Fall Detection
Sensors, cameras, and the surprisingly hard problem of predicting a bed exit.
Detection vs prediction
Fall detection — recognizing that a fall has occurred — is a solved problem with modern accelerometers and vision. Fall prediction — recognizing that a patient is about to try to stand up — is much harder, because the pre-fall behaviors are subtle and vary by patient.
Sensor comparison
| Sensor | Detects | Predicts (bed exit) | Privacy | Notes |
|---|---|---|---|---|
| Bed load cells | Bed exit event | Weight shift precursor | High | Baseline standard of care |
| Chair pad | Chair exit | Limited | High | Simple, cheap, high FPR |
| Wearable accelerometer | Fall after event | Poor | High | Compliance dependent |
| RGB camera + CV | Fall + pre-fall pose | Moderate | Requires privacy design | |
| Depth camera / radar | Fall + pose | Moderate | High | Preferred where imagery is sensitive |
The workflow around the alert
The determining factor is not the model. It is what happens in the ten seconds after an alert fires: who receives it, on what device, and whether they can get to the room in time. Route to the assigned nurse's phone first, escalate to charge and rapid response after documented no-answer.
Fall-risk assessment tools that fold into the room's technology:
- Morse Fall Scale (US default).
- Hendrich II (widely used).
- STRATIFY (UK origin).
- Johns Hopkins Fall Risk Assessment Tool.
Standards & frameworks
- AHRQ Preventing Falls in Hospitals toolkit.
- CMS Hospital-Acquired Conditions — falls with injury are a non-payable condition since 2008.
- The Joint Commission Sentinel Event Alert 55 on preventing falls.
References & further reading
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