Modelling mobile-based technology adoption among people with dementia.

The work described in this paper builds upon our previous research on adoption modelling and aims to identify the best subset of features that could offer a better understanding of technology adoption. The current work is based on the analysis and fusion of two datasets that provide detailed information on background, psychosocial, and medical history of the subjects. In the process of modelling adoption, feature selection is carried out followed by empirical analysis to identify the best classification models. With a more detailed set of features including psychosocial and medical history information, the developed adoption model, using kNN algorithm, achieved a prediction accuracy of 99.41% when tested on 173 participants. The second-best algorithm built, using NN, achieved 94.08% accuracy. Both these results have improved accuracy in comparison to the best accuracy achieved (92.48%) in our previous work, based on psychosocial and self-reported health data for the same cohort. It has been found that psychosocial data is better than medical data for predicting technology adoption. However, for the best results, we should use a combination of psychosocial and medical data where it is preferable that the latter is provided from reliable medical sources, rather than self-reported.

Factors that potentially influence successful weight loss for adults with intellectual disabilities: A qualitative comparison.

BACKGROUND: People with intellectual disabilities are more at risk of obesity than the general population. Emerging literature indicates that multicomponent interventions are most effective, however, individual results are variable and little research exists as to why this is the case. METHODS: Focus groups were conducted to explore lived experiences between two groups of adults with intellectual disabilities; an overweight group (n = 6) and a group identified as successful in losing weight (n = 6). Similarities and differences were explored across four domains. Transcripts were produced and analysed using Theoretical Thematic Analysis. RESULTS: Similarities included service centre supports, basic food knowledge and issues restricting independence. The successful weight loss group had also internalised health messages, engaged with external reinforcement programmes, responded to positive feedback and demonstrated healthier dietary habits. CONCLUSION: Weight management interventions would benefit from understanding the influence that internalisation of health messages, effective reinforcement systems and positive feedback can have on supporting the adoption of healthier habits.

Modelling assistive technology adoption for people with dementia.

PURPOSE: Assistive technologies have been identified as a potential solution for the provision of elderly care. Such technologies have in general the capacity to enhance the quality of life and increase the level of independence among their users. Nevertheless, the acceptance of these technologies is crucial to their success. Generally speaking, the elderly are not well-disposed to technologies and have limited experience; these factors contribute towards limiting the widespread acceptance of technology. It is therefore important to evaluate the potential success of technologies prior to their deployment. MATERIALS AND METHODS: The research described in this paper builds upon our previous work on modelling adoption of assistive technology, in the form of cognitive prosthetics such as reminder apps and aims at identifying a refined sub-set of features which offer improved accuracy in predicting technology adoption. Consequently, in this paper, an adoption model is built using a set of features extracted from a user's background to minimise the likelihood of non-adoption. The work is based on analysis of data from the Cache County Study on Memory and Aging (CCSMA) with 31 features covering a range of age, gender, education and details of health condition. In the process of modelling adoption, feature selection and feature reduction is carried out followed by identifying the best classification models. FINDINGS: With the reduced set of labelled features the technology adoption model built achieved an average prediction accuracy of 92.48% when tested on 173 participants. CONCLUSIONS: We conclude that modelling user adoption from a range of parameters such as physical, environmental and social perspectives is beneficial in recommending a technology to a particular user based on their profile.

Technology adoption and prediction tools for everyday technologies aimed at people with dementia.

A wide range of assistive technologies have been developed to support the elderly population with the goal of promoting independent living. The adoption of these technology based solutions is, however, critical to their overarching success. In our previous research we addressed the significance of modelling user adoption to reminding technologies based on a range of physical, environmental and social factors. In our current work we build upon our initial modeling through considering a wider range of computational approaches and identify a reduced set of relevant features that can aid the medical professionals to make an informed choice of whether to recommend the technology or not. The adoption models produced were evaluated on a multi-criterion basis: in terms of prediction performance, robustness and bias in relation to two types of errors. The effects of data imbalance on prediction performance was also considered. With handling the imbalance in the dataset, a 16 feature-subset was evaluated consisting of 173 instances, resulting in the ability to differentiate between adopters and non-adopters with an overall accuracy of 99.42 %.

A predictive model for assistive technology adoption for people with dementia.

Assistive technology has the potential to enhance the level of independence of people with dementia, thereby increasing the possibility of supporting home-based care. In general, people with dementia are reluctant to change; therefore, it is important that suitable assistive technologies are selected for them. Consequently, the development of predictive models that are able to determine a person's potential to adopt a particular technology is desirable. In this paper, a predictive adoption model for a mobile phone-based video streaming system, developed for people with dementia, is presented. Taking into consideration characteristics related to a person's ability, living arrangements, and preferences, this paper discusses the development of predictive models, which were based on a number of carefully selected data mining algorithms for classification. For each, the learning on different relevant features for technology adoption has been tested, in conjunction with handling the imbalance of available data for output classes. Given our focus on providing predictive tools that could be used and interpreted by healthcare professionals, models with ease-of-use, intuitive understanding, and clear decision making processes are preferred. Predictive models have, therefore, been evaluated on a multi-criterion basis: in terms of their prediction performance, robustness, bias with regard to two types of errors and usability. Overall, the model derived from incorporating a k-Nearest-Neighbour algorithm using seven features was found to be the optimal classifier of assistive technology adoption for people with dementia (prediction accuracy 0.84 ± 0.0242).

Addressing the challenges of sleep/wake class imbalance in bed based non-contact actigraphic recordings of sleep.

Utilising strategically positioned bed-mounted accelerometers, the Passive Sleep Actigraphy platform aims to deliver a non-contact method for identifying periods of wakefulness during night-time sleep. One of the key problems in developing data driven approaches for automatic sleep monitoring is managing the inherent sleep/wake class imbalance. In the current study, actigraphy data from three participants over a period of 30 days was collected. Upon examination, it was found that only 10% contained wake data. Consequently, this resulted in classifier overfitting to the majority class (sleep), thereby impeding the ability of the Passive Sleep Actigraphy platform to correctly identify periods of wakefulness during sleep; a key measure in the identification of sleep problems. Utilising Spread Subsample and Synthetic Minority Oversampling Techniques, this paper demonstrates a potential solution to this issue, reporting improvements of up to 28% in wake detection when compared to baseline data while maintaining an overall classifier accuracy of 90%.

Estimation performance of a reduced lead system during continuous 12-lead ECG ST-segment monitoring.

BACKGROUND: Reduced lead systems utilizing patient-specific transformation weights have been reported to achieve superior estimates than those utilizing population-based transformation weights. We report upon the effects of ischemic-type electrocardiographic changes on the estimation performance of a reduced lead system when utilizing patient-specific transformation weights and population-based transformation weights. METHOD: A reduced lead system that used leads I, II, V2 and V5 to estimate leads V1, V3, V4, and V6 was investigated. Patient-specific transformation weights were developed on electrocardiograms containing no ischemic-type changes. Patient-specific and population-based transformations weights were assessed on 45 electrocardiograms with ischemic-type changes and 59 electrocardiograms without ischemic-type changes. RESULTS: For patient-specific transformation weights the estimation performance measured as median root mean squared error values (no ischemic-type changes vs. ischemic-type changes) was found to be (V1, 27.5 µV vs. 95.8 µV, P<.001; V3, 33.9 µV vs. 65.2 µV, P<.001; V4, 24.8 µV vs. 62.0 µV, P<.001; V6, 11.7 µV vs. 51.5 µV, P<.001). The median magnitude of ST-amplitude difference 60 ms after the J-point between patient-specific estimated leads and actual recorded leads (no ischemic-type changes vs. ischemic-type changes) was found to be (V1, 18.9 µV vs. 61.4 µV, P<.001; V3, 14.3 µV vs. 61.1 µV, P<.001; V4, 9.7 µV vs. 61.3 µV, P<.001; V6, 5.9 µV vs. 46.0 µV, P<.001). CONCLUSION: The estimation performance of patient-specific transformations weights can deteriorate when ischemic-type changes develop. Performance assessment of patient-specific transformation weights should be performed using electrocardiographic data that represent the monitoring situation for which the reduced lead system is targeted.

Evaluation of connected health technology.

With current advances in sensing technology, communication networks and software applications, the use of connected health technology within the home environment has become both more affordable and widespread. Nevertheless, the introduction of this new care paradigm has brought with it many challenges, with one of the most notable being assessing of the impact or otherwise of its usage. The assessment of efficiency, benefit and utility of such technology is recognised as still being in its infancy. Traditional evaluation protocols may fail to address the specific challenges associated with increased use of networks, databases and home deployments, in addition to the multitude of factors influencing successful adoption. This article aims to delineate the required steps of connected health technology evaluations and move towards a common framework that can be used to support future evaluations. A series of recommendations are presented based on previous experience in the domain.

Transformation of the Mason-Likar 12-lead electrocardiogram to the Frank vectorcardiogram.

Vectorcardiograpic (VCG) parameters can supplement the diagnostic information of the 12-lead electrocardiogram (ECG). Nevertheless, the VCG is seldom recorded in modern-day practice. A common approach today is to derive the Frank VCG from the standard 12-lead ECG (distal limb electrode positions). There is, to date no direct method that allows for a transformation from 12-lead ECGs with proximal limb electrode positions (Mason-Likar (ML) 12-lead ECG), to Frank VCGs. In this research, we develop such a transformation (ML2VCG) by means of multivariate linear regression on a training data set of 545 ML 12-lead ECGs and corresponding Frank VCGs that were both extracted surface potential maps (BSPMs). We compare the performance of the ML2VCG method against an alternative approach (2step method) that utilizes two existing transformations that are applied consecutively (ML 12-lead ECG to standard 12-lead ECG and subsequently to Frank VCG). We quantify the performance of ML2VCG and 2step on an unseen test dataset (181 ML 12-lead ECGs and corresponding Frank VCGs again extracted from BSPMs) through root mean squared error (RMSE) values, calculated over the QRST, between actual and transformed Frank leads. The ML2VCG transformation achieved a reduction of the median RMSE values for leads X (13.9µV; p<.001), Y (15.1µV; p<.001) and Z (2.6µV; p=.001) when compared to the 2step transformation. Our results show that the 2step method may not be optimal when transforming ML 12-lead ECGs to Frank VCGs. The utilization of the herein developed ML2VCG transformation should thus be considered when transforming ML 12-lead ECGs to Frank VCGs.

A user profile ontology based approach for assisting people with dementia in mobile environments.

Personalization and context-aware applications have attracted increasing amounts of attention over recent years due to the emergence of pervasive computing applications. Nevertheless, it still remains a challenge to meet the needs of users while they are on the move. This paper introduces a novel approach for providing personalized, context-aware assistance services for users in mobile environments. Central to the approach is the use of ontological user profile modeling which captures various characteristics of a user in order to create a unique set of profile information. In addition, user profiles can adapt to changing user behavior, thus enabling services to respond to evolving user needs and preferences. We describe the overall system architecture of the proposed approach with special emphasis being placed on the user profile modelling and its expected utility based on a typical use case scenario, i.e., using a smart-phone to address the problem of the outdoor mobility of a person with Dementia. A prototype based on the Android OS is used to illustrate the application. The use of everyday technology for a real world problem highlights the potential and utility of our approach.

Assessing task compliance following mobile phone-based video reminders.

In the development of technology for people with mild dementia it is essential to achieve a combination of the features which provide both support and monitoring along with the ability to offer a level of personalization. Reminding support by means of personalized video reminders portraying a relative or friend combined with sensors to assess whether the requested task was performed lends itself as an ideal combination to achieve this aim. This study assesses the potential of using low cost, off the shelf sensors combined with a mobile phone-based video reminding system to assess compliance with task completion. A validation study has been conducted in a lab-based environment with 10 healthy young participants. The work presented discusses the implementation of the approach adopted, data analysis of the results attained along with outlining future developments of this approach.

Mining, knowledge and decision support.

Decision support systems (DSS) are software entities that assist the physician in the decision making process. They have found application in medicine due to the large amounts of data (e.g. laboratory measurements such as blood pressure, heart rate, body-mass index) and information (e.g. patient history, population statistics based on age and sex) that must be considered before diagnosing any disease or recommending a therapy. A well known example is the embedded software in defibrillators which allows a 'shock' to be delivered, by analyzing the electrocardiogram for known conditions (heart attack). The shock can restart the heart and timely delivery can resuscitate the patient. As well as assisting in primary diagnosis, a DSS can reduce medical error, assist compliance with clinical guidelines, improve efficiency of care delivery and improve quality of care. Decision support still has significant acceptance issues in clinical routine, but can achieve more prominence, as systems are demonstrated to provide effective knowledge based support. Data mining is often used to provide some insight to a data set and update our accepted knowledge. In this section, we discuss a study which examines where electrocardiographic information should be recorded from a patient's torso in order to increase diagnostic yield.

Effects of electrode placement errors in the EASI-derived 12-lead electrocardiogram.

In this study, we assess the effects of electrode placement error on the EASI-derived 12-lead electrocardiogram (ECG). The study data set consisted of 744 body surface potential map (BSPM) recordings. The BSPMs, each of which was made up of 117 leads, were recorded from a mixture of healthy, myocardial infarction, and left ventricular hypertrophy subjects. The BSPMs were interpolated to increase the number of data points in the region of the EASI recording electrodes I, E, and A and the precordial leads. This facilitated 3 experiments. Firstly, recording sites I, E, and A were simultaneously moved ±5 cm vertically, in 0.5 cm increments, from their correct locations. Secondly, recording sites I and A were moved horizontally, again up to ±5 cm, in 0.5 cm increments. Finally, all 6 precordial leads were moved vertically in 0.5 cm increments up to ±5 cm. At each movement step, the resulting 12-lead ECG was compared with the original 12-lead ECG. Root mean square error was determined along with the absolute difference in J-point amplitude. Although the EASI leads were found to be less sensitive to electrode misplacement than the standard precordial leads, it was found that when precordial leads were moved up to ±3 cm vertically, the resulting 12-lead ECG more accurately resembled the original 12-lead ECG than a 12-lead ECG reconstructed from accurately positioned EASI leads. Further work is required to establish the effects of electrode misplacement beyond the ±5 cm limits assessed in this study.

Eigenleads: ECG leads for maximizing information capture and improving SNR.

There is currently much interest in exploring new ways to optimize ECG acquisition. In the current study, we have investigated optimal configurations of ECG leads with respect to: 1) best signal magnitude (maximal signal variance) and 2) best reconstruction of the total body surface potential distribution and the 12-lead ECG. Principal component analysis was applied to a set of 117-lead body surface potential maps (BSPMs) recorded from 559 subjects. Three bipolar leads, referred to as "eigenleads," were identified from the extrema on the resulting eigenvectors. Recording sites for the three leads were largely located in the precordial region. The magnitude of the signals recorded from the eigenleads was calculated on a set of 185 unseen subjects. The accuracy of the eigenleads in the reconstruction of BSPMs and the 12-lead ECG was also assessed for each subject. These results were compared to existing limited lead systems. It was found that, when compared to conventional leads, eigenleads could be used to increase signal strength (rms voltage) by 27.9%, 39.0%, and 20.3% for P-waves, QRS segments, and STT segments, respectively. Although the eigenleads were not able to reconstruct total body surface information as well as the 12-lead ECG (24.4 mu V versus 20.2 mu V), the eigenleads did perform comparably with other limited lead systems in the estimation of the 12-lead ECG. In particular, the eigenleads performed well in the reconstruction of precordial leads in comparison to the EASI lead system and a limited lead system made up of a subset of precordial leads. The proposed leads are a suitable alternative limited leads system, and can be used to improve SNR. More work is needed to test the practicality of such leads.

Optimal electrocardiographic lead systems: practical scenarios in smart clothing and wearable health systems.

Advances in wearable health systems, from a smart textile, signal processing, and wireless communications perspective, have resulted in the recent deployment of such systems in real clinical and healthcare settings. Nevertheless, the problem of identifying the most appropriate sites from which biological parameters can be recorded still remains unsolved. This paper aims to asses the effects of various practical constraints that may be encountered when choosing electrocardiographic recording sites for wearable health systems falling within the category of smart shirts for cardiac monitoring and analysis. We apply a lead selection algorithm to a set of 192 lead body surface potential maps (BSPM) and simulate a number of practical constraints by only allowing selection of recording sites from specific regions available in the 192 lead array. Of the various scenarios that were investigated, we achieved the best results when the selection process to identify the recording sites was constrained to an area around the precordial region. The top ten recording sites chosen in this region exhibited an rms voltage error of 25.8 mu V when they were used to estimate total ECG information. The poorest performing scenario was that which constrained the selection to two vertical strips on the posterior surface. The top ten recording sites chosen in this scenario exhibited an rms voltage error of 41.1 muV. In general, it was observed that out of all the scenarios investigated, those which constrained available regions to the posterior and lateral surfaces performed less favorably than those where electrodes could also be chosen on the anterior surface. The overall results from our approach have validated the proposed algorithm and its ability to select optimal recording sites taking into consideration the practical constraints that may exist with smart shirts.

Lead selection: old and new methods for locating the most electrocardiogram information.

The present article summarizes the work presented in several key studies over the past 3 decades in the area of limited lead selection. Specifically, we summarize the pioneering research of those investigators searching for the most "signal" information and those searching for the most "diagnostic" information. Initially, we present the work conducted by Barr et al and, later, Lux et al who investigated body surface potential maps to locate those recording sites containing the most signal information that subsequently facilitated the estimation of the electrical potentials at all other areas of the thoracic surface. Subsequently, the discussion focuses on the early work conducted by Kornreich et al, who used statistical methods to identify those recording sites containing optimal measurement features to improve upon the identification of different disease types. In addition to the aforementioned work, an overview of more recent complementary work is summarized.

Selection of optimal recording sites for limited lead body surface potential mapping in myocardial infarction and left ventricular hypertrophy.

A lead selection algorithm was applied to find optimal recording sites for limited lead body surface potential maps. The studied population consisted of a set of 117 lead body surface potential maps recorded from 744 subjects (229, normal; 278, with myocardial infraction [MI]; and 237, with left ventricular hypertrophy [LVH]). One generic lead set derived from all disease groups was found. Also found were 3 disease-specific lead sets (normal, MI, and LVH) and one specific to abnormal subjects (MI and LVH combined). The performance of each lead set in estimating data from other disease groups was largely similar. This was with the exception of leads specific to LVH in the estimation of normal data and normal leads in the estimation of LVH data. Here, the difference was found to be significant (P < .001). The top 6 recording sites in each lead set did not occupy the same positions as the 6 precordial leads. Although disease-specific lead sets are of limited practical use, this study has illustrated that, largely, there is little difference between the performance of different lead sets. The suboptimality of the 6 precordial leads has also been illustrated.

The development of personalised cognitive prosthetics.

Persons suffering from mild dementia can benefit from a form of cognitive prosthetic which can be used to assist them with their day to day activities. Within our current work we are aiming to develop a successful user-validated cognitive prosthetic for persons with mild dementia. We have devised a three phased waterfall methodology to support our developments. Based on the evaluation of the first of these phases which involved the processes of user requirements gathering, prototype development and evaluation of in situ deployment of the technology we have been able to guide the technical development within the second phase of our work. Within this paper we provide an overview of the first phase of our methodology and demonstrate how we have used the results from this to guide the second phase of our work, especially with regards to the notion of personalisation.

Personalisation and configuration of assistive technologies.

In this paper we present a multimedia tool that allows personalisation and configuration of an assistive technology. We have developed a tool called HomePUI that allows configuration of three disease domains; cognitive, physical and chronic. In this paper we present the rationale for our work, details of the developed tool and also present two case studies in which the tool has been used within cognitive and chronic disease management domains.