30+min
Average time before a fall is found
From the moment of impact to first nurse response on a typical ward.
Bedside intelligence
Intelligence
atEveryBedside
Dual-sensor computer vision and on-device AI watch every patient, every second, with zero contact. Falls detected in real-time. Vitals streamed without a single wire.
Live monitor
Bed 14BCAM 03 // 3D + IR60 FPS
HR
72bpm
RR
14rpm
SpO₂
98%
BP
118/76mmHg
<2s
Fall alert latency
0%
HIPAA, by architecture
0
Probes. Wearables. Contact.
24/7
Continuous monitoring
Constant vigilanceZero contactContinuousClinicalEdge nativeSub 2 second
01/ 07
The blind spot
Intermittentmonitoringis
a20th-centurycompromise.
Modern wards run on hourly rounds and triggered alarms. Between checks, the most predictable injuries in medicine still go undetected. The gap is structural, not human.
1:8
Nurse to patient ratios stretched past safety
Night shifts now run at ratios that early-warning systems were never designed for.
0%
Of a typical stay spent unobserved
Between hourly rounds, the most predictable injuries in medicine still go undetected.
02/ 07
What it sees
Fourdetections,
onecamera,everypatient.
A single sensor replaces a tangle of probes, leads, and wristbands. Every category of preventable harm, watched at the same time.
< 2s
Alert latency
Fall prevention
RGB and 3D imaging posture tracking. Alerts in under two seconds, before impact when the trajectory is unmistakable.
Active surface
2h
Repositioning window
Pressure ulcer risk
Position duration logged continuously. Stage one wounds prevented at the source, not dressed at the cost.
Active surface
0.4mm
Motion resolution
Restlessness and sleep
Micro-movement analysis quantifies sleep architecture and surfaces agitation hours before delirium escalates.
Active surface
4 in 1
Biomarkers per camera
Contactless vitals
Heart rate, respiratory rate, oxygen saturation, and blood pressure captured from a single camera feed.
Active surface
03/ 07
Biomarkers without wires
Vitals,streamed
frompatientmonitors.
A standard patient monitor, transformed into a smart monitor that can be streamed directly to hospital staff. Four core biomarkers, captured in real-time with continuous monitoring and analysis.
Live monitor
Patient 0042 / Bed 14BHeart rate
72bpm
Respiratory rate
14rpm
Oxygen saturation
98%
Blood pressure
118/77mmHg
01 / 04
Heart rate
Monitor heart rate in real-time with intelligent alerts when critical thresholds are breached. Gain deeper health insights through advanced resting heart rate analysis to support faster, smarter care decisions.
Sampling
60 fps
Latency
< 1 s
Calibration
None
Storage
On-device
04/ 07
Live demo
Oneoperator,
anentirewing.
Swipe, drag, or scroll across a simulated ward. Each bed runs its own local model. The console is the only place they meet.
01 / 04
Stable
PT-0042CAM 03 // 3D + IRNEUROLOGY
12A60 fps
HR
72bpm
SpO₂
98%
RR
13rpm
Console note
Post-craniotomy. No restlessness in last 4h. Position changed at 02:14.
Watching
PT-0078CAM 03 // 3D + IRPOST-SURGICAL
16A60 fps
HR
96bpm
SpO₂
95%
RR
18rpm
Console note
Respiratory rate trending up since 03:08. Within range, sustained drift flagged.
Stable
PT-0117CAM 03 // 3D + IRNEUROLOGY
22C60 fps
HR
64bpm
SpO₂
97%
RR
11rpm
Console note
Sleep stage estimated at N3. No agitation markers.
Fall risk
PT-0023CAM 03 // 3D + IRCRITICAL CARE
08D60 fps
HR
122bpm
SpO₂
91%
RR
22rpm
Console note
Posture vector deviation 71 deg. Bedside team alerted 4s ago.
End of feed
Every bed reporting. Continuously.
Falls detected in under 2 seconds100% on device inference0 wearables requiredContinuous, 24 / 7
05/ 07
The pipeline
Fromphotonstoalerts
inunderaheartbeat.
Four stages, none of them in the cloud. The system runs in the same room as the patient, and the latency budget shows it.
01
Dual-sensor capture
Synchronized RGB and 3D imaging streams pulled at clinical frame rates. One device sees what the human eye can, and what it cannot.
Stage 01
02
Edge-native inference
Every frame is analyzed on the device. Privacy is enforced by physics, not policy.
Stage 02
03
AI classification
Deep neural network models trained on millions of frames flag anomalies before they cascade. Confidence is reported, not implied.
Stage 03
04
Workflow integration
Alerts route directly to hospital staff.
Stage 04
06/ 07
The intelligence layer
Fourmethodologies,
onecontinuousread.
The model layer is the product. Everything beneath it exists to feed it clean data. Everything above exists to act on its judgment.
01Method
Adaptive monitoring
Thresholds tune themselves to each patient's baseline, not a generic norm. A resting heart rate of 48 is unremarkable for one patient and an emergency for the next.
02Method
Multimodal fusion
RGB, 3D imaging, and physiologic signals reconciled into a single state estimate. The whole picture is always more reliable than any one channel.
03Method
Digital twin modelling
A live, patient-specific physiologic model running alongside the real one. Deviations are flagged the moment the body diverges from its own pattern.
04Method
Anomaly detection
Subtle deterioration surfaced hours before traditional early warning scores. The signal was always there. The system finally has the attention to see it.
07/ 07
Built for PHI
Privacyisthearchitecture,
notthepolicy.
The cheapest breach is the one that cannot happen. We removed the surface, not just the risk.
Edge-native by design
Raw video is processed and erased on the device. Nothing is stored, nothing is streamed, nothing waits for a breach.
HIPAA compliant by structure
No cloud video transit means no surface for breach. Compliance is a property of the system, not a checkbox.
Air-gapped ready
Deployable in fully isolated networks. Sub-two-second response, even when the building is offline.
Pilots open
Bringcontinuousintelligence
toyourfacility.
Pilots open now for neurology and post-surgical units. We deploy in days, not months, and the first reading lands the moment the camera is mounted.
Replies within one business day. No procurement gauntlet to start.