A wearable biofeedback device to improve motor symptoms in Parkinson’s disease

Dissertação de mestrado em Engenharia BiomédicaThis dissertation presents the work done during the fifth year of the course Integrated Master’s in Biomedical Engineering, in Medical Electronics. This work was carried out in the Biomedical & Bioinspired Robotic Devices Lab (BiRD Lab) at the MicroElectroMechanics Center (CMEMS) established at the University of Minho. For validation purposes and data acquisition, it was developed a collaboration with the Clinical Academic Center (2CA), located at Braga Hospital. The knowledge acquired in the development of this master thesis is linked to the motor rehabilitation and assistance of abnormal gait caused by a neurological disease. Indeed, this dissertation has two main goals: (1) validate a wearable biofeedback system (WBS) used for Parkinson's disease patients (PD); and (2) develop a digital biomarker of PD based on kinematic-driven data acquired with the WBS. The first goal aims to study the effects of vibrotactile biofeedback to play an augmentative role to help PD patients mitigate gait-associated impairments, while the second goal seeks to bring a step advance in the use of front-end algorithms to develop a biomarker of PD based on inertial data acquired with wearable devices. Indeed, a WBS is intended to provide motor rehabilitation & assistance, but also to be used as a clinical decision support tool for the classification of the motor disability level. This system provides vibrotactile feedback to PD patients, so that they can integrate it into their normal physiological gait system, allowing them to overcome their gait difficulties related to the level/degree of the disease. The system is based on a user- centered design, considering the end-user driven, multitasking and less cognitive effort concepts. This manuscript presents all steps taken along this dissertation regarding: the literature review and respective critical analysis; implemented tech-based procedures; validation outcomes complemented with results discussion; and main conclusions and future challenges.Esta dissertação apresenta o trabalho realizado durante o quinto ano do curso Mestrado Integrado em Engenharia Biomédica, em Eletrónica Médica. Este trabalho foi realizado no Biomedical & Bioinspired Robotic Devices Lab (BiRD Lab) no MicroElectroMechanics Center (CMEMS) estabelecido na Universidade do Minho. Para efeitos de validação e aquisição de dados, foi desenvolvida uma colaboração com Clinical Academic Center (2CA), localizado no Hospital de Braga. Os conhecimentos adquiridos no desenvolvimento desta tese de mestrado estão ligados à reabilitação motora e assistência de marcha anormal causada por uma doença neurológica. De facto, esta dissertação tem dois objetivos principais: (1) validar um sistema de biofeedback vestível (WBS) utilizado por doentes com doença de Parkinson (DP); e (2) desenvolver um biomarcador digital de PD baseado em dados cinemáticos adquiridos com o WBS. O primeiro objetivo visa o estudo dos efeitos do biofeedback vibrotáctil para desempenhar um papel de reforço para ajudar os pacientes com PD a mitigar as deficiências associadas à marcha, enquanto o segundo objetivo procura trazer um avanço na utilização de algoritmos front-end para biomarcar PD baseado em dados inerciais adquiridos com o dispositivos vestível. De facto, a partir de um WBS pretende-se fornecer reabilitação motora e assistência, mas também utilizá-lo como ferramenta de apoio à decisão clínica para a classificação do nível de deficiência motora. Este sistema fornece feedback vibrotáctil aos pacientes com PD, para que possam integrá-lo no seu sistema de marcha fisiológica normal, permitindo-lhes ultrapassar as suas dificuldades de marcha relacionadas com o nível/grau da doença. O sistema baseia-se numa conceção centrada no utilizador, considerando o utilizador final, multitarefas e conceitos de esforço menos cognitivo. Portanto, este manuscrito apresenta todos os passos dados ao longo desta dissertação relativamente a: revisão da literatura e respetiva análise crítica; procedimentos de base tecnológica implementados; resultados de validação complementados com discussão de resultados; e principais conclusões e desafios futuros

Novel Semi-Supervised Learning Models to Balance Data Inclusivity and Usability in Healthcare Applications

abstract: Semi-supervised learning (SSL) is sub-field of statistical machine learning that is useful for problems that involve having only a few labeled instances with predictor (X) and target (Y) information, and abundance of unlabeled instances that only have predictor (X) information. SSL harnesses the target information available in the limited labeled data, as well as the information in the abundant unlabeled data to build strong predictive models. However, not all the included information is useful. For example, some features may correspond to noise and including them will hurt the predictive model performance. Additionally, some instances may not be as relevant to model building and their inclusion will increase training time and potentially hurt the model performance. The objective of this research is to develop novel SSL models to balance data inclusivity and usability. My dissertation research focuses on applications of SSL in healthcare, driven by problems in brain cancer radiomics, migraine imaging, and Parkinson’s Disease telemonitoring. The first topic introduces an integration of machine learning (ML) and a mechanistic model (PI) to develop an SSL model applied to predicting cell density of glioblastoma brain cancer using multi-parametric medical images. The proposed ML-PI hybrid model integrates imaging information from unbiopsied regions of the brain as well as underlying biological knowledge from the mechanistic model to predict spatial tumor density in the brain. The second topic develops a multi-modality imaging-based diagnostic decision support system (MMI-DDS). MMI-DDS consists of modality-wise principal components analysis to incorporate imaging features at different aggregation levels (e.g., voxel-wise, connectivity-based, etc.), a constrained particle swarm optimization (cPSO) feature selection algorithm, and a clinical utility engine that utilizes inverse operators on chosen principal components for white-box classification models. The final topic develops a new SSL regression model with integrated feature and instance selection called s2SSL (with “s2” referring to selection in two different ways: feature and instance). s2SSL integrates cPSO feature selection and graph-based instance selection to simultaneously choose the optimal features and instances and build accurate models for continuous prediction. s2SSL was applied to smartphone-based telemonitoring of Parkinson’s Disease patients.Dissertation/ThesisDoctoral Dissertation Industrial Engineering 201

Objective assessment of upper limb motor symptoms in Parkinson's Disease using body-worn sensors

MD ThesisBackground There is a need for an objective method of symptom assessment in Parkinson's disease (PD) to enable better treatment decisions and to aid evaluation of new treatments. Current assessment methods; patient-completed symptom diaries and clinical rating scales, have limitations. Accelerometers (sensors capable of capturing data on human movement) and analysis using artificial neural networks (ANNs) have shown potential as a method of motor symptom evaluation in PD. It is unknown whether symptom monitoring with body-worn sensors is acceptable to PD patients due to a lack of previous research. Methods 34 participants with PD wore bilateral wrist-worn accelerometers for 4 hours in a research facility (phase 1) and then for 7 days in their homes (phase 2) whilst also completing symptom diaries. An ANN designed to predict a patient’s motor status, was developed and trained based on accelerometer data during phase 2. ANN performance was evaluated (leave-one-out approach) against patient-completed symptom diaries during phase 2, and against clinician rating of disease state during phase 1 observations. Participants’ views regarding the sensors were obtained via a Likert-style questionnaire completed after each phase. Differences in responses between phases were assessed for using the Wilcoxon rank-sum test. Results ANN-derived values of the proportion of time in each disease state (phase 2), showed strong, significant correlations with values derived from patient-completed symptom diaries. ANN disease state recognition during phase 1 was sub-optimal. High concordance with sensors was seen. Prolonged wearing of the sensors did not adversely affect participants’ opinions on the wearability of the sensors, when compared to their responses following phase 1 Conclusions Accelerometers and ANNs produced results comparable to those of symptom diaries. Our findings suggest that long-term monitoring with wrist-worn sensors is acceptable to PD patients

Investigating reach and grasp in Parkinson's disease cognitive impairment

Reach and grasp are evolutionary conserved motor actions controlled by highly specialised neural pathways that have major nodes in the posterior parietal and premotor frontal cortices. Mild cognitive impairment is an important non-motor symptom of Parkinson’s disease (PD) and there is evidence that the risk of transition between PD mild cognitive impairment (PD-MCI) and Parkinson’s disease dementia (PDD) is dependent on which neurotransmitter systems within the brain are most dysfunctional. Studies of reach and grasp in PD subjects with normal cognition (PD-NC) suggest a greater dependence on visual feedback to guide reach and grasp compared with controls.The primary aim of this thesis is to explore how cognitive impairment influences reach and grasp in PD. Twenty two PD-NC, 23 PD-MCI, ten PDD and 19 controls reached and grasped for a target whilst wearing movement sensing equipment in four conditions: full vision, a darkened room with an illuminated target, with eyes closed at a natural speed and as quickly as possible in full vision. All PD subjects were tested whilst on. Kinematic parameters of reach and grasp were extracted from the movement data and analysed using standard statistical methods.Our results show a spectrum of change to kinematic reach parameters when reaching and grasping with eyes closed: PD-NC are disproportionately affected compared to controls and PDD are disproportionately affected compared to PD-NC. Parameters of reach and grasp were similar between PD-NC and PD-MCI in all conditions. These results have been discussed in the context of abnormal integration of sensorimotor functions and impaired spatial working memory in PD. Reaction time when reaching and grasping as quickly as possible is significantly associated with global cognition in the PD subjects after controlling for age, motor signs and disease duration. This supports a role for reaction time as a potential biomarker for cognitive impairment in PD

Fused mechanomyography and inertial measurement for human-robot interface

Human-Machine Interfaces (HMI) are the technology through which we interact with the ever-increasing quantity of smart devices surrounding us. The fundamental goal of an HMI is to facilitate robot control through uniting a human operator as the supervisor with a machine as the task executor. Sensors, actuators, and onboard intelligence have not reached the point where robotic manipulators may function with complete autonomy and therefore some form of HMI is still necessary in unstructured environments. These may include environments where direct human action is undesirable or infeasible, and situations where a robot must assist and/or interface with people. Contemporary literature has introduced concepts such as body-worn mechanical devices, instrumented gloves, inertial or electromagnetic motion tracking sensors on the arms, head, or legs, electroencephalographic (EEG) brain activity sensors, electromyographic (EMG) muscular activity sensors and camera-based (vision) interfaces to recognize hand gestures and/or track arm motions for assessment of operator intent and generation of robotic control signals. While these developments offer a wealth of future potential their utility has been largely restricted to laboratory demonstrations in controlled environments due to issues such as lack of portability and robustness and an inability to extract operator intent for both arm and hand motion. Wearable physiological sensors hold particular promise for capture of human intent/command. EMG-based gesture recognition systems in particular have received significant attention in recent literature. As wearable pervasive devices, they offer benefits over camera or physical input systems in that they neither inhibit the user physically nor constrain the user to a location where the sensors are deployed. Despite these benefits, EMG alone has yet to demonstrate the capacity to recognize both gross movement (e.g. arm motion) and finer grasping (e.g. hand movement). As such, many researchers have proposed fusing muscle activity (EMG) and motion tracking e.g. (inertial measurement) to combine arm motion and grasp intent as HMI input for manipulator control. However, such work has arguably reached a plateau since EMG suffers from interference from environmental factors which cause signal degradation over time, demands an electrical connection with the skin, and has not demonstrated the capacity to function out of controlled environments for long periods of time. This thesis proposes a new form of gesture-based interface utilising a novel combination of inertial measurement units (IMUs) and mechanomyography sensors (MMGs). The modular system permits numerous configurations of IMU to derive body kinematics in real-time and uses this to convert arm movements into control signals. Additionally, bands containing six mechanomyography sensors were used to observe muscular contractions in the forearm which are generated using specific hand motions. This combination of continuous and discrete control signals allows a large variety of smart devices to be controlled. Several methods of pattern recognition were implemented to provide accurate decoding of the mechanomyographic information, including Linear Discriminant Analysis and Support Vector Machines. Based on these techniques, accuracies of 94.5% and 94.6% respectively were achieved for 12 gesture classification. In real-time tests, accuracies of 95.6% were achieved in 5 gesture classification. It has previously been noted that MMG sensors are susceptible to motion induced interference. The thesis also established that arm pose also changes the measured signal. This thesis introduces a new method of fusing of IMU and MMG to provide a classification that is robust to both of these sources of interference. Additionally, an improvement in orientation estimation, and a new orientation estimation algorithm are proposed. These improvements to the robustness of the system provide the first solution that is able to reliably track both motion and muscle activity for extended periods of time for HMI outside a clinical environment. Application in robot teleoperation in both real-world and virtual environments were explored. With multiple degrees of freedom, robot teleoperation provides an ideal test platform for HMI devices, since it requires a combination of continuous and discrete control signals. The field of prosthetics also represents a unique challenge for HMI applications. In an ideal situation, the sensor suite should be capable of detecting the muscular activity in the residual limb which is naturally indicative of intent to perform a specific hand pose and trigger this post in the prosthetic device. Dynamic environmental conditions within a socket such as skin impedance have delayed the translation of gesture control systems into prosthetic devices, however mechanomyography sensors are unaffected by such issues. There is huge potential for a system like this to be utilised as a controller as ubiquitous computing systems become more prevalent, and as the desire for a simple, universal interface increases. Such systems have the potential to impact significantly on the quality of life of prosthetic users and others.Open Acces

The role of the gut and the gastrointestinal microbiome in Parkinson’s disease

INTRODUCTION: Parkinson’s disease (PD) is a disabling and progressive neurodegenerative disorder that is increasing in prevalence with the aging and urbanisation of the global population. The mechanisms underlying PD pathogenesis and progression are incompletely understood. Improved clinical recognition of early and prodromal non-motor symptoms (NMS), namely gastrointestinal (GI) dysfunction, has focused research over the last two decades on the roles of the gut. More recently, the influences of the microbiota-gut-brain-axis (MGBA) in the development and progression of PD have become an intensive area of research. Studies have demonstrated an association between the GM and a variety of PD-related characteristics, identifying important impacts on levodopa metabolism by certain microbiota. Importantly, the effect of device-assisted therapies (DATs) on the GM and the robustness of microbiota compositional differences between PD patients and household controls (HCs) has not been well defined. The aims of this thesis were to 1) investigate GI dysfunction and nutritional patterns in PD, 2) determine if the GM is a biomarker of PD, and 3) investigate the temporal stability of the GM in PD patients receiving standard therapies and those initiating DATs. METHODS: 103 PD patients and 81 HCs were recruited and participants with PD were considered in two sub-cohorts; 1) PD patients initiating DAT; either Deep Brain Stimulation (DBS) (n=10), or levodopa-carbidopa intestinal gel (LCIG) (n=11), who had GM sampling from stool at -2, 0, 2 and 4 weeks around initiation of DAT and baseline, 6 and 12 months following DAT initiation, 2) 82 PD patients receiving standard PD therapies, who had GM sampling from stool at baseline, 6 and 12 months. Validated PD questionnaire metadata ascertaining motor characteristics and NMS, as well as nutritional data in the form of a Food Frequency Questionnaire, were collected for all participants at baseline, 6 and 12 months. Total DNA was isolated from stool before sequencing the V3-V4 region of 16S rRNA. Relative bacterial abundances, diversity measures, compositional differences and clinical-microbiome associations were determined, as well as developing predictive modelling to identify PD patients and assess disease progression. RESULTS: PD patients reported more prevalent and severe GI dysfunction, especially constipation, which was almost three-times more common compared to HC subjects, (78.6% vs 28.4%, p<0.001). PD patients had a higher intake of total carbohydrates (279 g/day vs 232 g/day; p=0.034), which was largely attributable to an increased daily sugar intake (153 g/day vs 119 g/day; p=0.003), particularly of free sugars (61 g/day vs 41 g/day; p=0.001). Significant GM compositional differences across several taxonomic levels were apparent between PD patients and HCs and associated with a number of PD motor and NMS features, as well as certain therapies. Predictive models to distinguish PD from HCs were developed considering global GM profiles, achieving an area under the curve (AUC) of 0.71, which was improved by addition of data on carbohydrate intake (AUC 0.74). Longitudinal analysis demonstrated persistent underrepresentation of known short-chain fatty acid producing bacteria in PD patients, particularly those concerned with butyrate production; Butyricicoccus, Fusicatenibacter, Lachnospiraceae ND3007 group and Erysipelotrichaceae UCG−003. Taxa differences observed over the short-term (four week) sampling period around DAT (DBS and LCIG) initiation, were not sustained at 6 and 12 months. Despite this, persistent longer-term overrepresentation of Prevotella was observed after DBS initiation, and a trend was found that was suggestive of overrepresentation of Roseburia after LCIG initiation. These results suggest that there may be variable shorter and longer-term DBS and LCIG influences on the GM, which are complex and multifactorial. PD progression analysis did not identify distinct persisting GM compositional differences between faster and slower progressing patients, although predictive modelling was strengthened by the consideration of nutritional data, specifically protein intake, and improved the predictive capacity for PD progression. CONCLUSION: This thesis demonstrates that there are numerous clinically significant associations between the gut, GM and PD. GI dysfunction is common, and carbohydrate nutritional intake appears to be different from the general population in PD. Persistent alterations of GM composition in PD compared to HCs were found. These findings provide support for the existence of disturbances of gut homeostatic pathways, which may disrupt intestinal barrier permeability and lead to gut leakiness, in the pathogenesis of PD. This thesis also highlights the potential to use the GM in the identification of PD and the characterisation of disease progression

A Longtitudinal Analysis of Cognitive and Eye Movement Deficits in Alzheimer's Disease.

The main purpose of this thesis was to investigate longitudinally, cognitive and eye movement deficits in Alzheimer's disease. A key aspect of the work was to examine the potential utility of saccadic eye movements in the diagnosis of Alzheimer's disease. Study I investigated saccadic error rates and error correction in Alzheimer's disease, other dementias and healthy elderly control participants using reflexive and voluntary saccade paradigms, to identify salient findings for further analysis. Study II explored the fixation offset effect in Alzheimer's disease, other dementias and healthy elderly control participants, to study the attention (fixation) disengagement deficit previously reported in Alzheimer's disease. Study III examined the effects of normal aging and disease, comparing Alzheimer's disease patients and other dementia types with healthy young adult control participants, healthy elderly control participants and Parkinson's disease patients. Study IV assessed the potential effects of acetylcholinesterase inhibitors on baseline data to eliminate medication effects. Study V investigated repeated measures data for salient observations from Studies I and II in Alzheimer's disease patients and healthy elderly control participants over an 18 month period. Study VI evaluated salient saccadic eye movement and neuropsychological assessment variables, with a view to generating regression models that could predict dementia. Alzheimer's disease patients were found to commit inhibition errors that increased in proportion according to the demands of the voluntary saccade task. Error-correction analysis, revealed that a high proportion of errors remain uncorrected in the antisaccade task, a finding apparently specific to dementia. The results were found to be consistent with the notion that the voluntary saccade tasks require selective attention, the facilitation of which is dependent on task goals being sufficiently activated in working memory. The magnitude of fixation offset effect was greater for Alzheimer's disease patients than controls and Parkinson's disease patients at baseline, but the longitudinal analysis showed that this magnitude decreased over subsequent test sessions. The large initial magnitude of fixation offset effect is believed to have been caused by over compensation of volitional compensation strategies at baseline, when the Alzheimer's disease patients had mild dementia. Regression models using antisaccade variables and neuropsychological assessment scores as predictors both performed well. It is feasible that models could be developed that would enable a reduced set of neuropsychological assessments to be used and three predictors from one antisaccade task. The results confirm that the antisaccade task is a useful model paradigm for the study of oculomotor dysfunction in dementia

Proceedings XXIII Congresso SIAMOC 2023

Il congresso annuale della Società Italiana di Analisi del Movimento in Clinica (SIAMOC), giunto quest’anno alla sua ventitreesima edizione, approda nuovamente a Roma. Il congresso SIAMOC, come ogni anno, è l’occasione per tutti i professionisti che operano nell’ambito dell’analisi del movimento di incontrarsi, presentare i risultati delle proprie ricerche e rimanere aggiornati sulle più recenti innovazioni riguardanti le procedure e le tecnologie per l’analisi del movimento nella pratica clinica. Il congresso SIAMOC 2023 di Roma si propone l’obiettivo di fornire ulteriore impulso ad una già eccellente attività di ricerca italiana nel settore dell’analisi del movimento e di conferirle ulteriore respiro ed impatto internazionale. Oltre ai qualificanti temi tradizionali che riguardano la ricerca di base e applicata in ambito clinico e sportivo, il congresso SIAMOC 2023 intende approfondire ulteriori tematiche di particolare interesse scientifico e di impatto sulla società. Tra questi temi anche quello dell’inserimento lavorativo di persone affette da disabilità anche grazie alla diffusione esponenziale in ambito clinico-occupazionale delle tecnologie robotiche collaborative e quello della protesica innovativa a supporto delle persone con amputazione. Verrà infine affrontato il tema dei nuovi algoritmi di intelligenza artificiale per l’ottimizzazione della classificazione in tempo reale dei pattern motori nei vari campi di applicazione