The number of people diagnosed with dementia is expected to rise in the coming years. Given that there is currently no definite cure for dementia and the cost of care for this condition soars dramatically, slowing the decline and maintaining independent living are important goals for supporting people with dementia. This paper discusses a study that is called Technology Integrated Health Management (TIHM). TIHM is a technology assisted monitoring system that uses Internet of Things (IoT) enabled solutions for continuous monitoring of people with dementia in their own homes. We have developed machine learning algorithms to analyse the correlation between environmental data collected by IoT technologies in TIHM in order to monitor and facilitate the physical well-being of people with dementia. The algorithms are developed with different temporal granularity to process the data for long-term and short-term analysis. We extract higher-level activity patterns which are then used to detect any change in patients’ routines. We have also developed a hierarchical information fusion approach for detecting agitation, irritability and aggression. We have conducted evaluations using sensory data collected from homes of people with dementia. The proposed techniques are able to recognise agitation and unusual patterns with an accuracy of up to 80%.
Dementia is a neurological and cognitive condition that affects millions of people around the world. At any given time in the United Kingdom, 1 in 4 hospital beds are occupied by a person with dementia, while about 22% of these hospital admissions are due to preventable causes. In this paper we discuss using Internet of Things (IoT) technologies and in-home sensory devices in combination with machine learning techniques to monitor health and well-being of people with dementia. This will allow us to provide more effective and preventative care and reduce preventable hospital admissions. One of the unique aspects of this work is combining environmental data with physiological data collected via low cost in-home sensory devices to extract actionable information regarding the health and well-being of people with dementia in their own home environment. We have worked with clinicians to design our machine learning algorithms where we focused on developing solutions for real-world settings. In our solutions, we avoid generating too many alerts/alarms to prevent increasing the monitoring and support workload. We have designed an algorithm to detect Urinary Tract Infections (UTI) which is one of the top five reasons of hospital admissions for people with dementia (around 9% of hospital admissions for people with dementia in the UK). To develop the UTI detection algorithm, we have used a Non-negative Matrix Factorisation (NMF) technique to extract latent factors from raw observation and use them for clustering and identifying the possible UTI cases. In addition, we have designed an algorithm for detecting changes in activity patterns to identify early symptoms of cognitive decline or health decline in order to provide personalised and preventative care services. For this purpose, we have used an Isolation Forest (iForest) technique to create a holistic view of the daily activity patterns. This paper describes the algorithms and discusses the evaluation of the work using a large set of real-world data collected from a trial with people with dementia and their caregivers.
This paper reports on the effect of age at fitting of hearing aids on subsequent achievements in speech intelligibility among hearing-impaired children. Four groups of hearing-impaired children were included in this investigation. Group A (n = 32) started using hearing aids in their first 6 months of life; Group B (n = 32) in their second 6 months of life; Group C (n = 38) in their second year of life and Group D (n = 51) in their third year of life. The children in each group were matched with regard to age, sex, age at onset of deafness, degree of hearing loss and educational establishment attended. The speech intelligibility of each child was rated by his/her class teacher on a 7-point scale. It was found that the speech intelligibility of the children in Group A was significantly superior to the speech intelligibility of the children in the other three groups. All other differences in speech intelligibility obtained between groups (Group B v. Group C v. Group D) were small and none proved to be statistically significant.
Abstract-In this paper we discuss a technical design and an ongoing trial that is being conducted in the UK, called Technology Integrated Health Management (TIHM). TIHM uses Internet of Things (IoT) enabled solutions provided by various companies in a collaborative project. The IoT devices and solutions are integrated in a common platform that supports interoperable and open standards. A set of machine learning and data analytics algorithms generate notifications regarding the well-being of the patients. The information is monitored around the clock by a group of healthcare practitioners who take appropriate decisions according to the collected data and generated notifications. In this paper we discuss the design principles and the lessons that we have learned by co-designing this system with patients, their carers, clinicians, and also our industry partners. We discuss the technical design of TIHM and explain why user-centred and human-experience should be an integral part of the technological design.
This investigation was concerned with the speech levels of both teachers and pupils in schools for the deaf and in units for partially-hearing children (PHUs) and with the levels of background noise found in these establishments. Twelve classes in 5 schools for the deaf and 8 PHUs were included in this study. The average speech level of the teachers, measured at 2 metres distance, was 57.5 dBA (range, 40-70 dBA) and that of the pupils was 52.9 dBA (range, 45-67 dBA). The levels of background noise measured were unacceptably high varying from 44.6 dBA (average level of quasi-stationary noise) to 76.5 dBA (average level of short-duration noise). It was concluded that the acoustic environments prevailing in these schools and units were not conducive to good hearing aid use and suggestions for improvements were put forward.
This paper reports on the use of individual hearing aids by hearing-impaired children over a period of 10 years, from 1977 to 1987. During this period the hearing aids of 1853 children attending schools for the deaf, units for the partially hearing (PHUs) and ordinary schools were examined. The examination covered those parts of a hearing aid which a teacher of the deaf, without the use of sophisticated equipment, could reasonably be expected to check to ensure that they were functioning properly. Thirty-nine per cent (39%) of these children were using bodyworn aids, and the rest (61%) were using ear-level aids. There was a marked degradation of both bodyworn and ear-level hearing aid use with increasing age of the children and this was true for both boys and girls in schools for the deaf, PHUs and ordinary schools. The girls were making better use of their aids than the boys. Only very little difference in good hearing aid use was found between the children in schools for the deaf and those in PHUs. (Good use--bodyworn aids: schools for the deaf 43%, PHUs 44%; ear-level aids; schools for the deaf 56%, PHUs 58%). The poorest use of aids was associated with the hearing-impaired children attending ordinary schools (Good use--bodyworn aids 36%, ear-level aids 49%). Overall, only 43% of the children wearing bodyworn aids were making good use of them. The corresponding figure for ear-level aids was 54%. These findings are discussed and suggestions for improvements put forward.
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