How AI Could Help Families Care for Ageing Relatives From Afar

For families living in different cities or countries, one of the hardest parts of dementia care is not knowing what is happening between phone calls. AI-enabled home monitoring could help close that gap by turning ordinary household signals—movement, sleep, door activity and appliance use—into information that carers can act on. The technology is not a substitute for visits, professional care or medical assessment, but it can provide a more continuous picture of how an older relative is managing at home.

That distinction matters. AI dementia monitoring is generally most useful when it detects a change from someone’s established routine and brings that change to the attention of a person who can respond. It is less useful when presented as a system that can diagnose illness, guarantee safety or watch over someone without their involvement. The emerging model is therefore less about replacing family carers than extending their reach.

Worldwide, more than 57 million people were living with dementia in 2021, according to the World Health Organization, and informal carers such as family members provide a substantial share of dementia care. WHO estimates that informal care accounted for about half of the global economic cost of dementia in 2019.

For a son working in another country, a daughter several hours away or siblings dividing responsibility between households, that creates a practical problem: how do you notice that something has changed when you cannot physically be there?

AI-powered monitoring is beginning to offer one answer.

What AI can actually see inside a home

The technology does not need to watch an older person continuously through cameras.

A growing class of systems uses relatively unobtrusive sensors to observe patterns such as:

• when someone gets out of bed
• how often they move between rooms
• whether doors are being opened at unusual times
• whether normal household activities are taking place
• changes in sleep patterns
• changes in breathing or heart rate
• whether an appliance appears to have been left on
• unusual periods of inactivity

The underlying idea is simple: a person’s routine contains information about their wellbeing.

Research into digital biomarkers for Alzheimer’s disease has examined door sensors, infrared motion sensors, location sensors and sleep sensors as ways of capturing changes in activity and behaviour in the home. Researchers are investigating whether these signals can provide useful information about cognitive and functional changes over time.

AI becomes valuable when there is too much data for a human to examine manually. Instead of asking a family member to review thousands of sensor readings, an algorithm can look for deviations from an established pattern and flag something that deserves attention.

That is an important difference from a conventional alarm.

A door sensor might tell a daughter that the front door opened at 2 a.m. An AI-based system could potentially recognise that this is unusual for that particular person because they normally sleep through the night, increasing the significance of the event.

The move from alarms to patterns

Traditional telecare has already provided many of these basic capabilities.

The NHS describes monitoring systems that can detect movement, doors opening, flooding, smoke, gas, room temperature and appliances left on, with alerts sent to family members, carers or monitoring centres.

AI adds another layer: interpretation across time.

Imagine an older person whose normal routine is relatively consistent. They usually wake at 7 a.m., make breakfast, spend time in the living room, prepare lunch and go to bed at around 10 p.m.

One unusual late morning might mean nothing.

But if the system detects several days of progressively later waking, reduced movement, disrupted sleep and less activity in the kitchen, those changes could form a more meaningful pattern.

The AI does not necessarily know why the pattern has changed. It might be illness, medication effects, poor sleep, pain, depression, a change in routine or a technical problem with the sensors.

What it can potentially do is say: something has changed enough to warrant human attention.

That distinction is central to responsible AI-assisted care.

What a remote family carer could receive

For someone caring from afar, the most useful system would probably not produce a constant stream of technical measurements.

It would turn the data into understandable information.

Some emerging systems combine these approaches. MinderCare, developed through research involving the UK Dementia Research Institute and Imperial College London, uses door, appliance and movement sensors together with a bed mat. The system analyses information from the home and provides it to a monitoring team that includes clinical specialists.

Importantly, the MinderCare devices described by Imperial do not record sound or video. The bed sensor can monitor sleep-related movement, breathing and heart rate, while other sensors provide information about activity around the home.

That illustrates an important direction for the technology: collecting enough information to identify meaningful changes without necessarily turning the home into a video surveillance environment.

Sleep may become one of the most useful signals

Sleep is particularly interesting because changes can be difficult for a distant relative to notice.

Someone might say during a video call that they are sleeping normally. A sensor could nevertheless reveal increasing night-time movement, longer periods awake or a shift in their normal sleeping pattern.

Research published in Communications Medicine found that under-mattress sleep sensors could identify dementia-related sleep patterns without requiring people to change their normal routines. The researchers described the technology as potentially useful for future monitoring and research, rather than as a replacement for clinical assessment.

Other research is investigating whether AI applied to wearable sleep data can help identify patterns associated with Alzheimer’s disease. A 2025 study involving 102 older participants, including people with Alzheimer’s disease, reported promising results from an AI model using multimodal wearable sleep recordings—but the researchers also highlighted limitations in signal quality and the need for further validation in relevant populations.

For families, the practical implication is more modest than “AI can detect Alzheimer’s.”

A change in sleep might simply provide another piece of evidence that something has changed.

From “Is Mum okay?” to a more useful conversation

Distance caregiving often depends on short conversations.

“Did you eat?”

“Did you sleep?”

“Are you feeling okay?”

Those questions remain important, but dementia can make self-reporting more difficult, particularly when memory problems affect the ability to recall recent events.

Sensor data could provide additional context.

Suppose a daughter receives an alert showing that her mother has been getting up repeatedly during the night. Rather than discovering the problem after several weeks, she could contact her mother, speak to a local carer and, where appropriate, raise the pattern with a healthcare professional.

The same principle could apply to a sudden reduction in activity.

The technology does not establish that an illness is present. It creates a reason to investigate.

This distinction is reflected in the emerging clinical model. The MinderCare research programme is designed to identify patterns that may indicate changing health or care needs and then have those findings reviewed by people able to provide support.

For families interested in how this type of system works in practice, provides a closer look at the monitoring approach.

AI could also reduce the burden of checking in

Remote care can become a cycle of reassurance calls.

A family member may call several times a day because they are worried that something might have happened. Yet frequent calls can also be tiring or frustrating for the person receiving them, particularly if they interpret the calls as intrusive supervision.

A well-designed monitoring system could allow the family to intervene when there is a reason to intervene rather than simply because they cannot see what is happening.

That could change the rhythm of care.

Instead of:

“I haven’t heard from Dad, so I need to call again.”

the question becomes:

“His normal morning activity has changed. Is there someone nearby who can check on him?”

This is potentially valuable not only for safety but also for preserving independence.

The NHS already recognises telecare and assistive technology as tools that can help people with dementia live more independently, including movement sensors, alarms and systems that alert family members or monitoring centres.

AI does not fundamentally change that objective. It may make the information generated by those systems more useful.

What families should not expect AI to do

The biggest mistake would be to treat an AI monitoring system as a remote doctor, nurse or full-time carer.

A sensor can detect that someone moved less than usual. It cannot reliably determine whether the cause is an infection, medication side effect, depression, pain or simply a quiet day.

Likewise, an unusual heart-rate or sleep measurement does not automatically mean that a medical emergency is occurring.

Even an apparently sophisticated algorithm is working from the information its sensors can capture. If the person leaves the home, switches off a device, moves somewhere the system cannot monitor or develops a problem that produces no detectable change in the available signals, the system may have little to report.

There is also a difference between detecting a change and predicting an outcome.

Research into AI-based dementia monitoring is promising, but much of the field remains under development. A system that performs well in a research study does not automatically have the same performance in thousands of different homes, with different layouts, routines, cultures, languages, devices and stages of dementia.

That is why families should treat AI-generated alerts as prompts for human judgement rather than diagnoses.

Privacy becomes part of the care decision

Remote monitoring creates an unavoidable question: how much privacy should someone exchange for additional safety?

A camera can show exactly what is happening, but that also means intimate moments inside someone’s home can become observable by other people.

Sensor-based systems can sometimes reduce that intrusion. For example, Imperial’s description of MinderCare emphasises that its home devices do not record sound or video.

But “no camera” does not mean “no privacy issue.”

Movement patterns, sleep information, door activity and physiological measurements can still reveal highly personal information.

Families should therefore establish:

• what is being collected
• where the information is stored
• who can access it
• how long it is retained
• who receives alerts
• whether the information is used for research or commercial purposes
• what happens if the person no longer wants the system
• how the system behaves when connectivity fails

Consent is particularly important. The Alzheimer’s Society advises that people should be involved in decisions about assistive technology wherever possible, and that technology should support independence and safety rather than unnecessarily restrict freedom.

This is not merely an administrative consideration. The person living with dementia is the one whose bedroom, movements, routines and private life are being measured.

The technology works best when it is designed around the person

A technically impressive system can still fail if the person using it finds it confusing, uncomfortable or intrusive.

The Alzheimer’s Society recommends choosing technology around the individual’s needs and preferences rather than expecting the person to adapt to the technology. It also notes that introducing devices earlier can make it easier for someone to become accustomed to them.

That has an important implication for families.

The question should not be:

“What is the most advanced AI monitoring system we can install?”

It should be:

“What is the smallest amount of technology that solves the problem we actually have?”

If the concern is that an older relative sometimes forgets to turn off the cooker, an appliance sensor or automatic safety device may be more appropriate than a comprehensive monitoring platform.

If the concern is night-time wandering, door and movement sensors may be sufficient.

If the concern is unexplained changes in sleep, a sleep sensor could be relevant.

The most sophisticated system is not necessarily the most appropriate one.

Where remote monitoring can make the biggest difference

AI-assisted home care is particularly relevant when there is a gap between complete independence and the need for continuous residential supervision.

That middle ground is where many families struggle.

An older person may still be capable of living at home but need someone to notice changes early. A daughter may live abroad. A son may have a full-time job. A sibling may be responsible for appointments while a neighbour handles emergencies.

Technology can provide a shared information layer between them.

One family member might receive an alert. A local carer might make a visit. A doctor might be given information about a change in sleep or activity. The older person can remain in their familiar environment while the people around them gain a better understanding of what is happening.

This is also why the broader development of home-based dementia technology matters. examines how monitoring is moving beyond individual devices towards systems that observe several aspects of daily life together.

What families should check before adopting an AI monitoring system

The practical questions are more important than the word “AI” on the product packaging.

What happens when there is an alert?

A notification sent to a family member is only useful if someone knows what to do next.

Families should establish who receives alerts, who is available locally and what happens if the first person does not respond.

Is there human oversight?

A system that sends raw alerts to relatives is different from one in which trained professionals review patterns.

Neither model is automatically appropriate for every situation, but the distinction should be explicit.

What happens during an internet or power failure?

Home monitoring depends on infrastructure. Families should know whether sensors continue recording locally, whether alerts are delayed and what backup arrangements exist.

How much data is actually necessary?

A system should not collect more information than is reasonably needed for the intended purpose.

Can the person living with dementia understand and accept it?

This may be the most important question of all.

A device that causes distress, confusion or resistance can undermine the care arrangement it was supposed to support.

Does the technology complement human contact?

The Alzheimer’s Society specifically cautions that assistive technology should not replace face-to-face contact.

A sensor cannot sit down for a cup of tea, notice a change in someone’s mood during conversation or provide the reassurance of a familiar family member’s presence.

The next stage is likely to be more contextual AI

The direction of research is moving beyond isolated events towards combinations of signals.

A door opening is one data point.

A door opening at an unusual hour, combined with disrupted sleep, reduced daytime movement and a change in kitchen activity, is a richer picture.

That is where AI may eventually provide its greatest value: not by making a single dramatic prediction, but by finding weak signals that become meaningful when considered together.

Researchers working on home monitoring are already exploring combinations of movement, sleep, physiological measurements and other digital biomarkers.

The challenge is proving that those patterns are reliable enough to influence real-world care.

False alarms could quickly become a problem. If a system repeatedly warns a family about events that turn out to be harmless, relatives may begin ignoring notifications. Conversely, a system that misses important changes can create a dangerous sense of reassurance.

The quality of an AI care system will therefore depend not simply on how accurately its algorithm performs in a laboratory, but on whether its alerts are understandable, actionable and trustworthy in everyday homes.

The real promise is extending human attention across distance

For families caring for ageing relatives from afar, AI is unlikely to eliminate the fundamental difficulties of dementia care. Distance will still mean missed visits. Technology will still fail. Algorithms will still make mistakes, and professional assessment will remain necessary when health changes occur.

But the technology could change what distance means.

Instead of being completely dependent on occasional phone calls and visits, families could have a continuous, privacy-conscious picture of ordinary routines and a way of noticing when those routines change.

That could be particularly valuable as dementia care increasingly moves into people’s homes.

The most important question is therefore not whether AI can “look after” an ageing relative. It cannot.

The more useful question is whether AI can help the people who do provide that care notice the right things sooner, coordinate across distance and intervene without unnecessarily taking away independence. The answer is increasingly plausible—but it will depend on evidence, thoughtful human oversight, reliable technology and the consent and dignity of the person whose life is being monitored.