Noctua provides continuous, non-invasive and privacy-first monitoring for elderly people and patients in homes, care facilities and hospitals. Thermal sensing and computer vision to detect falls in real time and anticipate cognitive decline.
Falls are the world's second leading cause of unintentional injury deaths: 684,000 per year according to the WHO, with more than 80% of victims over 65. In Spain alone, falls cause over 4,400 deaths annually — more than twice as many as road traffic accidents.
At the same time, Alzheimer's and other dementias are spreading unchecked: 800,000 cases in Spain, 40,000 new cases every year. Between falls and neurodegenerative conditions, the combined cost reaches €32 billion a year — 3% of Spanish GDP. And diagnosis still arrives too late.
Wearables are aged technology: riddled with false negatives, dependent on a battery that runs out, forgotten after a shower, left uncharged, or missed when the user can't press the panic button in the critical moment. They are not automatic. RGB cameras are invasive and have no place in a bedroom or a bathroom. Emergency pendants require the user to be conscious and lucid at the exact moment of the incident. No one is watching when it matters most.
At night, a single nurse may cover dozens of rooms. No human team can watch every bed simultaneously without fatigue. Noctua continuously monitors every room on the ward, detects falls and high-risk behaviours instantly, and frees staff to make their service more efficient — dedicating real time to the direct, human care of patients who need it most.
Sources: WHO, INE, Spanish Society of Neurology
We build proprietary thermal-imaging hardware combined with computer vision models running at the edge. No latency, no cloud dependency, no identifiable imagery.
Sensors that capture heat and motion patterns without recording faces or identifiable features. Privacy by design, even in bathrooms and bedrooms.
Deep learning models running locally on-device. Immediate detection, even when internet connectivity fails.
Long-term tracking of routines and patterns that detects subtle changes associated with cognitive decline, Alzheimer's and other neurodegenerative conditions.
A modular architecture ready to scale without rewriting the technology base, adapting to the specific needs of every care segment.
Elderly people who want to preserve their autonomy. Instant alerts to family and emergency services — no wearables to charge, no buttons to press.
Facilities under high care pressure. Night-time supervision of multiple rooms and early detection of behavioural changes linked to cognitive decline.
We help monitor every room on the ward during night shifts, when staff is scarce. We make the service more efficient and free up time for more direct, human care.
A workflow designed alongside clinical teams to fit into the everyday reality of care — without adding burden to staff.
Sensors record patterns 24/7 without disrupting the patient's life — no batteries to charge, no devices to wear.
Processing happens on the device itself: nothing leaves the edge except alerts and anonymised data.
Upon a fall or prolonged immobility, immediate notification to caregivers, family and emergency services through the mobile app.
Longitudinal patterns feed models that identify early signs of cognitive decline, enabling preventive interventions.
Four complementary profiles combining cutting-edge computer vision research, technology law and industrial management.
Lawyer specialised in AI and data protection. AI & IT Lawyer.
Computer vision expert. Stanford Lab & INRIA. Leads Noctua's model architecture and Edge AI stack.
CEO of Spinlock. Intellectual property management and operations.
Lawyer specialised in Web3 and AI. ESADE Business Angels School.
Noctua's clinical and technical validation is developed in partnership with academic institutions that provide scientific support, data access and expert guidance.
A few of the stages where we've presented Noctua: investment forums, international conferences and innovation programmes shaping the future of care.
A study that validates how computer vision enables real-time insights into patient behaviour and surpasses intermittent observation methods in hospital environments.
The SMART AI Patient Sitter system uses deep learning with optical sensors for real-time detection of bed exits, falls and staff presence, while preserving patient privacy.
The IMF analyses how demographic ageing will reshape the global economy between 2025 and 2100, and the role technology plays in healthy ageing.
Book a conversation with our team. We're exploring pilots with care homes, hospitals and research institutions.
ricardbaulies@icab.cat →