Robotics to enhance independence & safety for dementia patients in the home

Lead Research Organisation: Imperial College London

Abstract

How can interactive ‘social’ robots help improve the lives of people with dementia? We aim to exploit the exciting potential of artificial intelligence (AI) to improve dementia care. We are aiming to develop a family of robotic devices that can engage people living with dementia, helping improve safety in the home and enhancing quality of life.
We will begin by designing social robotic systems that can respond to safety alerts within smart homes. Once triggered, the robot will engage with the individual and act to reduce risks. This may involve directing them to address the hazard – or deploying automated tools that can do so and deciding to call carers or medical support.
We plan to exploit existing commercial technologies (e.g. Amazon Alexa) and grow into development of bespoke devices. We will closely involve people with dementia, family and carers during the design process to define the characteristics of prototype devices and test these to ensure these robots are safe, accessible and enjoyable for people to engage.

Technical Summary

The UK Dementia Research Institute (UK DRI) is an initiative funded by the Medical Research Council, Alzheimer's Society and Alzheimer's Research UK. Funding details for UK DRI programmes will be added from 2020.

Interactive ‘social’ robots have laboratory established capacity used to engage and stimulate people living with dementia. They can: (a) undertake diverse behaviours; (b) collect rich data; (c) change the environment and (d) respond appropriately in a flexible, task-dependent manner. They provide a way to continuously monitor the home and engage with people to support behaviour or to provide companionship. However, their clinical potential is untapped and development of robotics for dementia care is at an early stage.
We will develop an automated ‘family’ of robotic devices focusing on improving safety in the home and enhancing the quality of life for a person living with dementia. The robotic devices will be designed to be safe, accessible and enjoyable to engage with. A design sprint will be used to define key features and functionality. The team will develop a small number of exemplars providing proof-of-principle. For example, they will develop robotic systems to respond to (a) environmental alerts produced by the Healthy Home (e.g. noting a kitchen spillage or cooker left on); and (b) alerts related to the person with dementia (e.g. responding to signs of agitation or injury). Following an alert, the robot device will engage with the individuals and, guided by information from a smart environment, will act to reduce risks. The simplest solution may be to direct the owner (help to clean a spillage), but it may be possible to deploy automated tools to achieve similar ends. In the case of agitation, for example,, if a cause can be identified such as confusion simple interventions may be effective and interactions through a conversational agent or social robot may be directly beneficial.
A related focus for the programme will be to develop effective methods of communication between people with dementia and robotic devices. The design process will evaluate the impact of variations in features such as configurable visual appearance, voice characteristics, movement, and tactile features that are optimal for the individuals involved, and the team will produce ‘lightweight’ prototypes for testing purposes.).
Main objectives include
1. To produce robotic devices with elementary AI that are capable of interacting with people living with dementia.
2. To integrate robotic devices within smart living environments to monitor and manage the environment for improved safety and quality of life.
3. Draw from user-centred design to define the characteristics of robotic devices suitable for these tasks

Publications

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Castillo CSM (2021) Wearable MMG-Plus-One Armband: Evaluation of Normal Force on Mechanomyography (MMG) to Enhance Human-Machine Interfacing. in IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society

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Huo W (2020) A Heterogeneous Sensing Suite for Multisymptom Quantification of Parkinson's Disease. in IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society

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Madgwick S (2020) An Extended Complementary Filter for Full-Body MARG Orientation Estimation in IEEE/ASME Transactions on Mechatronics

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Martineau T (2020) Optimizing Time-Frequency Feature Extraction and Channel Selection through Gradient Backpropagation to Improve Action Decoding based on Subthalamic Local Field Potentials. in Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference