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Experts reveal 7 medical marvels to stop your partner snoring

Daily Mail - Science & tech

More medicine is blessed with cures and treatments that were once considered pure science fiction. Computerised scanners that can'see' inside the body, robots that perform intricate surgery and man-made implants to replace worn out body parts are commonplace on the NHS. And now there are a range of high-tech devices that can monitor your health in the comfort of your own home. Can tilting the bed really stop you snoring? 'New technology is excellent for encouraging patients to take an interest in their own health,' says Professor Helen Stokes-Lampard, chairwoman of the Royal College of GPs.


Virtual 3D app helps people make homes more 'dementia-friendly'

Daily Mail - Science & tech

For many people with dementia, moving out of their house and into a care home can be an inevitable and devastating side effect. Spatial and visual problems can accompany the more well-known memory loss, making it difficult for people to get around their once familiar homes. But a new app has been designed to help carers for people with dementia work out how to arrange furniture in their houses, which could allow their loved ones to stay at home for longer. The app will suggest improvements to make carers' homes more accessible to those with dementia. The'Dementia-Friendly Home' app, launched today, uses interactive 3D game technology to come up with ideas for carers to make their homes more accessible for those with dementia.


Guest blog: The promise of health devices:prevention & self-management

#artificialintelligence

Recent years have seen an explosion in the field of health apps, wearable devices and sensors monitoring many aspects of health and fitness. With an ageing population and the rise in multiple chronic conditions, demands on the health and social care system are increasing. Improving prevention and empowering patients to manage their own health can be successful strategies to eventually reduce financial pressures and improve patient outcomes. With some caution and appropriate patient safety checks, sensors and devices connected through the Internet of Things (IoT) can be great tools to deliver enhanced prevention, early diagnosis and the self-management of chronic conditions. But how far can sensors and wearables really take us in delivering such goals?


Detecting Activities of Daily Living and Routine Behaviours in Dementia Patients Living Alone Using Smart Meter Load Disaggregation

arXiv.org Machine Learning

The emergence of an ageing population is a significant public health concern. This has led to an increase in the number of people living with progressive neurodegenerative disorders like dementia. Consequently, the strain this is places on health and social care services means providing 24-hour monitoring is not sustainable. Technological intervention is being considered, however no solution exists to non-intrusively monitor the independent living needs of patients with dementia. As a result many patients hit crisis point before intervention and support is provided. In parallel, patient care relies on feedback from informal carers about significant behavioural changes. Yet, not all people have a social support network and early intervention in dementia care is often missed. The smart meter rollout has the potential to change this. Using machine learning and signal processing techniques, a home energy supply can be disaggregated to detect which home appliances are turned on and off. This will allow Activities of Daily Living (ADLs) to be assessed, such as eating and drinking, and observed changes in routine to be detected for early intervention. The primary aim is to help reduce deterioration and enable patients to stay in their homes for longer. A Support Vector Machine (SVM) and Random Decision Forest classifier are modelled using data from three test homes. The trained models are then used to monitor two patients with dementia during a six-month clinical trial undertaken in partnership with Mersey Care NHS Foundation Trust. In the case of load disaggregation for appliance detection, the SVM achieved (AUC=0.86074, Sen=0.756 and Spec=0.92838). While the Decision Forest achieved (AUC=0.9429, Sen=0.9634 and Spec=0.9634). ADLs are also analysed to identify the behavioural patterns of the occupant while detecting alterations in routine.


How AI could help dementia patients live more independently

#artificialintelligence

You might already have what's often called a "smart home," with your lights or music connected to voice-controlled technology such as Alexa or Siri. But when researchers talk about smart homes, we usually mean technologies that use artificial intelligence to learn your habits and automatically adjust your home in response to them. Perhaps the most obvious example of this are thermostats that learn when you are likely to be home and what temperature you prefer, and adjust themselves accordingly without you needing to change the settings. My colleagues and I are interested in how this kind of true smart home technology could help people with dementia. We hope it could learn to recognize the different domestic activities a dementia sufferer carries out throughout the day and help them with each one.