptly or remains on the ground for a longer period of time.
Can the system generate false alarms?
Yes. Kneeling, occlusions, light and perspective can influence the result. Therefore, verification and pilot operation are required.
Is it allowed to monitor employees permanently?
Targeted behavioural monitoring is prohibited in Switzerland. Security applications need a clear purpose, proportionality and transparent rules.
Does the application need facial recognition?
No. PSA and fall events can be detected without identifying individuals.
For operations in the EU, the GDPR and the relevant national regulations must be checked. In the case of police authorities, special data protection law can be decisive for criminal prosecution. Swiss requirements should also be viewed as a separate legal framework. Event-based processing is not automatically anonymous. Our article provides a current overview Data protection and EU AI Act.
Occupational safety and data protection
- International Labor organisation: Safety and health at work
- FDPIC: Video surveillance in the workplace
- FDPIC: Video surveillance by private individuals
Editorial note: Information status August 2026. The article does not replace a risk assessment, occupational health advice or legal advice.
