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.

The "Worktivity" mHealth intervention to reduce sedentary behaviour in the workplace: a feasibility cluster randomised controlled pilot study.

BACKGROUND: Office work generally consists of high amounts of sedentary behaviour (SB) which has been associated with negative health consequences. We developed the "WorktivIty" mobile app to help office workers reduce their SB through self-monitoring and feedback on sedentary time, prompts to break sedentary time, and educational facts. The aim of this paper is to report the feasibility of delivering the Worktivity intervention to desk-based office workers in the workplace setting and describe methodological considerations for a future trial. METHODS: We conducted a three-arm feasibility cluster randomised controlled pilot study over an 8-week period with full time-desk based employees. Clustered randomisation was to one of three groups: Worktivity mobile app (MA; n = 20), Worktivity mobile app plus SSWD (MA+SSWD; n = 20), or Control (C; n = 16). Feasibility was assessed using measures of recruitment and retention, intervention engagement, intervention delivery, completion rates and usable data, adverse events, and acceptability. RESULTS: Recruitment of companies to participate in this study was challenging (8% of those contacted), but retention of individual participants within the recruited groups was high (81% C, 90% MA + SSWD, 95% MA). Office workers' engagement with the app was moderate (on average 59%). Intervention delivery was partially compromised due to diminishing user engagement and technical issues related to educational fact delivery. Sufficient amounts of useable data were collected, however either missing or unusable data were observed with activPAL™, with data loss increasing at each follow up time point. No serious adverse events were identified during the study. The majority of participants agreed that the intervention could be implemented within the workplace setting (65% MA; 72% MA + SSWD) but overall satisfaction with the intervention was modest (58% MA; 39% MA + SSWD). CONCLUSIONS: The findings suggest that, in principle, it is feasible to implement a mobile app-based intervention in the workplace setting however the Worktivity intervention requires further technical refinements before moving to effectiveness trials. Challenges relating to the initial recruitment of workplaces and maintaining user engagement with the mHealth intervention over time need to be addressed prior to future large-scale implementation. Further research is needed to identify how best to overcome these challenges.

Data Mining and Fusion of Unobtrusive Sensing Solutions for Indoor Activity Recognition.

This paper proposes the fusion of data from unobtrusive sensing solutions for the recognition and classification of activities in home environments. The ability to recognize and classify activities can help in the objective monitoring of health and wellness trends in ageing adults. While the use of video and stereo cameras for monitoring activities provides an adequate insight, the privacy of users is not fully protected (i.e., users can easily be recognized from the images). Another concern is that widely used wearable sensors, such as accelerometers, have some disadvantages, such as limited battery life, adoption issues and wearability. This study investigates the use of low-cost thermal sensing solutions capable of generating distinct thermal blobs with timestamps to recognize the activities of study participants. More than 11,000 thermal blobs were recorded from 10 healthy participants with two thermal sensors placed in a laboratory kitchen: (i) one mounted on the ceiling, and (ii) the other positioned on a mini tripod stand in the corner of the room. Furthermore, data from the ceiling thermal sensor were fused with data gleaned from the lateral thermal sensor. Contact sensors were used at each stage as the gold standard for timestamp approximation during data acquisition, which allowed the attainment of: (i) the time at which each activity took place, (ii) the type of activity performed, and (iii) the location of each participant. Experimental results demonstrated successful cluster-based activity recognition and classification with an average regression co-efficient of 0.95 for tested clusters and features. Also, an average accuracy of 95% was obtained for data mining models such as k-nearest neighbor, logistic regression, neural network and random forest on Evaluation Test.Clinical Relevance-This study presents an unobtrusive (i.e., privacy-friendly) solution for activity recognition and classification, for the purposes of profiling trends in health and wellbeing.

Leveraging Wearable Sensors for Human Daily Activity Recognition with Stacked Denoising Autoencoders.

Activity recognition has received considerable attention in many research fields, such as industrial and healthcare fields. However, many researches about activity recognition have focused on static activities and dynamic activities in current literature, while, the transitional activities, such as stand-to-sit and sit-to-stand, are more difficult to recognize than both of them. Consider that it may be important in real applications. Thus, a novel framework is proposed in this paper to recognize static activities, dynamic activities, and transitional activities by utilizing stacked denoising autoencoders (SDAE), which is able to extract features automatically as a deep learning model rather than utilize manual features extracted by conventional machine learning methods. Moreover, the resampling technique (random oversampling) is used to improve problem of unbalanced samples due to relatively short duration characteristic of transitional activity. The experiment protocol is designed to collect twelve daily activities (three types) by using wearable sensors from 10 adults in smart lab of Ulster University, the experiment results show the significant performance on transitional activity recognition and achieve the overall accuracy of 94.88% on three types of activities. The results obtained by comparing with other methods and performances on other three public datasets verify the feasibility and priority of our framework. This paper also explores the effect of multiple sensors (accelerometer and gyroscope) to determine the optimal combination for activity recognition.

Iterative four-phase development of a theory-based digital behaviour change intervention to reduce occupational sedentary behaviour.

INTRODUCTION: As high amounts of occupational sitting have been associated with negative health consequences, designing workplace interventions to reduce sedentary behaviour (SB) is of public health interest. Digital technology may serve as a cost-effective and scalable platform to deliver such an intervention. This study describes the iterative development of a theory-based, digital behaviour change intervention to reduce occupational SB. METHODS: The behaviour change wheel and The Behaviour Change Technique Taxonomy were used to guide the intervention design process and form a basis for selecting the intervention components. The development process consisted of four phases: phase 1 - preliminary research, phase 2 - consensus workshops, phase 3 - white boarding and phase 4 - usability testing. RESULTS: The process led to the development and refinement of a smartphone application - Worktivity. The core component was self-monitoring and feedback of SB at work, complemented by additional features focusing on goal setting, prompts and reminders to break up prolonged periods of sitting, and educational facts and tips. Key features of the app included simple data entry and personalisation based on each individual's self-reported sitting time. Results from the 'think-aloud' interviews (n=5) suggest Worktivity was well accepted and that users were positive about its features. CONCLUSION: This study led to the development of Worktivity, a theory-based and user-informed mobile app intervention to reduce occupational SB. It is the first app of its kind developed with the primary aim of reducing occupational SB using digital self-monitoring. This paper provides a template to guide others in the development and evaluation of technology-supported behaviour change interventions.

Exploring the Views of Desk-Based Office Workers and Their Employers' Beliefs Regarding Strategies to Reduce Occupational Sitting Time, With an Emphasis on Technology-Supported Approaches.

OBJECTIVE: Employee and employer views regarding how technology-supported strategies can best meet their needs to reduce occupational sitting are not well known. This study explored target user and key stakeholder beliefs regarding strategies to reduce occupational sitting focusing on technology-supported approaches. METHODS: Nine focus groups and two interviews (employees, n = 27; employers, n = 19; board members, n = 2) were conducted, transcribed, and analyzed thematically. RESULTS: The main barrier to reducing sitting was job-related tasks taking primary priority. Intervention designers should consider individual preferences, environmental factors, judgmental culture, productivity concerns, and staff knowledge. Technology-supported strategies such as smartphone applications, computer software, wearables, and emails were deemed to be useful tools to provide prompts and allow behavioral self-monitoring in an easily individualized manner. CONCLUSIONS: Technology-supported strategies were seen to be valuable approaches and might fruitfully be incorporated into future interventions to reduce sitting time.

Using computer, mobile and wearable technology enhanced interventions to reduce sedentary behaviour: a systematic review and meta-analysis.

BACKGROUND: High levels of sedentary behaviour (SB) are associated with negative health consequences. Technology enhanced solutions such as mobile applications, activity monitors, prompting software, texts, emails and websites are being harnessed to reduce SB. The aim of this paper is to evaluate the effectiveness of such technology enhanced interventions aimed at reducing SB in healthy adults and to examine the behaviour change techniques (BCTs) used. METHODS: Five electronic databases were searched to identify randomised-controlled trials (RCTs), published up to June 2016. Interventions using computer, mobile or wearable technologies to facilitate a reduction in SB, using a measure of sedentary time as an outcome, were eligible for inclusion. Risk of bias was assessed using the Cochrane Collaboration's tool and interventions were coded using the BCT Taxonomy (v1). RESULTS: Meta-analysis of 15/17 RCTs suggested that computer, mobile and wearable technology tools resulted in a mean reduction of -41.28 min per day (min/day) of sitting time (95% CI -60.99, -21.58, I2 = 77%, n = 1402), in favour of the intervention group at end point follow-up. The pooled effects showed mean reductions at short (≤ 3 months), medium (>3 to 6 months), and long-term follow-up (>6 months) of -42.42 min/day, -37.23 min/day and -1.65 min/day, respectively. Overall, 16/17 studies were deemed as having a high or unclear risk of bias, and 1/17 was judged to be at a low risk of bias. A total of 46 BCTs (14 unique) were coded for the computer, mobile and wearable components of the interventions. The most frequently coded were "prompts and cues", "self-monitoring of behaviour", "social support (unspecified)" and "goal setting (behaviour)". CONCLUSION: Interventions using computer, mobile and wearable technologies can be effective in reducing SB. Effectiveness appeared most prominent in the short-term and lessened over time. A range of BCTs have been implemented in these interventions. Future studies need to improve reporting of BCTs within interventions and address the methodological flaws identified within the review through the use of more rigorously controlled study designs with longer-term follow-ups, objective measures of SB and the incorporation of strategies to reduce attrition. TRIAL REGISTRATION: The review protocol was registered with PROSPERO: CRD42016038187.

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.

The Empowering Role of Mobile Apps in Behavior Change Interventions: The Gray Matters Randomized Controlled Trial.

BACKGROUND: Health education and behavior change programs targeting specific risk factors have demonstrated their effectiveness in reducing the development of future diseases. Alzheimer disease (AD) shares many of the same risk factors, most of which can be addressed via behavior change. It is therefore theorized that a behavior change intervention targeting these risk factors would likely result in favorable rates of AD prevention. OBJECTIVE: The objective of this study was to reduce the future risk of developing AD, while in the short term promoting vascular health, through behavior change. METHODS: The study was an interventional randomized controlled trial consisting of subjects who were randomly assigned into either treatment (n=102) or control group (n=42). Outcome measures included various blood-based biomarkers, anthropometric measures, and behaviors related to AD risk. The treatment group was provided with a bespoke "Gray Matters" mobile phone app designed to encourage and facilitate behavior change. The app presented evidence-based educational material relating to AD risk and prevention strategies, facilitated self-reporting of behaviors across 6 behavioral domains, and presented feedback on the user's performance, calculated from reported behaviors against recommended guidelines. RESULTS: This paper explores the rationale for a mobile phone-led intervention and details the app's effect on behavior change and subsequent clinical outcomes. Via the app, the average participant submitted 7.3 (SD 3.2) behavioral logs/day (n=122,719). Analysis of these logs against primary outcome measures revealed that participants who improved their high-density lipoprotein cholesterol levels during the study duration answered a statistically significant higher number of questions per day (mean 8.30, SD 2.29) than those with no improvement (mean 6.52, SD 3.612), t97.74=-3.051, P=.003. Participants who decreased their body mass index (BMI) performed significantly better in attaining their recommended daily goals (mean 56.21 SD 30.4%) than those who increased their BMI (mean 40.12 SD 29.1%), t80 = -2.449, P=.017. In total, 69.2% (n=18) of those who achieved a mean performance percentage of 60% or higher, across all domains, reduced their BMI during the study, whereas 60.7% (n=34) who did not, increased their BMI. One-way analysis of variance of systolic blood pressure category changes showed a significant correlation between reported efforts to reduce stress and category change as a whole, P=.035. An exit survey highlighted that respondents (n=83) reported that the app motivated them to perform physical activity (85.4%) and make healthier food choices (87.5%). CONCLUSIONS: In this study, the ubiquitous nature of the mobile phone excelled as a delivery platform for the intervention, enabling the dissemination of educational intervention material while simultaneously monitoring and encouraging positive behavior change, resulting in desirable clinical effects. Sustained effort to maintain the achieved behaviors is expected to mitigate future AD risk. TRIAL REGISTRATION: ClinicalTrails.gov NCT02290912; https://clinicaltrials.gov/ct2/show/NCT02290912 (Archived by WebCite at http://www.webcitation.org/6ictUEwnm).

Assessing app quality through expert peer review: a case study from the gray matters study.

Health apps focused on inciting behavior change are becoming increasingly popular. Nevertheless, many lack underlying evidence base, scientific credibility and have limited clinical effectiveness. It is therefore important that apps are well-informed, scientifically credible, peer reviewed and evidence based. This paper presents the use of the Mobile App Rating Scale (MARS) to assess the quality of the Grey Matters app, a cross platform app to deliver health education material and track behavior change across multi-domains with the aim of reducing the risk of developing Alzheimer's disease. The Gray Matters app shows promising results following reviews from 5 Expert raters, achieving a mean overall MARS score of 4.45 ± 0.14. Future work will involve undertaking of a detailed content analysis of behavior change apps to identify common themes and features which may lead to the successful facilitation of sustained behavior change.

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 %.

Doorstep: A doorbell security system for the prevention of doorstep crime.

Safety and security rank highly in the priorities of older people on both an individual and policy level. Older people are commonly targeted as victims of doorstep crime, as they can be perceived as being vulnerable. As a result, this can have a major effect on the victim's health and wellbeing. There have been numerous prevention strategies implemented in an attempt to combat and reduce the number of doorstep crimes. There is, however, little information available detailing the effectiveness of these strategies and how they impact on the fear of crime, particularly with repeat victims. There is therefore clear merit in the creation and piloting of a technology based solution to combat doorstep crime. This paper presents a developed solution to provide increased security for older people within their home.

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).

A usability evaluation of medical software at an expert conference setting.

INTRODUCTION: A usability test was employed to evaluate two medical software applications at an expert conference setting. One software application is a medical diagnostic tool (electrocardiogram [ECG] viewer) and the other is a medical research tool (electrode misplacement simulator [EMS]). These novel applications have yet to be adopted by the healthcare domain, thus, (1) we wanted to determine the potential user acceptance of these applications and (2) we wanted to determine the feasibility of evaluating medical diagnostic and medical research software at a conference setting as opposed to the conventional laboratory setting. METHODS: The medical diagnostic tool (ECG viewer) was evaluated using seven delegates and the medical research tool (EMS) was evaluated using 17 delegates that were recruited at the 2010 International Conference on Computing in Cardiology. Each delegate/participant was required to use the software and undertake a set of predefined tasks during the session breaks at the conference. User interactions with the software were recorded using screen-recording software. The 'think-aloud' protocol was also used to elicit verbal feedback from the participants whilst they attempted the pre-defined tasks. Before and after each session, participants completed a pre-test and a post-test questionnaire respectively. RESULTS: The average duration of a usability session at the conference was 34.69 min (SD=10.28). However, taking into account that 10 min was dedicated to the pre-test and post-test questionnaires, the average time dedication to user interaction of the medical software was 24.69 min (SD=10.28). Given we have shown that usability data can be collected at conferences, this paper details the advantages of conference-based usability studies over the laboratory-based approach. For example, given delegates gather at one geographical location, a conference-based usability evaluation facilitates recruitment of a convenient sample of international subject experts. This would otherwise be very expensive to arrange. A conference-based approach also allows for data to be collected over a few days as opposed to months by avoiding administration duties normally involved in laboratory based approach, e.g. mailing invitation letters as part of a recruitment campaign. Following analysis of the user video recordings, 41 (previously unknown) use errors were identified in the advanced ECG viewer and 29 were identified in the EMS application. All use errors were given a consensus severity rating from two independent usability experts. Out of a rating scale of 4 (where 1=cosmetic and 4=critical), the average severity rating for the ECG viewer was 2.24 (SD=1.09) and the average severity rating for the EMS application was 2.34 (SD=0.97). We were also able to extract task completion rates and times from the video recordings to determine the effectiveness of the software applications. For example, six out of seven tasks were completed by all participants when using both applications. This statistic alone suggests both applications already have a high degree of usability. As well as extracting data from the video recordings, we were also able to extract data from the questionnaires. Using a semantic differential scale (where 1=poor and 5=excellent), delegates highly rated the 'responsiveness', 'usefulness', 'learnability' and the 'look and feel' of both applications. CONCLUSION: This study has shown the potential user acceptance and user-friendliness of the novel EMS and the ECG viewer applications within the healthcare domain. It has also shown that both medical diagnostic software and medical research software can be evaluated for their usability at an expert conference setting. The primary advantage of a conference-based usability evaluation over a laboratory-based evaluation is the high concentration of experts at one location, which is convenient, less time consuming and less expensive.

A smartphone application to evaluate technology adoption and usage in persons with dementia.

Dementia affects a proportionally large number of the older population, presenting a set of symptoms that cause cognitive decline and negatively affect quality of life. Technology offers an assistive role for some of these symptoms, specifically in addressing forgetfulness. Current works have explored the benefits of reminding technology, which whilst useful is only effective for those who adopt the technology. Therefore it is of merit to establish the individual parameters that characterize an adopter and non-adopter, to better target future interventions and their deployment. To aid the collection of this data a smartphone app was developed for persons with dementia. It has been designed as both a reminder application to help those with dementia accommodate their forgetfulness and a data collection tool to log usage and compliance with reminders. The app has been evaluated by a pre-pilot cohort (n=9) and was found to have a mean reminder acknowledgement of 73.09%.

A sensor and video based ontology for activity recognition in smart environments.

Activity recognition is used in a wide range of applications including healthcare and security. In a smart environment activity recognition can be used to monitor and support the activities of a user. There have been a range of methods used in activity recognition including sensor-based approaches, vision-based approaches and ontological approaches. This paper presents a novel approach to activity recognition in a smart home environment which combines sensor and video data through an ontological framework. The ontology describes the relationships and interactions between activities, the user, objects, sensors and video data.

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%.

Methods for presenting and visualising electrocardiographic data: From temporal signals to spatial imaging.

INTRODUCTION: The electrocardiogram (ECG) is a recording of the electrical activity of the heart. It is commonly used to non-invasively assess the cardiac activity of a patient. Since 1938, ECG data has been visualised as 12 scalar traces (known as the standard 12-lead ECG). Although this is known as the standard approach, there has been a myriad of alternative methods proposed to visualise ECG data. The purpose of this paper is to provide an overview of these methods and to introduce the field of ECG visualisation to early stage researchers. A scientific purpose is to consider the future of ECG visualisation within routine clinical practice. METHODS: This paper structures the different ECG visualisation methods using four categories, i.e. temporal, vectorial, spatial and interactive. Temporal methods present the data with respect to time, vectorial methods present data with respect to direction and magnitude, spatial methods present data in 2D or 3D space and interactive methods utilise interactive computing to facilitate efficient interrogation of ECG data at different levels of detail. CONCLUSION: Spatial visualisation has been around since its introduction by Waller and vector based visualisation has been around since the 1920s. Given these approaches have already been given the 'test of time', they are unlikely to be replaced as the standard in the near future. Instead of being replaced, the standard is more likely to be 'supplemented'. However, the design and presentation of these ECG visualisation supplements need to be universally standardised. Subsequent to the development of 'standardised supplements', as a requirement, they could then be integrated into all ECG machines. We recognise that without intuitive software and interactivity on mobile devices (e.g. tablet PCs), it is impractical to integrate the more advanced ECG visualisation methods into routine practice (i.e. epicardial mapping using an inverse solution).

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.

WiiPD--objective home assessment of Parkinson's disease using the Nintendo Wii remote.

Current clinical methods for the assessment of Parkinson's disease suffer from inconvenience, infrequency and subjectivity. WiiPD is an approach for the objective home based assessment of Parkinson's disease which utilizes the intuitive and sensor rich Nintendo Wii Remote. Combined with an electronic patient diary, a suite of mini-games, a metric analyzer, and a visualization engine, we propose that this system can complement existing clinical practice by providing objective metrics gathered frequently over extended periods of time. In this paper we detail the approach and introduce a series of metrics deemed capable of quantifying the severity of tremor and bradykinesia in those with Parkinson's disease. The system has been tested on a 71 year old participant with Parkinson's disease over a period of 15 days, a 72 year old control user without Parkinson's disease, and a group of 8 young adults. Results indicate a clear correlation between patient self rating scores of tremor severity and metric values obtained, in addition to clear differences in metrics obtained from each user group. These results suggest that this approach is capable of indicating the presence and severity of the motor symptoms of Parkinson's disease that affect arm motor control.