Artificial intelligence is increasingly being deployed to improve safety and care quality for residents living with dementia. A new partnership in Canada highlights how non-intrusive monitoring technology could support staff, reduce falls and improve clinical insight in long-term care environments
AI Monitoring System Aims to Transform Dementia Care in Long-Term Care Homes
Artificial intelligence is steadily moving from experimental pilot projects to practical tools supporting frontline care. A new collaboration between technology firm Esprit-ai and Canadian long-term care provider Perley Health demonstrates how AI-powered monitoring systems could help staff detect risks earlier and respond faster to residents living with dementia.
The deployment centres on Esprit-ai’s Sense monitoring platform, a non-intrusive system installed beneath mattresses that tracks micro-movements and provides alerts to care staff.
Following a successful evaluation, Perley Health has committed to deploying the system across 20 rooms in its Gatineau Residence unit in Ottawa, Canada.
For the UK care sector, where providers are grappling with workforce shortages, rising complexity of need and increasing demand for dementia care, the example offers insight into how AI-driven monitoring tools could play a role in the future of care delivery.
Addressing Dementia Care Challenges in Residential Settings
Dementia presents significant challenges for care providers, particularly in long-term care homes where residents often live with multiple health conditions alongside cognitive decline.
People living with dementia can be at increased risk of falls, wandering, agitation and unsafe interactions with other residents. Monitoring these risks while preserving dignity and independence is a complex balancing act for staff.
The Esprit-ai Sense system attempts to address these challenges using sensor-based technology that detects small body movements from beneath the mattress. The system generates alerts when it identifies patterns linked to potential risks, enabling staff to intervene earlier.
Unlike some monitoring technologies used in care settings, the system does not rely on wearable devices or cameras. Instead, it analyses micro-movement patterns to detect behaviours such as restlessness, attempts to leave bed or irregular sleep patterns.
The technology was evaluated at the Perley Health Centre of Excellence in Frailty-Informed Care, where it was tested specifically within dementia care environments.
According to the organisations involved, the pilot explored whether the system could help staff detect early signs of resident-to-resident interactions, identify agitation or wandering behaviours and support fall prevention.
Moving From Pilot Projects to Real-World Adoption
A common barrier to innovation in health and social care is the difficulty of moving from small pilots to full implementation. In this case, the technology was evaluated through the Early Adopter Health Network (EAHN), a programme designed to test and scale healthcare innovations in real care environments.
The programme is run by the Ontario Bioscience Innovation Organization (OBIO), which works with care providers to validate new technologies before wider adoption.
The evaluation demonstrated that the monitoring system could support staff responsiveness, provide insights into resident sleep and mobility patterns, and integrate with existing nurse call systems.
Following the evaluation, Perley Health confirmed it would move ahead with procurement and ongoing use of the technology in its dementia care unit.
Patrick Tan, chief executive of Esprit-ai, said the transition from evaluation to procurement shows the potential value of non-intrusive monitoring technologies in care settings.
“This transition from evaluation to procurement is a powerful validation of both the clinical value and real-world impact of Esprit-ai Sense™,” he said in a statement.
“At Perley Health, we’ve shown that non-intrusive AI without wearables or cameras can meaningfully support staff, reduce risk, and improve safety for residents living with dementia, including veterans.”
Lessons for the UK Care Sector
Although the project is based in Canada, the challenges it addresses closely mirror those faced by the UK care sector.
According to NHS England, around 70 per cent of residents in UK care homes are living with dementia or severe memory problems. Managing falls risk, night-time wandering and behavioural symptoms places significant pressure on care staff.
Technology-enabled care has been highlighted as a potential solution to these challenges. In England, the Department of Health and Social Care has promoted digital tools such as remote monitoring, sensor technology and AI-driven analytics as part of wider efforts to modernise social care.
Several UK providers have already begun exploring similar approaches. Sensor-based fall detection systems, digital care planning platforms and remote monitoring tools are increasingly being deployed in care homes and supported living environments.
Organisations such as the Care Providers Alliance and the Local Government Association have also emphasised the importance of technology in supporting workforce capacity and improving safety.
However, adoption across the UK care sector remains uneven. Smaller providers often face barriers including cost, integration challenges and uncertainty about the return on investment.
Projects such as the Perley Health evaluation illustrate how structured testing environments can help providers assess the benefits of care technology before committing to full deployment.
The Future of AI in Community and Residential Care
As populations age across many developed countries, demand for long-term care services is expected to increase significantly.
The UK already faces growing pressure on care homes, home care providers and community health services. Workforce shortages and rising care complexity are pushing organisations to explore new approaches to delivering support.
AI-powered monitoring tools could become an important component of this transformation.
By analysing behavioural patterns, detecting risks earlier and providing data-driven insights, such technologies may help care providers deliver more proactive and personalised care.
They could also generate valuable data to inform care planning, clinical research and policy development.
However, widespread adoption will depend on evidence of effectiveness, affordability and integration with existing systems.
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