Clinical Decision Support Systems with Game-based Environments, Monitoring Symptoms of Parkinson’s Disease with Exergames

Parkinson’s Disease (PD) is a malady caused by progressive neuronal degeneration, deriving in several physical and cognitive symptoms that worsen with time. Like many other chronic diseases, it requires constant monitoring to perform medication and therapeutic adjustments. This is due to the significant variability in PD symptomatology and progress between patients. At the moment, this monitoring requires substantial participation from caregivers and numerous clinic visits. Personal diaries and questionnaires are used as data sources for medication and therapeutic adjustments. The subjectivity in these data sources leads to suboptimal clinical decisions. Therefore, more objective data sources are required to better monitor the progress of individual PD patients. A potential contribution towards more objective monitoring of PD is clinical decision support systems. These systems employ sensors and classification techniques to provide caregivers with objective information for their decision-making. This leads to more objective assessments of patient improvement or deterioration, resulting in better adjusted medication and therapeutic plans. Hereby, the need to encourage patients to actively and regularly provide data for remote monitoring remains a significant challenge. To address this challenge, the goal of this thesis is to combine clinical decision support systems with game-based environments. More specifically, serious games in the form of exergames, active video games that involve physical exercise, shall be used to deliver objective data for PD monitoring and therapy. Exergames increase engagement while combining physical and cognitive tasks. This combination, known as dual-tasking, has been proven to improve rehabilitation outcomes in PD: recent randomized clinical trials on exergame-based rehabilitation in PD show improvements in clinical outcomes that are equal or superior to those of traditional rehabilitation. In this thesis, we present an exergame-based clinical decision support system model to monitor symptoms of PD. This model provides both objective information on PD symptoms and an engaging environment for the patients. The model is elaborated, prototypically implemented and validated in the context of two of the most prominent symptoms of PD: (1) balance and gait, as well as (2) hand tremor and slowness of movement (bradykinesia). While balance and gait affections increase the risk of falling, hand tremors and bradykinesia affect hand dexterity. We employ Wii Balance Boards and Leap Motion sensors, and digitalize aspects of current clinical standards used to assess PD symptoms. In addition, we present two dual-tasking exergames: PDDanceCity for balance and gait, and PDPuzzleTable for tremor and bradykinesia. We evaluate the capability of our system for assessing the risk of falling and the severity of tremor in comparison with clinical standards. We also explore the statistical significance and effect size of the data we collect from PD patients and healthy controls. We demonstrate that the presented approach can predict an increased risk of falling and estimate tremor severity. Also, the target population shows a good acceptance of PDDanceCity and PDPuzzleTable. In summary, our results indicate a clear feasibility to implement this system for PD. Nevertheless, long-term randomized clinical trials are required to evaluate the potential of PDDanceCity and PDPuzzleTable for physical and cognitive rehabilitation effects

Synthesis of normal and abnormal heart sounds using Generative Adversarial Networks

En esta tesis doctoral se presentan diferentes métodos propuestos para el análisis y síntesis de sonidos cardíacos normales y anormales, logrando los siguientes aportes al estado del arte: i) Se implementó un algoritmo basado en la transformada wavelet empírica (EWT) y la energía promedio normalizada de Shannon (NASE) para mejorar la etapa de segmentación automática de los sonidos cardíacos; ii) Se implementaron diferentes técnicas de extracción de características para las señales cardíacas utilizando los coeficientes cepstrales de frecuencia Mel (MFCC), los coeficientes de predicción lineal (LPC) y los valores de potencia. Además, se probaron varios modelos de Machine Learning para la clasificación automática de sonidos cardíacos normales y anormales; iii) Se diseñó un modelo basado en Redes Adversarias Generativas (GAN) para generar sonidos cardíacos sintéticos normales. Además, se implementa un algoritmo de eliminación de ruido utilizando EWT, lo que permite una disminución en la cantidad de épocas y el costo computacional que requiere el modelo GAN; iv) Finalmente, se propone un modelo basado en la arquitectura GAN, que consiste en refinar señales cardíacas sintéticas obtenidas por un modelo matemático con características de señales cardíacas reales. Este modelo se ha denominado FeaturesGAN y no requiere una gran base de datos para generar diferentes tipos de sonidos cardíacos. Cada uno de estos aportes fueron validados con diferentes métodos objetivos y comparados con trabajos publicados en el estado del arte, obteniendo resultados favorables.DoctoradoDoctor en Ingeniería Eléctrica y Electrónic

Actas de SABI2020

Los temas salientes incluyen un marcapasos pulmonar que promete complementar y eventualmente sustituir la conocida ventilación mecánica por presión positiva (intubación), el análisis de la marchaespontánea sin costosos equipamientos, las imágenes infrarrojas y la predicción de la salud cardiovascular en temprana edad por medio de la biomecánica arterial

An automatic wearable multi-sensor based gait analysis system for older adults.

Gait abnormalities in older adults are very common in clinical practice. They lead to serious adverse consequences such as falls and injury resulting in increased care cost. There is therefore a national imperative to address this challenge. Currently gait assessment is done using standardized clinical tools dependent on subjective evaluation. More objective gold standard methods (motion capture systems such as Qualisys and Vicon) to analyse gait rely on access to expensive complex equipment based in gait laboratories. These are not widely available for several reasons including a scarcity of equipment, need for technical staff, need for patients to attend in person, complicated time consuming procedures and overall expense. To broaden the use of accurate quantitative gait monitoring and assessment, the major goal of this thesis is to develop an affordable automatic gait analysis system that will provide comprehensive gait information and allow use in clinic or at home. It will also be able to quantify and visualize gait parameters, identify gait variables and changes, monitor abnormal gait patterns of older people in order to reduce the potential for falling and support falls risk management. A research program based on conducting experiments on volunteers is developed in collaboration with other researchers in Bournemouth University, The Royal Bournemouth Hospital and care homes. This thesis consists of five different studies toward addressing our major goal. Firstly, a study on the effects on sensor output from an Inertial Measurement Unit (IMU) attached to different anatomical foot locations. Placing an IMU over the bony prominence of the first cuboid bone is the best place as it delivers the most accurate data. Secondly, an automatic gait feature extraction method for analysing spatiotemporal gait features which shows that it is possible to extract gait features automatically outside of a gait laboratory. Thirdly, user friendly and easy to interpret visualization approaches are proposed to demonstrate real time spatiotemporal gait information. Four proposed approaches have the potential of helping professionals detect and interpret gait asymmetry. Fourthly, a validation study of spatiotemporal IMU extracted features compared with gold standard Motion Capture System and Treadmill measurements in young and older adults is conducted. The results obtained from three experimental conditions demonstrate that our IMU gait extracted features are highly valid for spatiotemporal gait variables in young and older adults. In the last study, an evaluation system using Procrustes and Euclidean distance matrix analysis is proposed to provide a comprehensive interpretation of shape and form differences between individual gaits. The results show that older gaits are distinguishable from young gaits. A pictorial and numerical system is proposed which indicates whether the assessed gait is normal or abnormal depending on their total feature values. This offers several advantages: 1) it is user friendly and is easy to set up and implement; 2) it does not require complex equipment with segmentation of body parts; 3) it is relatively inexpensive and therefore increases its affordability decreasing health inequality; and 4) its versatility increases its usability at home supporting inclusivity of patients who are home bound. A digital transformation strategy framework is proposed where stakeholders such as patients, health care professionals and industry partners can collaborate through development of new technologies, value creation, structural change, affordability and sustainability to improve the diagnosis and treatment of gait abnormalities

Wearable sensor technologies applied for post-stroke rehabilitation

Stroke is a common cerebrovascular disease that is recognized as one of the leading causes of death and ongoing disability around the globe. Stroke can lead to losses of various body functions depending on the affected area of the brain and leave significant impacts to the victim’s daily life. Post-stroke rehabilitation plays an important role in improving the life quality of stroke survivors. Properly designed rehabilitation training programs can not only prevent further functional deterioration, but also helps patients gradually regain their body functionalities. However, the delivery of rehabilitation service can be a complex and labour intensive task. In conventional rehabilitation systems, the chart-based ordinal scales are considered the dominant tools for impairment assessment and the administration of the scales primarily relies on the doctor’s manual observation. Measuring instruments such as strain gauge and force platforms can sometimes be used to collect quantitative evidence for some of the body functions such as grip strength and balance. However, the evaluation of the patients’ impairment level using ordinal scales still depend on the human interpretation of the data which can be both subjective and inefficient. The preferred scale and evaluation standard also vary among institutions across different regions which make the comparison of data difficult and sometimes unreliable. Furthermore, the intensive manual supervision and support required in rehabilitation training session limits the accessibility of the service as the regular visit to qualified hospital can be onerous for many patients and the associated cost can impose an enormous financial burden on both the government and the households. The situation can be even more challenging in developing countries due to higher growing rate of stroke population and more limited medical resources. The works presented in this thesis are focused on exploring the possibilities of integrating wearable sensor and pattern recognition techniques to improve the efficiency and the effectiveness of post-stroke rehabilitation by addressing the abovementioned issues. The study was initiated by a comprehensive literature review on the latest motion tracking technologies and non-visual based Inertia Measurement Unit (IMU) had been selected as the most suitable candidate for motion sensing in unsupervised training environment due to its low-cost and easy-to-operate characteristics. Following the design and construction of the 6-axis IMU based Body Area Network (BAN), a series of stroke patient motion data collection experiments had been conducted in conjunction with the Jiaxing 2nd Hospital Rehabilitation Centre in Zhejiang province, China. The collected motion samples were then investigated using various signal processing algorithms and pattern recognition techniques to achieve the three major objectives: automatic impairment level classification for reducing human effort involved in regular clinical assessment, single-index based limb mobility evaluation for providing objective evidence to support unified body function assessment standards, and training motion classification for enabling home or community based rehabilitation training with reduced supervision. At last, the study has been further expanded by incorporating surface Electromyography (sEMG) signal sampled during rehabilitation exercises as an alternative input to enhance accurate impairment level classification. The outcome of the investigations demonstrate that the wearable technology can play an important role within a tele-rehabilitation system by providing objective, accurate and often realtime indications of the recovery process as well as the assistance for training management

Investigation of the effects of transcutaneous electrical stimulation on physiological stress, marksmanship, and cognitive performance

Military training and operations can place significant demands on cognitive and physical resources of service members, resulting in heightened stress and fatigue, elevated risk of accidents and injuries, and diminished cognitive and occupational performance. Transcutaneous electrical stimulation (TES) is a novel, non-invasive neuromodulatory technique being investigated as a means to improve alertness and preserve performance under stress with few-to-no side effects. Despite the recent increase in research using TES, few studies have explored the effects of stimulation of the trigeminal nerve on cognition and the human stress response. Therefore, the aims of this study were to elucidate the effects of TES on biochemical and physiological responses to stress, cognition, and marksmanship performance under cognitive load. Participants in this repeated measures, crossover-design study included 23 healthy male (n = 18) and female (n = 5) civilians and members of the military ranging in age from 19 to 37 (mean 24.00 ± 5.65) years. Study procedures occurred in the afternoon on five consecutive days, including two testing days involving administration of active or sham TES to the right supraorbital region of the face using a commercially-available device (Thync One, Cerevast Therapeutics). To evaluate the effects of TES on the stress response, participants were required to complete a prolonged, cognitively challenging target discrimination task using a simulated firing range, which has been previously demonstrated to induce a reliable stress response in human research volunteers. Computer-assisted cognitive tasks were administered before and after rifle marksmanship in order to provide complementary assessment of functional domains challenged during the marksmanship task. Salivary markers of cortisol and α-amylase were collected at several time points during the testing day, and electrocardiography (ECG) and photoplethysmography (PPG), both markers of heart rate variability and stress responding, were monitored continuously. Linear mixed models with random slopes were used to analyze the effect of stimulation condition (active versus sham TES) on marksmanship and cognitive, physiological, and salivary outcomes across the testing period and at each measurement time point. No significant effects of stimulation condition or the interactions between stimulation condition and measurement time point were found for salivary stress biomarkers (punadj range 0.12 – 0.98) or for cognitive (punadj range 0.25 – 0.88) and physical workload (punadj range 0.31 – 0.79). There were no significant effects of stimulation condition on time-series indicators of heart rate variability (punadj range 0.10 – 0.96) except for pNN50 when measured with PPG (β = -4.97, punadj = 0.04, padj = n.s., d < 0.01). There were, however, significant stimulation condition by time interaction effects on mean heart rate, mean R-R interval, SDNN, RMSSD, and pNN50 (punadj range 0.12 – 0.98, d range < 0.01 – 0.02), indicating that trigeminal TES using the Thync One device increased activity of both the sympathetic and parasympathetic nervous systems during marksmanship and cognitive testing. Similar effects were noted on frequency-series indicators of heart rate variability using both ECG and PPG, in which stimulation condition effects were noted on ECG high frequency absolute (β = 8.50, punadj < 0.01, padj = 0.01, d < 0.01) and relative powers (β = -8.54, punadj < 0.01, padj = 0.01, d < 0.01), as well as PPG very low frequency power (β = -367.98, punadj < 0.01, padj = n.s., d = 0.12). Effects of the interaction between stimulation condition and measurement time point were noted on very low, low, and high frequency powers (punadj range < 0.01 – 0.048, d range < 0.01 – 0.21), as well as the ratio of low- to high-frequency powers in ECG (punadj range < 0.01 – 0.048, d < 0.01 for all). These results also suggest that trigeminal TES increased activity of both the sympathetic and parasympathetic nervous systems during marksmanship and cognitive testing. Furthermore, significant effects of stimulation condition were noted on marksmanship shot accuracy (β = 0.14, punadj = 0.01, padj = n.s., d = 0.60) and distance of shots from the targets’ center of mass (β = -0.08, punadj = 0.02, padj = n.s., d = 0.56), indicating that trigeminal TES impaired shot accuracy. There were also significant condition-by-time interaction effects on target detection latency (β = 220.46, punadj = 0.04, padj = n.s., d = 0.49); significant impairments in shot latency observed during the first marksmanship session in the active TES condition only resolved by the second marksmanship session. There were no significant effects of TES on accuracy or response times for neuropsychological tasks assessing response inhibition, sustained attention, and working memory (punadj range 0.09 – 0.98). Active trigeminal TES did, however, significantly reduce the standard deviation of response times on a measure of sustained attention and response inhibition (β = -16.29, punadj = 0.045, padj = n.s., d = 0.43). Although the literature suggests that TES may benefit stress and performance, these results do not support that conclusion. Overall, these analyses found that TES using a commercially available device did not influence chemical biomarkers of stress, but did influence markers of physiological stress, as well as cognitive and marksmanship performance under high cognitive load. TES was associated with impairments in marksmanship performance as well as increases in both sympathetic and parasympathetic nervous system activity. Further studies using different stimulation parameters, including multiple sessions of stimulation, will be necessary to more fully characterize possible influences of trigeminal nerve stimulation on stress responding and marksmanship performance or other military relevant tasks. In addition, this project underscores the need for more investigation into the mechanisms of effect of the Thync One device and other devices applying TES of the trigeminal nerve

Quantifying Quality of Life

Describes technological methods and tools for objective and quantitative assessment of QoL Appraises technology-enabled methods for incorporating QoL measurements in medicine Highlights the success factors for adoption and scaling of technology-enabled methods This open access book presents the rise of technology-enabled methods and tools for objective, quantitative assessment of Quality of Life (QoL), while following the WHOQOL model. It is an in-depth resource describing and examining state-of-the-art, minimally obtrusive, ubiquitous technologies. Highlighting the required factors for adoption and scaling of technology-enabled methods and tools for QoL assessment, it also describes how these technologies can be leveraged for behavior change, disease prevention, health management and long-term QoL enhancement in populations at large. Quantifying Quality of Life: Incorporating Daily Life into Medicine fills a gap in the field of QoL by providing assessment methods, techniques and tools. These assessments differ from the current methods that are now mostly infrequent, subjective, qualitative, memory-based, context-poor and sparse. Therefore, it is an ideal resource for physicians, physicians in training, software and hardware developers, computer scientists, data scientists, behavioural scientists, entrepreneurs, healthcare leaders and administrators who are seeking an up-to-date resource on this subject