The automated box and blocks test an autonomous assessment method of gross manual dexterity in stroke rehabilitation

Traditional motor assessment is carried out by clinicians using standard clinical tests in order to have objectivity in the evaluation, but this manual procedure is liable to the observer subjectivity. In this article, an automatic assessment system based on the Box and Blocks Test (BBT) of manual dexterity is presented. Also, the automatic test administration and the motor performance of the user is addressed. Through cameras RGB-D the execution of the test and the patient's movements are monitored. Based on colour segmentation, the cubes displaced by the user are detected and the traditional scoring is automatically calculated. Furthermore, a pilot trial in a hospital environment was conducted, to compare the automatic system and its e ectiveness with respect to the traditional one. The results support the use of automatic assessment methods of motor functionality, which in combination with robotic rehabilitation systems, could address an autonomous and objective rehabilitation process.The research leading to these results has received funding from the ROBOHEALTH-A project (DPI2013-47944-C4-1-R) funded by Spanish Ministry of Economy and Competitiveness and from the RoboCity2030-III-CM project (S2013/MIT-2748), funded by Programas de Actividades I+D en la Comunidad de Madrid and cofunded by Structural Funds of the EU

Data Augmentation for Time-Series Classification: An Extensive Empirical Study and Comprehensive Survey

Data Augmentation (DA) has emerged as an indispensable strategy in Time Series Classification (TSC), primarily due to its capacity to amplify training samples, thereby bolstering model robustness, diversifying datasets, and curtailing overfitting. However, the current landscape of DA in TSC is plagued with fragmented literature reviews, nebulous methodological taxonomies, inadequate evaluative measures, and a dearth of accessible, user-oriented tools. In light of these challenges, this study embarks on an exhaustive dissection of DA methodologies within the TSC realm. Our initial approach involved an extensive literature review spanning a decade, revealing that contemporary surveys scarcely capture the breadth of advancements in DA for TSC, prompting us to meticulously analyze over 100 scholarly articles to distill more than 60 unique DA techniques. This rigorous analysis precipitated the formulation of a novel taxonomy, purpose-built for the intricacies of DA in TSC, categorizing techniques into five principal echelons: Transformation-Based, Pattern-Based, Generative, Decomposition-Based, and Automated Data Augmentation. Our taxonomy promises to serve as a robust navigational aid for scholars, offering clarity and direction in method selection. Addressing the conspicuous absence of holistic evaluations for prevalent DA techniques, we executed an all-encompassing empirical assessment, wherein upwards of 15 DA strategies were subjected to scrutiny across 8 UCR time-series datasets, employing ResNet and a multi-faceted evaluation paradigm encompassing Accuracy, Method Ranking, and Residual Analysis, yielding a benchmark accuracy of 88.94 +- 11.83%. Our investigation underscored the inconsistent efficacies of DA techniques, with..

Advances in Quantitative Characterizations of Electrophysiological Neural Activity

Disorders of the brain and nervous system result in more hospitalizations and lost productivity than any other disease group. Electroencephalography (EEG), which measures brain electrical signals from the scalp, is a common neuro-monitoring technique used for diagnostic, rehabilitative, and therapeutic purposes. Understanding EEG quantitatively and its neural correlates with patient characteristics could inform the safety and efficacy of technologies that rely on EEG. In this dissertation, a large clinical data set comprised of over 35,000 recordings as well as data from previous research experiments are utilized to better quantify characteristics of neurological activity. We first propose non-parametric methods of evaluating consistency of quantitative EEG features (qEEG) by applying novel statistical approaches. These results provide data-driven methods of identifying qEEG and their spatial characteristics ideal for various applications, and determining consistencies of novel features using existing data. These qEEG are commonly used in feature-based machine learning applications. Further, EEG-driven deep learning has shown promising results in distinguishing recordings of subjects. To better understand the performance of these two machine learning approaches, we assess their ability to distinguish between subjects taking different anticonvulsants. Our methods could successfully discriminate between patients taking either anticonvulsant and those taking no medications solely from neural activity with similar performance from both feature-based and deep learning approaches. With feature-based methods, it is easier to interpret which qEEG have the most impact on algorithm performance. However, deep learning applications in EEG can present difficulty in understanding and investigating underlying neurophysiological implications. We propose and validate a method to investigate frequency band importance in EEG-driven deep learning models. The easy perturbation EEG algorithm for spectral importance (easyPEASI) is simpler than previous methods and is applied to classifications investigated in this work. Until this point, our work used well segmented EEG from clinical settings. However, EEG is usually corrupted by noise which can degrade its utility. We formulate and validate novel approaches to score electrophysiological signal quality based on the presence of noise from various sources. Further, we apply our method to compare and evaluate the performance of existing artifact removal algorithms

Data-Driven Models, Techniques, and Design Principles for Combatting Healthcare Fraud

In the U.S., approximately $700 billion of the$2.7 trillion spent on healthcare is linked to fraud, waste, and abuse. This presents a significant challenge for healthcare payers as they navigate fraudulent activities from dishonest practitioners, sophisticated criminal networks, and even well-intentioned providers who inadvertently submit incorrect billing for legitimate services. This thesis adopts Hevner’s research methodology to guide the creation, assessment, and refinement of a healthcare fraud detection framework and recommended design principles for fraud detection. The thesis provides the following significant contributions to the field:1. A formal literature review of the field of fraud detection in Medicaid. Chapters 3 and 4 provide formal reviews of the available literature on healthcare fraud. Chapter 3 focuses on defining the types of fraud found in healthcare. Chapter 4 reviews fraud detection techniques in literature across healthcare and other industries. Chapter 5 focuses on literature covering fraud detection methodologies utilized explicitly in healthcare.2. A multidimensional data model and analysis techniques for fraud detection in healthcare. Chapter 5 applies Hevner et al. to help develop a framework for fraud detection in Medicaid that provides specific data models and techniques to identify the most prevalent fraud schemes. A multidimensional schema based on Medicaid data and a set of multidimensional models and techniques to detect fraud are presented. These artifacts are evaluated through functional testing against known fraud schemes. This chapter contributes a set of multidimensional data models and analysis techniques that can be used to detect the most prevalent known fraud types.3. A framework for deploying outlier-based fraud detection methods in healthcare. Chapter 6 proposes and evaluates methods for applying outlier detection to healthcare fraud based on literature review, comparative research, direct application on healthcare claims data, and known fraudulent cases. A method for outlier-based fraud detection is presented and evaluated using Medicaid dental claims, providers, and patients.4. Design principles for fraud detection in complex systems. Based on literature and applied research in Medicaid healthcare fraud detection, Chapter 7 offers generalized design principles for fraud detection in similar complex, multi-stakeholder systems.<br/

Development of a lesion localisation tool to improve outcome prediction in Traumatic Brain Injury patients

Tese de mestrado integrado, Engenharia Biomédica e Biofísica (Engenharia Clínica e Instrumentação Médica) Universidade de Lisboa, Faculdade de Ciências, 2022Traumatic brain injury (TBI) is a highly heterogeneous pathology that poses severe health and socioeconomic problems on a global scale. Neuroimaging research and development has advanced its clinical care in numerous ways, as injured brains are being imaged and studied in greater detail. The size and location of TBI lesions are often necessary to accurately determine a prognosis, which is key in defining a patient-specific rehabilitation program. This dissertation aims to investigate the impact of lesion characteristics, such as volume and location, on outcome prediction in TBI patients. Lesion localisation was achieved by comparing annotated TBI lesions to a brain atlas. Furthermore, other lesion characteristics were examined across different Magnetic Resonance Imaging (MRI) sequences and scanners, with results suggesting that the use of different scanners or MRI contrasts introduced biases in said lesion characteristics. Patient outcome was predicted using four generalised linear models. Besides clinical variables, these models included lesion volume, group and location as predictors. Model comparison indicated that lesion volume could be beneficial for outcome prediction, but may be dependent on both lesion group and location. Overall, this methodology showed potential in uncovering the effect that certain lesion groups and/or locations have on patient outcome after TBI

Kinematic changes following robotic-assisted upper extremity rehabilitation in children with hemiplegia : dosage effects on movement time

Indiana University-Purdue University Indianapolis (IUPUI)Background: Rehabilitation Robotics (RR) has become a more widely used and better understood treatment intervention and research tool in the last 15 years. Traditional research involves pre and post-test outcomes, making it difficult to analyze changes in behavior during the treatment process. Harnessing kinematics captured throughout each treatment allows motor learning to be quantified and questions of application and dosing to be answered. Objective: The aims of this secondary analysis were: (i) to investigate the impact of treatment presentation during RR on upper extremity movement time (mt) in children with hemiplegic cerebral palsy (CP) and (ii) to investigate the impact of training structure (dose and intensity) on mt in children with CP participating in RR. Methods: Subjects completed 16 intervention sessions of RR (2 x week; 8 weeks) with a total of 1,024 repetitions of movement per session and three assessments: pre, post and 6 month f/u. During each assessment and intervention, subjects completed “one-way record” assessments tracking performance on a planar task without robotic assistance. Kinematics from these records were extracted to assess subject performance over the course of and within sessions. Results: For all participants, a significant decrease in mt was found at post-test and follow-up. No significant differences were found in mt for age, severity or group placement. A significant interaction was found between treatment day, block and group (p = .033). Significant mt differences were found between the three blocks of intervention within individual days (p = .001). Specifically, significant differences were found over the last block of treatment (p = .032) and between successive treatment days (p = .001). Conclusion: The results indicate that for children with CP participating in RR, the number of repetitions per session is important. We hypothesized that children’s performance would plateau during a treatment day as attention waned, the opposite proved to be true. Despite the high-number of repetitions and associated cognitive demand, subjects’ performance actually trended upwards throughout the 1,024 repetitions suggesting that children were able to tolerate and learn from a high volume of repetitions

Clinical and imaging biomarkers of audiovestibular function in infratentorial superficial siderosis

Disabling hearing loss is known to affect over 400 million people worldwide while the lifetime prevalence of dizziness can be as high as 40%. Rare causes for hearing and balance impairment are often understudied. Infratentorial (classical) superficial siderosis (iSS) is a rare but sometimes disabling complex neurological condition most often associated with hearing and balance impairment, and myelopathy. Olfactory loss has been reported but not yet systematically studied. iSS results from a chronic low-grade and low volume bleeding into the cerebrospinal fluid and the deposition of iron-degradation products (predominantly haemosiderin) in the subpial layers of the central nervous system, with predilection for the cerebellum and the vestibulocochlear nerves. Magnetic resonance imaging (MRI) allows haemosiderin to be visualised in-vivo and is the mainstream diagnostic modality. Due to the assumed rarity of iSS (prevalence of 0.03-0.14%), our research opportunities are limited. Few dedicated studies describe iSS-related audiovestibular (AV) findings, often limited to case-series, with mixed findings. There is currently no robust evidence that the radiological haemosiderin appearances correlate with the objective clinical tests. This project focuses on phenotyping the AV function in iSS and identifies predominantly retrocochlear hearing loss with features suggestive of central auditory dysfunction, and mixed vestibular (predominantly cerebellar) dysfunction. This work introduces and validates an imaging rating scale aiming to capture the anatomical extent of haemosiderin deposits visualised on MRI in a standardised and reproducible way. The scale demonstrates excellent reliability and good validity, with the scores correlating with hearing thresholds. This project estimates the prevalence of MRI-defined iSS in a large UK Biobank sample, similar to other rare neurootological disorders. Using patient/self-report measures, this work captures markedly low health-states of individuals with iSS and identifies possible iSS-specific auditory characteristics. Finally, the work identifies high prevalence of olfactory dysfunction in individuals with iSS

The will-to-incapacitate: An experiment in actuarial justice in the period between 1970 and 1987 in the United States.

This thesis interrogates incapacitation as it developed in the 1970s and 1980s in the United States to conduct a genealogy of the conditions of emergence of actuarial justice (Foucault, 1981; Feeley and Simon, 1992; 1994) as it is enacted within this particular knowledge-power formation. Incapacitation is a penal rationale that concentrates on anticipating future crimes, and preventing offenders from committing crimes, effectively prioritizing public safety above all other considerations. My mapping of incapacitation demonstrates that it is recursively performed along two mutually conditioning poles that are illustrative of Foucault’s account of biopolitics and security (1978, 2003, 2007). These poles are: technocratic penal managerialism, which regulates the actions of diverse agents and authorities as they participate in a program of reducing recidivism within a mobile population of offenders; and, danger management of this distributed population of offenders, driven by a desire to anticipate and selectively incapacitate the most dangerous offenders. This analysis supports the mapping of actuarial justice provided by Feeley and Simon; however, my typology uses Galloway’s (2004) concept of protocol, to extend and refine their diagram about actuarial power. Given the high levels of scientific uncertainty about the efficacy of selective incapacitation as a penal policy, and the poor predictive powers of actuarial instruments in accurately classifying high-rate offenders in the early 1980s, my analysis demonstrates how protocollary power established the rules for modulating the participation of autonomous and diverse agents that are enlisted within the distributed networks of actuarial justice to propel its movement forward, this being the birth of evidence-based penal policy and practice. This protocol projects an ontological view of recidivism derived from criminal career research that filters and experiments with probabilistic actuarial codes or profiles of risk. These biopolitical codes regulate future research into advancing knowledge, predicting and controlling levels of dangerousness, and auditing of governmental performance in reducing recidivism, all of which are contingent upon the anticipatory longitudinal tracking of an aleatory population of offenders within the penal environment. Protocol is a biopolitical form of management that is central in the logistical control of this penal network and its nodes of operation and decision-making, constantly mining data for new possibilities. At the same time, I demonstrate that this will-to knowledge uses its technocratic expertise to distort, exaggerate, or conceal difference in its struggle for authority given high levels of uncertainty about recidivism and how to control it

Virtual environments for stroke rehabilitation: examining a novel technology against end-user, clinical and management demands with reference to UK care provision

In the field of post-stroke rehabilitation, there appears to be growing interest in the use of virtual reality (VR)-based systems as adjunct technologies to standard therapeutic practices. The limitations and the potentials of this technology are not, however, generally well understood. The present study thus seeks to determine the value of the technology with reference to end-user requirements by surveying and evaluating its application against a variety of parameters: user focus, clinical effectiveness, marketability and contextual meaningfulness, etc. A key theme in the research considers how a technology developed internationally might interface with care provision demands and cultures specific to the United Kingdom. The barriers to innovation entry in this context are thus examined. Further practical study has been conducted in the field with a small sample of post-stroke rehabilitation patients. The data garnered from these enquiries have informed a detailed system analysis, a strategy for innovation and a broad theoretical discussion as to the effectiveness of the technology in delivering VR environments by which the patient can undertake ‘meaningful’ therapeutic activities. The data reveal that there does appear to be clinical value in using this technology, yet establishing its maximal value necessitates greater integrity among clinicians and engineers, and the furthering of progressive channels for innovation by public health administrators

Wearable fusion system for assessment of motor function in lesion-symptom mapping studies

Lesion-symptom mapping studies are a critical component of addressing the relationship between brain and behaviour. Recent developments have yielded significant improvements in the imaging and detection of lesion profiles, but the quantification of motor outcomes is still largely performed by subjective and low-resolution standard clinical rating scales. This mismatch means than lesion-symptom mapping studies are limited in scope by scores which lack the necessary accuracy to fully quantify the subcomponents of motor function. The first study conducted aimed to develop a new automated system of motor function which addressed the limitations inherent in the clinical rating scales. A wearable fusion system was designed that included the attachment of inertial sensors to record the kinematics of upper extremity. This was combined with the novel application of mechanomyographic sensors in this field, to enable the quantification of hand/wrist function. Novel outputs were developed for this system which aimed to combine the validity of the clinical rating scales with the high accuracy of measurements possible with a wearable sensor system. This was achieved by the development of a sophisticated classification model which was trained on series of kinematic and myographic measures to classify the clinical rating scale. These classified scores were combined with a series of fine-grained clinical features derived from higher-order sensor metrics. The developed automated system graded the upper-extremity tasks of the Fugl-Meyer Assessment with a mean accuracy of 75\% for gross motor tasks and 66\% for the wrist/hand tasks. This accuracy increased to 85\% and 74\% when distinguishing between healthy and impaired function for each of these tasks. Several clinical features were computed to describe the subcomponents of upper extremity motor function. This fine-grained clinical feature set offers a novel means to complement the low resolution but well-validated standardised clinical rating scales. A second study was performed to utilise the fine-grained clinical feature set calculated in the previous study in a large-scale region-of-interest lesion-symptom mapping study. Statistically significant regions of motor dysfunction were found in the corticospinal tract and the internal capsule, which are consistent with other motor-based lesion-symptom mapping studies. In addition, the cortico-ponto-cerebellar tract was found to be statistically significant when testing with a clinical feature of hand/wrist motor function. This is a novel finding, potentially due to prior studies being limited to quantifying this subcomponent of motor function using standard clinical rating scales. These results indicate the validity and potential of the clinical feature set to provide a more detailed picture of motor dysfunction in lesion-symptom mapping studies.Open Acces