Emotional Models for the Estimation of Arousal and Pleasure in Older Adults During Balance Rehabilitation Training.

Emotional computing has been previously applied to assess physiological behavior in a wide variety of tasks and activities. This study extends for the first time the use of emotional computing in the field of balance rehabilitation training. A proof-of-concept study was conducted to assess arousal and pleasure response to a range of physical exercises from the OTAGO and HOLOBALANCE balance rehabilitation programs with varying levels of difficulty and physical demand. Eleven participants were enrolled and performed a set of exercises wearing an ECG sensor, reporting arousal and pleasure at the end of each session. A dataset of 264 unique sessions was collected and used to extract heart rate variability (HRV) features from the measured RR intervals and automatically assess user arousal and pleasure, evaluating different classification algorithms. The results suggested that assessment of both emotions is feasible, reaching an accuracy of 72% and 74% for arousal and pleasure estimation, resnectively. Clinical Relevance- Arousal and pleasure are clinically useful indicators of patient's experience and engagement while performing balance rehabilitation exercises with novel sensing technologies and monitoring platforms.

Exploring the Acceptability and Feasibility of Providing a Balance Tele-Rehabilitation Programme to Older Adults at Risk for Falls: An Initial Assessment.

Falls are a major health concern. The HOLOBALANCE tele-rehabilitation system was developed to deliver an evidence based, multi-sensory balance rehabilitation programme, to the elderly at risk of falls. The system delivers a series of balance physiotherapy exercises and cognitive and auditory training tasks prescribed by an expert balance physiotherapist following an initial balance assessment. The HOLOBALANCE system uses augmented reality (AR) to deliver exercises and games, and records task performance via a combination of body worn sensors and a depth camera. The HOLOBALANCE tele-rehabilitation system provides feedback to the supervising clinical team regarding task performance, participant usage and user feedback. Herewith we present the findings from the first 25 study participants regarding the feasibility and acceptability of the proposed system. The results of the clinical study indicate that the system is acceptable by the end users and also feasible for using in hospital and home environments.

Exploring the knowledge and views of Greek Neurologists regarding Palliative Care Topics.

Palliative care for Parkinson's disease is characterized by inconsistency and varies from country to country. Although some countries have taken significant steps to include palliative care in their health programs, others, such as Greece, are still at an early stage. One step towards the widespread adoption of palliative care is the education of all stakeholders, especially clinicians. This paper presents a preliminary version of a curriculum toolkit for Palliative Care education in Parkinson's disease. Also, we explore Greek neurologists' knowledge of Palliative care based on a questionnaire and present their feedback on the topics included in this toolkit.Clinical Relevance-The toolkit aims to benefit patients in need of palliative care through promoting health literacy and further educating healthcare providers. The proposed toolkit provides all the necessary information to become sufficient knowledge and ultimately translate into clinical practice skills.

Education on palliative care for Parkinson patients: development of the "Best care for people with late-stage Parkinson's disease" curriculum toolkit.

BACKGROUND: Palliative care education among all stakeholders involved in the care of patients with late-stage Parkinson's disease is not adequate. In fact, there are many unmet educational and training needs as confirmed with a targeted, narrative literature review. METHODS: To address these needs we have developed the "Best Care for People with Late-Stage Parkinson's Disease" curriculum toolkit. The toolkit is based on recommendations and guidelines for training clinicians and other healthcare professionals involved in palliative care, educational material developed in recent research efforts for patients and caregivers with PD and consensus meetings of leading experts in the field. The final version of the proposed toolkit was drafted after an evaluation by external experts with an online survey, the feedback of which was statistically analysed with the chi-square test of independence to assess experts' views on the relevance and importance of the topics. A sentiment analysis was also done to complement statistics and assess the experts positive and negative sentiments for the curriculum topics based on their free text feedback. RESULTS: The toolkit is compliant with Kern's foundational framework for curriculum development, recently adapted to online learning. The statistical analysis of the online survey, aiming at toolkit evaluation from external experts (27 in total), confirms that all but one (nutrition in advanced Parkinson's disease) topics included, as well as their objectives and content, are highly relevant and useful. CONCLUSIONS: In this paper, the methods for the development of the toolkit, its stepwise evolution, as well as the toolkit implementation as a Massive Open Online Course (MOOC), are presented. The "Best Care for People with Late-Stage Parkinson' s disease" curriculum toolkit can provide high-quality and equitable education, delivered by an interdisciplinary team of educators. The toolkit can improve communication about palliative care in neurological conditions at international and multidisciplinary level. It can also offer continuing medical education for healthcare providers.

Automatic Absence Seizures Detection in EEG signals: An Unsupervised Module.

Absence seizures are expressed with distinctive spike-and-wave complexes in the electroencephalogram (EEG), which can be used to automatically distinguish them from other types of seizures and interictal activity. Considering the chaotic nature of the EEG signal, it is very unlikely that such continuous, repetitive patterns with strict periodic behavior would occur naturally under normal conditions. Searching for spectral activity in the range of 2.5-4.5 Hz and assessing the presence of synchronous, repeated patterns across multiple EEG channels in an unsupervised manner, the proposed methodology provides high absence seizure detection sensitivity of 93.94% with a low false detection rate of 0.168 FD/h using the open TUSZ dataset.

A Dynamic Bayesian Network Approach to Behavioral Modelling of Elderly People during a Home-based Augmented Reality Balance Physiotherapy Programme.

In this study, we propose a dynamic Bayesian network (DBN)-based approach to behavioral modelling of community dwelling older adults at risk for falls during the daily sessions of a hologram-enabled vestibular rehabilitation therapy programme. The component of human behavior being modelled is the level of frustration experienced by the user at each exercise, as it is assessed by the NASA Task Load Index. Herein, we present the topology of the DBN and test its inference performance on real-patient data.Clinical Relevance- Precise behavioral modelling will provide an indicator for tailoring the rehabilitation programme to each individual's personal psychological needs.

Decision Support for Medication Change of Parkinson's Disease Patients.

BACKGROUND AND OBJECTIVE: Parkinson's disease (PD) is a degenerative disorder of the central nervous system for which currently there is no cure. Its treatment requires long-term, interdisciplinary disease management, and usage of typical medications, including levodopa, dopamine agonists, and enzymes, such as MAO-B inhibitors. The key goal of disease management is to prolong patients' independence and keep their quality of life. Due to the different combinations of motor and non-motor symptoms from which PD patients suffer, in addition to existing comorbidities, the change of medications and their combinations is difficult and patient-specific. To help physicians, we developed two decision support models for PD management, which suggest how to change the medication treatment. METHODS: The models were developed using DEX methodology, which integrates the qualitative multi-criteria decision modelling with rule-based expert systems. The two DEX models differ in the way the decision rules were defined. In the first model, the decision rules are based on the interviews with neurologists (DEX expert model), and in the second model, they are formed from a database of past medication change decisions (DEX data model). We assessed both models on the Parkinson's Progression Markers Initiative (PPMI) and on a questionnaire answered by 17 neurologists from 4 European countries using accuracy measure and the Jaccard index. RESULTS: Both models include 15 sub-models that address possible medication treatment changes based on the given patients' current state. In particular, the models incorporate current state changes in patients' motor symptoms (dyskinesia intensity, dyskinesia duration, OFF duration), mental problems (impulsivity, cognition, hallucinations and paranoia), epidemiologic data (patient's age, activity level) and comorbidities (cardiovascular problems, hypertension and low blood pressure). The highest accuracy of the developed sub-models for 15 medication treatment changes ranges from 69.31 to 99.06 %. CONCLUSIONS: Results show that the DEX expert model is superior to the DEX data model. The results indicate that the constructed models are sufficiently adequate and thus fit for the purpose of making "second-opinion" suggestions to decision support users.

Microsurgery training: A combined educational program.

A proposed microsurgical training program is presented that includes all the existing training methods, such as simulation in nonliving models, virtual reality simulation system and exercise in living models. Our experience in microsurgery training over the last decades indicates the need of evolution in training programs. This can be achieved with the introduction of new technologies into education and training. The first primary results of the described training program are promising, however this system needs to be assessed by training greater number of microsurgeons. Furthermore, more complex scenarios (such as whole operations) should be inserted into the virtual reality simulation system to create a more interactive experience.

Evaluating the Performance of Balance Physiotherapy Exercises Using a Sensory Platform: The Basis for a Persuasive Balance Rehabilitation Virtual Coaching System.

Rehabilitation programs play an important role in improving the quality of life of patients with balance disorders. Such programs are usually executed in a home environment, due to lack of resources. This procedure usually results in poorly performed exercises or even complete drop outs from the programs, as the patients lack guidance and motivation. This paper introduces a novel system for managing balance disorders in a home environment using a virtual coach for guidance, instruction, and inducement. The proposed system comprises sensing devices, augmented reality technology, and intelligent inference agents, which capture, recognize, and evaluate a patient's performance during the execution of exercises. More specifically, this work presents a home-based motion capture and assessment module, which utilizes a sensory platform to recognize an exercise performed by a patient and assess it. The sensory platform comprises IMU sensors (Mbientlab MMR© 9axis), pressure insoles (Moticon©), and a depth RGB camera (Intel D415©). This module is designed to deliver messages both during the performance of the exercise, delivering personalized notifications and alerts to the patient, and after the end of the exercise, scoring the overall performance of the patient. A set of proof of concept validation studies has been deployed, aiming to assess the accuracy of the different components for the sub-modules of the motion capture and assessment module. More specifically, Euler angle calculation algorithm in 2D (R 2 = 0.99) and in 3D (R 2 = 0.82 in yaw plane and R 2 = 0.91 for the pitch plane), as well as head turns speed (R 2 = 0.96), showed good correlation between the calculated and ground truth values provided by experts' annotations. The posture assessment algorithm resulted to accuracy = 0.83, while the gait metrics were validated against two well-established gait analysis systems (R 2 = 0.78 for double support, R 2 = 0.71 for single support, R 2 = 0.80 for step time, R 2 = 0.75 for stride time (WinTrack©), R 2 = 0.82 for cadence, and R 2 = 0.79 for stride time (RehaGait©). Validation results provided evidence that the proposed system can accurately capture and assess a physiotherapy exercise within the balance disorders context, thus providing a robust basis for the virtual coaching ecosystem and thereby improve a patient's commitment to rehabilitation programs while enhancing the quality of the performed exercises. In summary, virtual coaching can improve the quality of the home-based rehabilitation programs as long as it is combined with accurate motion capture and assessment modules, which provides to the virtual coach the capacity to tailor the interaction with the patient and deliver personalized experience.

A Review of Virtual Coaching Systems in Healthcare: Closing the Loop With Real-Time Feedback.

This review focuses on virtual coaching systems that were designed to enhance healthcare interventions, combining the available sensing and system-user interaction technologies. In total, more than 1,200 research papers have been retrieved and evaluated for the purposes of this review, which were obtained from three online databases (i.e.,PubMed, Scopus and IEEE Xplore) using an extensive set of search keywords. After applying exclusion criteria, the remaining 41 research papers were used to evaluate the status of virtual coaching systems over the past 10 years and assess current and future trends in this field. The results suggest that in home coaching systems were mainly focused in promoting physical activity and a healthier lifestyle, while a wider range of medical domains was considered in systems that were evaluated in lab environment. In home patient monitoring with IoT devices and sensors was mostly limited to activity trackers, pedometers and heart rate monitoring. Real-time evaluations and personalized patient feedback was also found to be rather lacking in home coaching systems and this is the most alarming find of this analysis. Feasibility studies in controlled environment and an ongoing active research on Horizon 2020 funded projects, show that the future trends in this field are aiming to close the loop with automated patient monitoring, real-time evaluations and more precise interventions.

Predicting rapid progression of Parkinson's Disease at baseline patients evaluation.

The rate of Parkinson's Disease (PD) progression in the initial post-diagnosis years can vary significantly. In this work, a methodology for the extraction of the most informative features for predicting rapid progression of the disease is proposed, using public data from the Parkinson's Progression Markers Initiative (PPMI) and machine learning techniques. The aim is to determine if a patient is at risk of expressing rapid progression of PD symptoms from the baseline evaluation and as close to diagnosis as possible. By examining the records of 409 patients from the PPMI dataset, the features with the best predictive value at baseline patient evaluation are found to be sleep problems, daytime sleepiness and fatigue, motor symptoms at legs, cognition impairment, early axial and facial symptoms and in the most rapidly advanced cases speech issues, loss of smell and affected leg muscle reflexes.

PD_Manager: an mHealth platform for Parkinson's disease patient management.

PD_Manager is a mobile health platform designed to cover most of the aspects regarding the management of Parkinson's disease (PD) in a holistic approach. Patients are unobtrusively monitored using commercial wrist and insole sensors paired with a smartphone, to automatically estimate the severity of most of the PD motor symptoms. Besides motor symptoms monitoring, the patient's mobile application also provides various non-motor self-evaluation tests for assessing cognition, mood and nutrition to motivate them in becoming more active in managing their disease. All data from the mobile application and the sensors is transferred to a cloud infrastructure to allow easy access for clinicians and further processing. Clinicians can access this information using a separate mobile application that is specifically designed for their respective needs to provide faster and more accurate assessment of PD symptoms that facilitate patient evaluation. Machine learning techniques are used to estimate symptoms and disease progression trends to further enhance the provided information. The platform is also complemented with a decision support system (DSS) that notifies clinicians for the detection of new symptoms or the worsening of existing ones. As patient's symptoms are progressing, the DSS can also provide specific suggestions regarding appropriate medication changes.

Monitoring of motor and non-motor symptoms of Parkinson's disease through a mHealth platform.

Parkinson's disease (PD) is a complex, chronic disease that many patients live with for many years. In this work we propose a mHealth approach based on a set of unobtrusive, simple-in-use, off-the-self, co-operative, mobile devices that will be used for motor and non-motor symptoms monitoring and evaluation, as well as for the detection of fluctuations along with their duration through a waking day. Ideally, a multidisciplinary and integrated care approach involving several professionals working together (neurologists, physiotherapists, psychologists and nutritionists) could provide a holistic management of the disease increasing the patient's independence and Quality of Life (QoL). To address these needs we describe also an ecosystem for the management of both motor and non-motor symptoms on PD facilitating the collaboration of health professionals and empowering the patients to self-manage their condition. This would allow not only a better monitoring of PD patients but also a better understanding of the disease progression.

Modeling of blood flow through sutured micro-vascular anastomoses.

Microanastomosis is a surgical procedure used to reconnect two blood vessels using sutures. The optimal microanastomosis may be predicted by assessing the factors that influence this invasive procedure. Blood flow and hemodynamics following microanastomosis are important factors for the successful longevity of this operation. How is the blood flow affected by the presence of sutures? Computational Fluid Dynamics (CFD) is a powerful tool that permits the estimation of specific quantities, such as fluid stresses, that are hardly measurable in vivo. In this study, we propose a methodology which evaluates the alterations in the hemodynamic status due to microanastomosis. A CFD model of a reconstructed artery has been developed, based on anatomical information provided by intravascular ultrasound and angiography, and was used to simulate blood flow after microanastomosis. The 3D reconstructed arterial segments are modeled as non-compliant 1.24 - 1.47 mm diameter ducts, with approximately 0.1 mm arterial thickness. The blood flow is considered laminar and the no-slip condition is imposed on the boundary wall, which is assumed to be rigid. In analyzing the results, the distribution of the wall shear stress (WSS) is presented in the region of interest, near the sutures. The results indicate that high values of WSS appear in the vicinity of sutures. Such regions may promote thrombus formation and subsequently anastomotic failure, therefore their meticulous study is of high importance.