Patient and staff safety protocols

Monitoring of hand hygiene adherence at sinks and washbasins in hospitals, kitchens and factories. The system identifies the start and end of each handwash, measures rubbing duration and generates compliance reports by department — supporting patient safety goals and GMP.

How it works

1. 1The sink zone is outlined on the camera image.
2. 2The pose model locates the wrists; hands together and moving within the zone = rubbing.
3. 3Duration is compared with the configured minimum (WHO: 40–60 s with soap and water).
4. 4Insufficient handwashes trigger an alert and feed the supervision reports.

Benefits

• Continuous measurement of adherence (currently done by sample observation).
• Fewer healthcare-associated infections (HAIs).
• Reports by department and shift for targeted training.

Human pose estimation (17 body keypoints) applied to every person in the field of view. When it identifies a sudden transition from standing to lying down — or a person down for a prolonged time — the system triggers an immediate alert to the response team, shortening the time to assistance.

How it works

1. 1The pose model locates each person's shoulders, hips, knees and ankles; the tracker follows each individual.
2. 2Standing: legs extended under the hips (knee above 140° and feet well below the hips) — bending over to pick something up still counts as standing.
3. 3Sitting: upright torso, thighs nearly horizontal and feet below the hips (chair, bench, edge of the bed). Leaning torso with legs extended = reclining (bed, armchair).
4. 4Crouching/kneeling: knees sharply bent with hips close to the heels, or body height much lower than the same person standing.
5. 5On the floor: horizontal torso without the legs supporting the body, or feet above the hips. Sitting on the floor: hips at the height of the feet.
6. 6Measurements are normalized by the person's own body size and posture is confirmed by voting over several frames, so it does not flicker.
7. 7Fall: a person seen standing who reaches the floor with a rapid drop of the hips. A person found already lying on the floor triggers an alert after a few seconds.
8. 8An alert with an image of the moment is sent within seconds; sitting, reclining and crouching do not trigger alarms.

Benefits

• Drastically shorter time between the fall and assistance ("long lie").
• Video records for root cause analysis and legal defense.
• Less visual-monitoring burden on the nursing staff.

Continuous monitoring of the lying position and side of bedridden patients. The system identifies the position (supine, right/left lateral, prone), times how long the patient stays in each one and alerts the nursing staff when the repositioning interval is exceeded — preventing pressure injuries.

How it works

1. 1The pose model locates the patient's shoulders, hips and face in bed.
2. 2Shoulder width, face visibility and which side of the face is visible determine the position.
3. 3A timer tracks the time in each position and the changes made.
4. 4Reports show adherence to the repositioning protocol by bed and shift.

Benefits

• Prevention of pressure injuries — an avoidable adverse event.
• Automatic record of position changes (auditing and accreditation).
• Fewer check rounds for the nursing staff.

Automatic verification of jewelry use — rings, wedding bands, watches, bracelets, necklaces and earrings — in environments where they are prohibited due to biological risk, product contamination or mechanical safety. It uses an open-vocabulary detector: new classes can be added by text, without retraining.

How it works

1. 1The YOLO-World detector receives the list of jewelry items in natural language.
2. 2Each item is associated with the person wearing it.
3. 3Non-compliance triggers an alert with the image crop as evidence.

Benefits

• Auditable compliance with occupational safety regulations and GMP.
• Less cross-contamination and fewer product recalls.
• Prevention of entanglement and degloving injuries.

Computer vision model trained by Neural to identify, on each worker, the presence or absence of Personal Protective Equipment — coveralls, gloves, safety glasses, masks and face shields — using the cameras already installed. Each non-compliance triggers an alert with photographic evidence and feeds safety metrics by area, shift and team.

How it works

1. 1The cameras (RTSP/IP) send images to the on-premises or cloud inference server.
2. 2The YOLO model detects people and each PPE item (or its absence) in milliseconds.
3. 3A tracker associates detections with each worker to avoid duplicate alerts.
4. 4Per-area rules define which PPE is mandatory (e.g. hard hat and vest on the construction site).
5. 5Alerts with photos are sent to the safety officer (dashboard, email, WhatsApp, webhook).

Benefits

• Continuous 24/7 monitoring, without relying on in-person rounds.
• Photographic evidence of every occurrence for training and audits (occupational safety regulations, ISO 45001).
• Compliance metrics by area, shift and team for targeted preventive action.
• Fewer accidents, lost-time injuries and labor liabilities.
• Leverages the existing camera (CCTV) infrastructure.

AI model trained to recognize facial expressions associated with emotions — happiness, sadness, fear, disgust, surprise, anger, contempt and neutral. It measures in real time how customers, students and audiences react to products, service, classes, storefronts and presentations.

How it works

1. 1The YuNet detector locates the faces in each frame.
2. 2The HSEmotion model (trained on the AffectNet dataset) classifies 8 emotions with probabilities.
3. 3Readings are smoothed over time per person to avoid fluctuations.
4. 4A satisfaction (valence) index summarizes the audience's mood minute by minute.

Benefits

• Objective emotional feedback, without questionnaires.
• Identifies the moments and products that generate the most delight (or frustration).
• Complements NPS and surveys with continuous, anonymous data.