DeCloak™
Patient Movement
Monitoring
AI-powered real-time patient movement monitoring and critical event detection for hospital environments — with privacy-preserving de-identification built in.
Key Capabilities
What DeCloak Does
DeCloak uses advanced computer vision and AI to provide continuous, passive monitoring of patient movement in hospital settings — detecting critical events in real time without compromising patient privacy.
Continuous AI-powered analysis of patient movement patterns in hospital rooms. Detects patient activity, bed exits, wandering, and unsupervised movement — key indicators for fall risk and adverse events.
Instant notification to nursing staff when critical patient movement events are detected. Enables rapid response to potential fall situations, boundary crossings, or periods of patient isolation requiring attention.
Built-in de-identification processes patient imagery at the point of capture. Identifiable visual data is not stored or transmitted — the system works on movement patterns and metadata, protecting patient and staff privacy by design.
Advanced machine learning models trained specifically for hospital room environments. Handles variable lighting, camera angles, and dynamic ward settings with high accuracy — continuously improving over time.
No wearables, no patient interaction required. DeCloak operates passively in the background — continuous monitoring without disrupting patient care workflows or requiring patient compliance.
Supports nursing and allied health teams by providing an additional layer of patient oversight — particularly valuable during high patient-to-staff ratios, night shifts, and busy ward environments.
How DeCloak Works
A four-step passive monitoring process designed for seamless integration into existing hospital workflows.
Room-mounted cameras continuously capture patient environment data in real time.
AI de-identifies visual data at the point of capture — no identifiable imagery is stored or transmitted.
Computer vision models analyse movement patterns and detect critical events in real time.
Instant notifications sent to nursing staff when critical movement events are detected.
Built for Privacy. Designed for Safety.
Privacy is not an afterthought — it is foundational to how DeCloak operates. The system is built from the ground up to protect patient and staff identity at every stage.
DeCloak processes visual data locally at the point of capture. Face blurring and patient de-identification occur before any data is transmitted or stored. The AI analyses movement patterns and event metadata — not identifiable imagery. This approach ensures compliance with Australian privacy legislation and hospital patient privacy policies, while maintaining the effectiveness of real-time monitoring.
Where DeCloak Is Used
Designed for a wide range of hospital and healthcare settings where continuous patient monitoring is required.
Continuous monitoring of post-operative and medically complex patients at risk of falls or unexpected deterioration during routine ward care.
Monitoring of elderly or rehabilitation patients with elevated fall risk — providing an additional safety layer without restricting patient mobility or dignity.
Supplementary monitoring for patients transitioning from intensive care — where continuous oversight remains important but full ICU staffing ratios are not maintained.
Enhanced patient safety during periods of reduced staffing — providing nursing teams with real-time alerts when patient movement requires immediate attention.
Important Notice: DeCloak is a patient safety monitoring tool — it is not a medical device and is not intended for clinical diagnosis, clinical decision-making, or therapeutic purposes. DeCloak does not replace clinical assessment by qualified healthcare professionals. All clinical decisions remain the responsibility of the treating clinical team.
Enquire About DeCloak for Your Facility
Contact the Advanced Biotech team to discuss DeCloak implementation, technical specifications, and suitability for your hospital or healthcare facility.