Fall & Bed-Exit Monitoring

Elder Care Solution

Fall Alert & Bed-Exit Monitoring

Detect falls, prolonged lying, bed-exit events, and other high-risk situations in real time without disturbing residents, helping reduce false alarms, shorten response time, and provide replayable event evidence.

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Measurable KPI: FPR / FNR / Latency / FPS

Integration: Webhook / API / Alarm Platform

Traceable Events: Clips + Timeline Annotations

Response Speed

Shorter Notification Time

Validated by on-site PoC measurement

False Alarm Cost

Lower False Alarm Burden

Thresholds can be tuned to staff capacity

Evidence Chain

Replayable and Traceable Events

Event clips + timeline + annotations

Fall Alert and Bed-Exit Monitoring Scenario

The Challenges You Face

Help decision-makers focus quickly on risk, cost, and evidence.

High Nighttime Risk

Falls or bed-exit events that go unnoticed may lead to secondary injury.

Limited Staffing

Care staff cannot continuously watch monitors or manually check every bed.

High False Alarm Cost

Traditional motion detection may mistake blankets, lighting, or turning over as abnormal events.

Lack of Evidence Chain

It is difficult to clarify when an event happened, how long it lasted, or whether there were warning signs beforehand.

How It Works

Turn video into actionable events.

Pose / Human Understanding: identify body state and key posture cues
Sequence Interpretation: determine events from “standing → loss of balance → fall → prolonged lying”
Event Output: time, type, severity, event clip, and timeline annotation
Alarm Integration: connect to existing platforms or notification systems via Webhook / API
Feedback & Optimization: use true / false alarm feedback to continuously reduce false positives

Note: Actual performance should be validated through on-site PoC. KPI definitions and acceptance methods should be agreed upon in advance.

Camera

Edge
Inference

Event Output

Alarm / Integration
Webhook / API

Event Example

• Fall (High Risk)

• Time: 2026/03/04 09:12

• Clip: 10 seconds before and after + timeline annotation

Notify: Nurse Station / Mobile

Detectable Event Types

Start with core events, then expand step by step.

High Risk

Fall

Identify the sequence from standing or walking to falling, and provide event evidence.

High Risk

Prolonged Lying

Detect when a person remains on the floor or lying down beyond a configurable threshold such as 30s / 60s / 120s.

Medium Risk

Bed Exit

Recognize the sequence of sitting up → getting out of bed → leaving the bedside area, with support for zone-based rules.

Medium Risk

Wandering / Unsteady Gait

Evaluate risk through sequence features to reduce nighttime accidents.

Extended

Abnormal Posture

Detect prolonged bending, prone posture, semi-sitting, or semi-lying states according to the application scenario.

Extended

Sit-Up

Use as a precursor to bed exit or as a behavior record for rehabilitation activities.

Deployment & Integration

SDK, rules, alarm integration, and maintainable system design

Integration Items

Alarm Push: Webhook / REST API for existing platforms
Event Content: event ID, type, severity, time, and clip location
Event Evidence: configurable 5–15 second clips before and after the event
Permissions & Audit: role-based viewing, downloading, and rule management
Operations: logs, monitoring, version control, and rollback procedures

Deliverables

Inference SDK with version information and hardware requirements
Rule configuration sheet and test cases for acceptance
Deployment documents and integration API documentation

Camera

IR / Low-Light

Edge Inference

Event Service

Event ID / Type / Severity

Clip / Timeline / Metadata

Alarm Platform / App

Webhook / REST API

Dashboard / Replay

KPI & Acceptance

Avoid “it feels useful” — deliver with measurable results.

False Positive Rate (FPR)

Measure daily false alarms per camera to reflect staff workload, and continuously reduce them through rule tuning and data feedback.

False Negative Rate (FNR)

Assess missed detections through sample review and event comparison, with priority on ensuring that high-risk events are not missed.

Latency

Measure the time from event occurrence to alarm delivery. Actual latency depends on the deployment architecture.

Sample PoC Acceptance Workflow

Week 1: Requirements / KPI definition and camera angle review
Week 2: Data review, MVD, and initial rules
Week 3: On-site validation, false alarm / missed event analysis
Week 4: Parameter tuning completed, PoC report, and recommendations

Note: Target thresholds should be defined based on the site conditions and staffing capacity. Avoid unrealistic commitments based on arbitrary numbers.

Privacy & Security

Reduce concerns by transmitting only what is necessary and keeping permissions under control.

Data Minimization

Only event metadata such as time, type, and severity can be uploaded, while video remains on-site.

Keep Event Clips Only

Configure the system to save only pre- and post-event clips instead of continuous recording.

Permissions & Audit

Provide role-based viewing, downloading, and management permissions with operation logs retained.

FAQ

Answer common questions in one place.

Q: How long does a PoC usually take?

A: In many cases, an initial validation can be completed within 2–4 weeks, followed by a KPI report and next-step recommendations.

Q: How do you handle low-light conditions at night?

A: We recommend IR or low-light cameras and a dedicated nighttime parameter set, which should be verified during the PoC stage.

Q: Will occlusions such as blankets, rails, or multiple people cause false detections?

A: We reduce false detections through multi-feature fusion, zone-based rules, and threshold tuning, and continue improving through event feedback.

Q: Can it integrate with our existing alarm platform?

A: Yes. Events can be delivered via Webhook / REST API, including event ID, type, severity, and clip location.

Want to Evaluate Feasibility for Your Site?

We can spend 30 minutes reviewing camera angles, KPI requirements, and data conditions, then provide PoC recommendations and a suggested timeline.