Home monitoring of patients with early and late stages of dementia
Lead Research Organisation:
University of Oxford
Abstract
This project falls within the EPSRC Artificial Intelligence Technologies
In recent years, various changes in modern societies have resulted in a sig-nificant number of people spending a considerable amount of their day in their home environments. More and more people turn to self-employment, while businesses seem to be exploring recent studies related to increasing productivity, by allowing their employees to have flexible working times, of-ten working from home. At the same time, the advances in medicine and the increase in life expectancy have resulted in the phenomenon of the ageing population; even though nowadays older retired adults normally have many more years to live, they are often faced with age-related diseases, such as arthritis, Parkinson's, dementia or geriatric depression, that might keep them at home as they become more severe.
In this DPhil project, the task of monitoring human behaviour in their home environment employing widely available, low-cost and light-weight sensors is tackled. In particular, we will explore the following research directions:
1. An algorithm for room identification, based only on BLE beacons and IMU data from smartwatches. Even though RSSI methods based on the use of smartwatches have been popular these last few years, the use of smartwatches as the main tool for tracking is not met frequently in existing literature; it is also particularly challenging, as smartwatch recordings are very noisy and also related to tasks that might be per-formed alongside movement intended to travel from one place to an-other.
2. An algorithm to perform PDR from smartwatches; the VICON system will be used to provide ground truth positioning, and the noisy acceleration data will have to be analysed carefully to identify steps. Smartwatches have been used in PDR methods before, but only as a sensor fusion method, with smartphones or smartglasses being the primary sensing device.
3. Motion pattern analysis at home using location and gait information. Though motion patterns have long been studied, it is either specific movements that are usually tackled, or the sensors used are either com-plicated networks, or intrusive (e.g., cameras), both of which are not appropriate for the privacy-preserving home environment.
4. Should data from dementia patients become available, application of the aforementioned
The project includes collaboration with expert psychiatrists on dementia.
In recent years, various changes in modern societies have resulted in a sig-nificant number of people spending a considerable amount of their day in their home environments. More and more people turn to self-employment, while businesses seem to be exploring recent studies related to increasing productivity, by allowing their employees to have flexible working times, of-ten working from home. At the same time, the advances in medicine and the increase in life expectancy have resulted in the phenomenon of the ageing population; even though nowadays older retired adults normally have many more years to live, they are often faced with age-related diseases, such as arthritis, Parkinson's, dementia or geriatric depression, that might keep them at home as they become more severe.
In this DPhil project, the task of monitoring human behaviour in their home environment employing widely available, low-cost and light-weight sensors is tackled. In particular, we will explore the following research directions:
1. An algorithm for room identification, based only on BLE beacons and IMU data from smartwatches. Even though RSSI methods based on the use of smartwatches have been popular these last few years, the use of smartwatches as the main tool for tracking is not met frequently in existing literature; it is also particularly challenging, as smartwatch recordings are very noisy and also related to tasks that might be per-formed alongside movement intended to travel from one place to an-other.
2. An algorithm to perform PDR from smartwatches; the VICON system will be used to provide ground truth positioning, and the noisy acceleration data will have to be analysed carefully to identify steps. Smartwatches have been used in PDR methods before, but only as a sensor fusion method, with smartphones or smartglasses being the primary sensing device.
3. Motion pattern analysis at home using location and gait information. Though motion patterns have long been studied, it is either specific movements that are usually tackled, or the sensors used are either com-plicated networks, or intrusive (e.g., cameras), both of which are not appropriate for the privacy-preserving home environment.
4. Should data from dementia patients become available, application of the aforementioned
The project includes collaboration with expert psychiatrists on dementia.
Planned Impact
The UK is faced with an increasing skills shortage, with a recent (2012) large-scale survey reporting that half of all key UK industries surveyed suffer from a worsening skills shortage. This is even more acute in high-tech industry and requires core investment in teaching highly-qualified cohorts, not only the foundational theoretical underpinning in this CDT's remit, but also the acumen to bring this theory to bear on a range of real problems. This CDT will promote training in transformative research that will revolutionise and intertwine theory and practice. If we are to train a generation of researchers to lead in the use of pervasive computation we must actively promote interconnecting research areas. The CDT directly addresses the Autonomous Systems & Robotics priority area and interlinks with priorities in Digitally Connected Citizens, New Digital Ventures, and smart Energy Systems and Digital Healthcare. Furthermore, the CDT has strong links to several current EPSRC challenge themes: 1) Manufacturing the Future: Sustainable manufacturing can only be achieved via autonomy, and machine intelligence at global scale. In today's market, the UK's competitive advantage lies in training highly-skilled researchers that will be able to pioneer distributed autonomous systems into manufacturing processes. 2) Energy: Intelligence and autonomy are key to energy-efficient driving and transportation systems, smart energy grids and efficient use of sparse resources. 3) Digital Economy: Intelligent machines and systems can assist people and give them control over their lives in a number of contexts, such as assisted living, home healthcare, transportation, skill & knowledge transfer and telepresence. 4) Living with Environmental Change: Intelligent hand-held devices and participatory sensing will extend environmental monitoring to unprecedented spatial and temporal scales, building real sensor systems and citizen science platforms to monitor the environment, pollutants and biodiversity.
The CDT will allow us to bring together our collaborations with industrial partners into a unique consortium, which will underpin the student training program, from fundamentals to development, deployment and use. The CDT has secured support not only from the University, but also from a team of industrial partners, who share our vision. We have support from an impressive list of companies, from global multi-nationals and large corporations, such as BAE Systems, BP, Schlumberger & YouGov (internships, studentships and membership of the external steering group), Microsoft, Google, Honeywell, Ascending Technologies, SciSys & Man Group (internships & part of our external steering group), ABB, Infosys, QinetiQ (internships and studentships). Industry and commerce will have an active participation in the CDT programme via internships and studentships; provision of short lectures highlighting the practical application of the taught material; proposing first-year research projects; membership of the steering committee; industrial placements into Oxford. Industrial participation, at all levels, will enhance the quality of the training programme and provide access to a unique pool of CDT talent. We believe that our approach to industrial engagement places realistic requirements on both industry and students.
The benefits of the CDT will be many-fold. The students will benefit via a strong foundation in the principles & practice of autonomous & intelligent systems and subsequent research with world-leading groups. The enthusiasm shown by a range of industries indicates an appetite for engaging with the student cohort, promoting clear dissemination, impact and collaboration routes benefiting industry, academia and the UK economy.
The CDT will allow us to bring together our collaborations with industrial partners into a unique consortium, which will underpin the student training program, from fundamentals to development, deployment and use. The CDT has secured support not only from the University, but also from a team of industrial partners, who share our vision. We have support from an impressive list of companies, from global multi-nationals and large corporations, such as BAE Systems, BP, Schlumberger & YouGov (internships, studentships and membership of the external steering group), Microsoft, Google, Honeywell, Ascending Technologies, SciSys & Man Group (internships & part of our external steering group), ABB, Infosys, QinetiQ (internships and studentships). Industry and commerce will have an active participation in the CDT programme via internships and studentships; provision of short lectures highlighting the practical application of the taught material; proposing first-year research projects; membership of the steering committee; industrial placements into Oxford. Industrial participation, at all levels, will enhance the quality of the training programme and provide access to a unique pool of CDT talent. We believe that our approach to industrial engagement places realistic requirements on both industry and students.
The benefits of the CDT will be many-fold. The students will benefit via a strong foundation in the principles & practice of autonomous & intelligent systems and subsequent research with world-leading groups. The enthusiasm shown by a range of industries indicates an appetite for engaging with the student cohort, promoting clear dissemination, impact and collaboration routes benefiting industry, academia and the UK economy.
Organisations
People |
ORCID iD |
| Niki Trigoni (Primary Supervisor) | |
| Antigoni Alevizaki (Student) |
| Description | Bluetooth sensors have been employed to try to obtain information of room occupancy at a home environment. Bluetooth emitters were installed in key rooms around the house (kitchen, living room, bedroom, etc.), but not all rooms of the house. Initial trials that used the maximum received signal strength at a timepoint as a measurement did not provide correct basic-level room identification, due to signal attenuation (through walls, floors, etc.). We tried a different approach that took into account the signals received from all emitters simultaneously, to try to estimate room occupancy. This approach gave very accurate results for the rooms that are equipped with bluetooth emitters. We are currently working on improving these estimations with data recorded from an accelerometer and gyroscope that are contained in a smartwatch the participants are asked to wear when at home. Mobility information is extracted, and associated with the previous results to achieve tracking not only in the rooms that are equipped with bluetooth emitters, but also other rooms around the house that the participant visits throughout the day. |
| Exploitation Route | The motivation for this funding was to ensure a home-monitoring approach for dementia patients, through estimation of their location in their home, i.e., an indoor-localisation algorithm. Such a result can provide information about mobility and behavioural patterns inside the house, so that remote intervention can be called should an abnormal behaviour be detected. In the era of smart homes though, our approach could be used to learn daily patterns of a house's occupants, so that the smart home can "prepare" the house appropriately for the occupants' needs, e.g., by warming up the water at the time the participant was identified to normally be taking their shower. |
| Sectors | Digital/Communication/Information Technologies (including Software),Energy,Healthcare |