The Internet of Things for Dementia Care

Enshaeifar, Shirin; Barnaghi, Payam; Skillman, Severin; Markides, Andreas; Elsaleh, Tarek; Acton, Sahr Thomas; Nilforooshan, Ramin; Rostill, Helen

doi:10.1109/mic.2018.112102418

Cited by 64 publications

(34 citation statements)

References 12 publications

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“…All the sensor and medical devices record and send the data to their corresponding gateways over Wi-Fi, Bluetooth or, in exceptional cases, via auxiliary interfaces of such a device. Gateways relay these data to the companies' back end systems over GPRS, SigFox or home broadband as in TIHM [40]. At this stage, all participating companies comply with a common JSON data model for the TIHM project.…”

Section: Assistive Living Technologies and Autonomous Remote Monitoringmentioning

confidence: 99%

Toward Kinecting cognition by behaviour recognition-based deep learning and big data

Soufian

Nefti-Mezian

Drake³

2020

Univ Access Inf Soc

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The majority of older people wish to live independently at home as long as possible despite having a range of age-related conditions including cognitive impairment. To facilitate this, there has been an extensive focus on exploring the capability of new technologies with limited success. This paper investigates whether MS Kinect (a motion-based sensing 3-D scanner device) within the MiiHome (My Intelligent Home) project in conjunction with other sensory data, machine learning and big data techniques can assist in the diagnosis and prognosis of cognitive impairment and hence prolong independent living. A pool of Kinect devices and various sensors powered by minicomputers providing internet connectivity are being installed in up to 200 homes. This enables continuous remote monitoring of elderly residents living alone. Passive and off-the-shelf sensor technologies were chosen to implement data acquisition specifically from sources that are part of the fabric of the homes, so that no extra effort is required from the participants. Various constraints including environmental, geometrical and big data were identified and appropriately dealt with. A visualization tool (MAGID) was developed for validation and verification of numerous behavioural activities. Then, a subset of data, from twelve pensioners aged over 65 with age-related cognitive decline and frailty, were collected over a period of 6 months. These data were subjected to several machine learning algorithms (multilayer perceptron neural network, neuro-fuzzy and deep learning) for classification and to extract routine behavioural patterns. These patterns were then analysed further to ascertain any health-related information and their attributes. For the first time, important routine behaviour related to Activities of Daily Living (ADL) of elderly people with cognitive and physical decline has been learnt by machine learning techniques from selected sample data obtained by MS Kinect. Medically important behaviour, e.g. eating, walking, sitting, was best learnt by deep learning with accuracy of 99.30% during training stage and average error rate of 1.83% with maximum of 12.98% during the implementation phase. Observations obtained from the application of the above learnt behaviours are presented as trends over a period of time. These trends, supplemented by other sensory signals, have provided a clearer picture of physical (in)activities (including falls) of the pensioners. The calculated behavioural attributes related to key indicators of health events can be used to model the trajectory of health status related to cognitive decline in a home setting. These results, based on a small number of elderly residents over a short period of time, imply that within the results obtained from the MiiHome project, it is possible to find indicators of cognitive decline. However, further studies are needed for full clinical validation of these indications in conjunction with assessment of cognitive decline of the participants.

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Section: Assistive Living Technologies and Autonomous Remote Monitoringmentioning

confidence: 99%

Toward Kinecting cognition by behaviour recognition-based deep learning and big data

Soufian

Nefti-Mezian

Drake³

2020

Univ Access Inf Soc

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“…In the following, we discuss the role of the ontologies through the presentation of the details of some of these solutions. (iii) Technology Integrated Health Management (TIHM) (http://iot.ee.surrey.ac.uk/tihm/models/fhir4tihm) application was developed to support home-based dementia care by machine learning based information extraction from aggregated environmental and physiological data [172].…”

Section: The Application Layermentioning

confidence: 99%

A Review of Semantic Sensor Technologies in Internet of Things Architectures

Honti

Abonyi

2019

Complexity

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Intelligent sensors should be seamlessly, securely, and trustworthy interconnected to enable automated high-level smart applications. Semantic metadata can provide contextual information to support the accessibility of these features, making it easier for machines and humans to process the sensory data and achieve interoperability. The unique overview of sensor ontologies according to the semantic needs of the layers of IoT solutions can serve a guideline of engineers and researchers interested in the development of intelligent sensor-based solutions. The explored trends show that ontologies will play an even more essential role in interlinked IoT systems as interoperability and the generation of controlled linkable data sources should be based on semantically enriched sensory data.

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“…Converting PGHD data into actionable information. [References for kHealth Bariatrics, 5 kHealth ADHF, 6 kHealth Dementia, 8 TIHM 7 provide further details. ]…”

Section: Figurementioning

confidence: 99%

How Will the Internet of Things Enable Augmented Personalized Health?

2018

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The Internet of Things refers to network-enabled technologies, including mobile and wearable devices, which are capable of sensing and actuation as well as interaction and communication with other similar devices over the Internet. The IoT is profoundly redefining the way we create, consume, and share information. Ordinary citizens increasingly use these technologies to track their sleep, food intake, activity, vital signs, and other physiological statuses. This activity is complemented by IoT systems that continuously collect and process environment-related data that has a bearing on human health. This synergy has created an opportunity for a new generation of healthcare solutions.

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The Internet of Things for Dementia Care

Cited by 64 publications

References 12 publications

Toward Kinecting cognition by behaviour recognition-based deep learning and big data

Toward Kinecting cognition by behaviour recognition-based deep learning and big data

A Review of Semantic Sensor Technologies in Internet of Things Architectures

How Will the Internet of Things Enable Augmented Personalized Health?

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