Real-Time Feedback Training to Improve Gait and Posture in Parkinson's Disease

abstract: Progressive gait disorder in Parkinson's disease (PD) is usually exhibited as reduced step/stride length and gait speed. People with PD also exhibit stooped posture, which can contribute to reduced step length and arm swing. Since gait and posture deficits in people with PD do not respond well to pharmaceutical and surgical treatments, novel rehabilitative therapies to alleviate these impairments are necessary. Many studies have confirmed that people with PD can improve their walking patterns when external cues are presented. Only a few studies have provided explicit real-time feedback on performance, but they did not report how well people with PD can follow the cues on a step-by-step basis. In a single-session study using a novel-treadmill based paradigm, our group had previously demonstrated that people with PD could follow step-length and back angle feedback and improve their gait and posture during treadmill walking. This study investigated whether a long-term (6-week, 3 sessions/week) real-time feedback training (RTFT) program can improve overground gait, upright posture, balance, and quality of life. Three subjects (mean age 70 ± 2 years) with mild to moderate PD (Hoehn and Yahr stage III or below) were enrolled and participated in the program. The RTFT sessions involved walking on a treadmill while following visual feedback of step length and posture (one at any given time) displayed on a monitor placed in front of the subject at eye-level. The target step length was set between 110-120% of the step length obtained during a baseline non-feedback walking trial and the target back angle was set at the maximum upright posture exhibited during a quiet standing task. Two subjects were found to significantly improve their posture and overground walking at post-training and these changes were retained six weeks after RTFT (follow-up) and the third subject improved his upright posture and gait rhythmicity. Furthermore, the magnitude of the improvements observed in these subjects was greater than the improvements observed in reports on other neuromotor interventions. These results provide preliminary evidence that real-time feedback training can be used as an effective rehabilitative strategy to improve gait and upright posture in people with PD.Dissertation/ThesisMasters Thesis Biomedical Engineering 201

Sensor Approach for Brain Pathophysiology of Freezing of Gait in Parkinson\u27s Disease Patients

Parkinson\u27s Disease (PD) affects over 1% of the population over 60 years of age and is expected to reach 1 million in the USA by the year 2020, growing by 60 thousand each year. It is well understood that PD is characterized by dopaminergic loss, leading to decreased executive function causing motor symptoms such as tremors, bradykinesia, dyskinesia, and freezing of gait (FoG) as well as non-motor symptoms such as loss of smell, depression, and sleep abnormalities. A PD diagnosis is difficult to make since there is no worldwide approved test and difficult to manage since its manifestations are widely heterogeneous among subjects. Thus, understanding the patient subsets and the neural biomarkers that set them apart will lead to improved personalized care. To explore the physiological alternations caused by PD on neurological pathways and their effect on motor control, it is necessary to detect the neural activity and its dissociation with healthy physiological function. To this effect, this study presents a custom ultra-wearable sensor solution, consisting of electroencephalograph, electromyograph, ground reaction force, and symptom measurement sensors for the exploration of neural biomarkers during active gait paradigms. Additionally, this study employed novel de-noising techniques for dealing with the motion artifacts associated with active gait EEG recordings and compared time-frequency features between a group of PD with FoG and a group of age-matched controls and found significant differences between several EEG frequency bands during start and end of normal walking (with a p\u3c0.05)

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

Rhythmic Haptic Cueing Using Wearable Devices as Physiotherapy for Huntington Disease: Case Study

Background: Huntington disease (HD) is an inherited genetic disorder that results in the death of brain cells. HD symptoms generally start with subtle changes in mood and mental abilities; they then degenerate progressively, ensuing a general lack of coordination and an unsteady gait, ultimately resulting in death. There is currently no cure for HD. Walking cued by an external, usually auditory, rhythm has been shown to steady gait and help with movement coordination in other neurological conditions. More recently, work with other neurological conditions has demonstrated that haptic (ie, tactile) rhythmic cues, as opposed to audio cues, offer similar improvements when walking. An added benefit is that less intrusive, more private cues are delivered by a wearable device that leaves the ears free for conversation, situation awareness, and safety. This paper presents a case study where rhythmic haptic cueing (RHC) was applied to one person with HD. The case study has two elements: the gait data we collected from our wearable devices and the comments we received from a group of highly trained expert physiotherapists and specialists in HD. Objective: The objective of this case study was to investigate whether RHC can be applied to improve gait coordination and limb control in people living with HD. While not offering a cure, therapeutic outcomes may delay the onset or severity of symptoms, with the potential to improve and prolong quality of life. Methods: The approach adopted for this study includes two elements, one quantitative and one qualitative. The first is a repeated-measures design with three conditions: before haptic rhythm (ie, baseline), with haptic rhythm, and after exposure to haptic rhythm. The second element is an in-depth interview with physiotherapists observing the session. Results: In comparison to the baseline, the physiotherapists noted a number of improvements to the participant’s kinematics during her walk with the haptic cues. These improvements continued in the after-cue condition, indicating some lasting effects. The quantitative data obtained support the physiotherapists’ observations. Conclusions: The findings from this small case study, with a single participant, suggest that a haptic metronomic rhythm may have immediate, potentially therapeutic benefits for the walking kinematics of people living with HD and warrants further investigation

Rhythmic Haptic Cueing for Gait Rehabilitation of People With Hemiparesis: Quantitative Gait Study

Background: Rhythm, brain and body are closely linked. Humans can synchronise their movement to auditory rhythms in ways that can improve regularity of movement while reducing perceived effort. However, the ability to perform rhythmic movement may be disrupted by various neurological conditions. Many such conditions impair mechanisms that control movement, such as gait – but typically without rhythmic perception being affected. This paper focuses on hemiparetic stroke – a neurological condition which affects one side of the body. Hemiparetic stroke can cause severe asymmetries in gait, leading to numerous physical problems – ranging from muscle degeneration to bone fractures. Movement synchronisation via entrainment to auditory metronomes is known to improve asymmetry and related gait problems; this paper presents the first systematic study of entrainment for gait rehabilitation via the haptic modality.Objective: This paper explores the gait rehabilitation of people with hemiparesis following a stroke or brain injury, by a process of haptic entrainment to rhythmic cues. Various objective measures such as stride length and stride time are considered. Methods: The study presented here is a quantitative gait study combining temporal and spatial data on haptically cued participants with hemiparetic stroke and brain injury. Gait characteristics were measured before, during, and after cueing.Results: All eleven successfully screened participants were able to synchronise their steps to a haptically presented rhythm. Specifically, six participants demonstrated immediate improvements regarding their temporal gait characteristics, and three out of six improved their gait in terms of spatial characteristics.Conclusions: Considering the great variability between stroke and brain injury survivors, and the limited number of available participants in our study, there is no claim here of statistical evidence able to support a formal experimental result of improved gait. However, viewing this empirical gait investigation as a set of eleven case studies, more modest empirical claims can be made. All participants were able to synchronise their steps to a haptically presented rhythm. For a substantial proportion of participants, an immediate (though not necessarily lasting) improvement of temporal gait characteristics was found during cueing. Some improvements over baseline occurred immediately afterwards, rather than during, haptic cueing. Design issues and trade-offs are identified, and interactions between perception, sensory deficit, attention, memory, cognitive load and haptic entrainment are noted

High-tech aid tool to monitor postural stability in Parkinson’s Disease

Dissertação de mestrado integrado em Engenharia BiomédicaParkinson’s disease (PD) is a neurodegenerative disease that affects around 1% of the population over 65 and has increased in prevalence in recent years. One of the most disabling motor symptoms and a major contributor to falls is postural instability, which threatens the independence and well-being of people with PD. Usually, physicians assess this symptom with a traditional clinical examination named pull test, which, although easy to administer without requiring any instruments, it is a difficult test to standardize and lacks sensitivity to small but significant changes. Thus, other approaches based on high technologies have emerged to provide objective metrics and long-term data on postural stability, complementing clinical assessment. Wearable sensors appeared as a promising tech-based solution to better capture postural instability and eliminate the subjectivity of postural-associated clinical examinations. This dissertation proposes the design, development and validation of a postural assessment tool to perform more objective evaluations of postural instability during basic dynamic day-to-day activities. To achieve this goal, the following steps were accomplished: (i) create a dataset based on 3D motion data of PD patients performing the pull test and dynamic activities using an inertial measurement unit (IMU); (ii) extract relevant features from the data collected, conduct an extensive statistical search, and find correlations to clinical scales; (iii) implement a tool based in artificial intelligence (AI) to classify the level of postural instability through the data collected. Different deep learning models were designed and several combinations of data input were considered in order to find the best model to predict the pull test score. Overall, satisfactory results were achieved as the statistical analysis revealed that many features were considered relevant to distinguish between the scores of the pull test, for diagnostic purposes and also to differentiate the several stages of the disease and levels of motor disability. Regarding the AI-based tool, the results suggest that the combination of IMU-based data with deep learning may be a promising solution for postural instability assessment. The model that achieved the best performance in the testing phase with unseen data presented an accuracy, precision, recall and F1-score of approximately 0.86. The results also show that when fewer daily activities are included in the dataset, the complexity of the model reduces, making it more efficient. Despite the promising results, more data should be collected to assess the actual performance of the model as a postural assessment tool.A doença de Parkinson (DP) é uma doença neurodegenerativa que afeta cerca de 1% da população acima de 65 anos e cuja prevalência tem aumentado nos últimos anos. Um dos sintomas motores mais incapacitantes e um dos principais contribuintes para quedas é a instabilidade postural, que ameaça a independência e o bem-estar das pessoas com a DP. Normalmente, o teste utilizado para avaliar a instabilidade postural é o pull test, que, embora fácil de executar e não necessitando de qualquer instrumento, é um teste difícil de padronizar e com falta de sensibilidade para detetar pequenas alterações que podem ser significativas. Assim, os sensores vestíveis surgiram como uma solução promissora para capturar a instabilidade postural e eliminar a subjetividade dos exames clínicos associados à postura. Esta dissertação tem como objetivo o idealizar, desenvolver e validar um instrumento para realizar avaliações mais objetivas da instabilidade postural durante atividades dinâmicas básicas do dia-a-dia. Para atingir esse objetivo, as seguintes etapas foram realizadas: (i) criar um dataset baseado em dados de movimento 3D de pacientes com a DP enquanto executam o pull test e atividades dinâmicas através de uma unidade de medida inercial; (ii) extrair características relevantes dos dados adquiridos, realizar uma extensa pesquisa estatística e encontrar correlações com escalas clínicas; (iii) implementar uma ferramenta baseada em inteligência artificial (IA) para classificar o nível de instabilidade postural através dos dados recolhidos. É de notar que diferentes frameworks de deep learning foram projetados e vários datasets foram considerados de modo a encontrar o melhor modelo para prever a pontuação da escala do pull test. No geral, os resultados alcançados foram satisfatórios, pois o estudo estatístico revelou que muitas das características extraidas dos sinais recolhidos foram consideradas relevantes para distinguir entre as pontuações do pull test, para fins diagnósticos e também para diferenciar os estágios da doença e os níveis de incapacidade motora. Em relação à ferramenta baseada em IA, os resultados apresentados sugerem que o deep learning pode ser promissor na área de avaliação de instabilidade postural através de IMUs. O modelo que obteve o melhor desempenho apresentou uma exatidão, precisão, sensibilidade e F1-score no teste de aproximadamente 0.86. Os resultados também mostram que dataset com um menor número de actividades diferentes incluídas leva a que o modelo se torne menos complexo, tornando-o mais eficiente. Apesar dos resultados promissores, mais dados devem ser recolhidos para avaliar o real desempenho do modelo como ferramenta de avaliação postural

Automated Intelligent Cueing Device to Improve Ambient Gait Behaviors for Patients with Parkinson\u27s Disease

Freezing of gait (FoG) is a common motor dysfunction in individuals with Parkinson’s disease (PD). FoG impairs walking and is associated with increased fall risk. Although pharmacological treatments have shown promise during ON-medication periods, FoG remains difficult to treat during medication OFF state and in advanced stages of the disease. External cueing therapy in the forms of visual, auditory, and vibrotactile, has been effective in treating gait deviations. Intelligent (or on-demand) cueing devices are novel systems that analyze gait patterns in real-time and activate cues only at moments when specific gait alterations are detected. In this study we developed methods to analyze gait signals collected through wearable sensors and accurately identify FoG episodes. We also investigated the potential of predicting the symptoms before their actual occurrence. We collected data from seven participants with PD using two Inertial Measurement Units (IMUs) on ankles. In our first study, we extracted engineered features from the signals and used machine learning (ML) methods to identify FoG episodes. We tested the performance of models using patient-dependent and patient-independent paradigms. The former models achieved 92.5% and 89.0% for average sensitivity and specificity, respectively. However, the conventional binary classification methods fail to accurately classify data if only data from normal gait periods are available. In order to identify FoG episodes in participants who did not freeze during data collection sessions, we developed a Deep Gait Anomaly Detector (DGAD) to identify anomalies (i.e., FoG) in the signals. DGAD was formed of convolutional layers and trained to automatically learn features from signals. The convolutional layers are followed by fully connected layers to reduce the dimensions of the features. A k-nearest neighbors (kNN) classifier is then used to classify the data as normal or FoG. The models identified 87.4% of FoG onsets, with 21.9% being predicted on average for each participant. This study demonstrates our algorithm\u27s potential for delivery of preventive cues. The DGAD algorithm was then implemented in an Android application to monitor gait patterns of PD patients in ambient environments. The phone triggered vibrotactile and auditory cues on a connected smartwatch if an FoG episode was identified. A 6-week in-home study showed the potentials for effective treatment of FoG severity in ambient environments using intelligent cueing devices

Methods and models in signal processing for gait analysis using waist-worn accelerometer : a contribution to Parkinson’s disease

Parkinson's disease (PD) is a neurodegenerative disease that predominantly alters patients' motor performance and compromises the speed, the automaticity and fluidity of natural movements. After some years, patients fluctuate between periods in which they can move almost normally for some hours (ON state) and periods with motor disorders (OFF state). Reduced step length and inability of step are important symptoms associated with PD. Monitoring patients¿ step length helps to infer patients¿ motor state fluctuations during daily life and, therefore, enables neurologists to track the evolution of the disease and improve medication regimen. In this sense, MEMS accelerometers can be used to detect steps and to estimate the step length outside the laboratory setting during unconstrained daily life activities. This thesis presents the original contributions of the author in the field of human movement analysis based on MEMS accelerometers, specifically on step detection and step length estimation of patients with Parkinson's disease. In this thesis, a user-friendly position, the lateral side of the waist, is selected to locate a triaxial accelerometer. The position was selected to enhance comfortability and acceptability. Assuming this position, first, a new method for step detection has been developed for the signals captured by the accelerometer from this location. The method is validated on healthy persons and patients with Parkinson's disease while compared to current state-of-the-art methods, performing better than the existing ones. Second, current methods of selected step length estimators that were originally developed for the signals from lower back close to L4-L5 region are modified in order to be adapted to the new sensor positions. Results obtained from 25 PD patients are discussed and the effects of calibrating in each motor state are compared. A generic correction factor is also proposed and compared with the best method to use instead of individual calibration. Despite variable gait speed and different motor state, the new step detection method achieved overall accuracy of 96.76% in detecting steps. Comparing the original and adapted methods, adapted methods performs better than the original ones. The best one is with multiplying individual correction factors that consider left and right step length separately providing average error of 0.033 m. Finally, an adapted inverted pendulum (IP) model based step length estimators is proposed using the signals from left lateral side of waist. The model considers vertical displacement of waist as an inverted pendulum during right step.For left step, the displacement during single support and double support phase is considered as an inverted pendulum and a standard pendulum respectively.Results obtained from 25 PD patients are discussed.Validity and reliability of the new model is compared with three existing estimators. Experimental results show that ICE-CETpD estimates step length with higher accuracy than the three best contenders taken from the literature.The mean errors of this method during OFF state and ON states are 0.021m and 0.029m respectively.The standard deviation and RMSE shown as (SD) RMSE are (0.02)0.029m during OFF state and (0.027)0.038m during ON state. The intra-class correlations of proposed estimator with reference step length are above 0.9 during both motor states.The calibration of model parameters in each motor state is tested and found that the training sessions done with patients in ON state provide more accurate results than in OFF state. Given that training is in ON state, the advantage of this approach is that patients would not need to attend without medication in order to train the method.La enfermedad de Parkinson (EP) es una enfermedad neurodegenerativa que altera, de forma predominante, la capacidad motora de los pacientes y, además, afecta la velocidad, la automaticidad y la fluidez de los movimientos naturales. Tras varios años, los pacientes fluctúan entre unos periodos en los cuales pueden moverse de forma casi normal durante varias horas (periodos o estados ON) y periodos donde los desórdenes del movimiento aparecen (periodos o estados OFF). Entre otros síntomas, los pacientes con la EP sufren una reducción de la longitud del paso y una inhabilitación de la marcha. Monitorizar la longitud del paso contribuye a inferir el estado motor de los pacientes, a conocer las fluctuaciones durante su vida diaria y, en consecuencia, permitiría a los neurólogos realizar un seguimiento de la evolución de la enfermedad y mejorar la pauta terapéutica. En este sentido, los acelerómetros MEMS pueden ser usados para detectar pasos y estimar la longitud del paso más allá de las instalaciones de los laboratorios, es decir, en entornos no controlados. Esta tesis presenta las contribuciones originales del autor en el campo del análisis del movimiento humano basado en acelerómetros MEMS, específicamente en la detección de pasos y la estimación de la longitud del paso en pacientes con la EP. En esta tesis, se ha seleccionado una posición amigable en la cual localizar un acelerómetro MEMS triaxial. La posición, que consiste en el lateral de la cintura cerca de la cresta ilíaca, fue seleccionada para mejorar la comodidad y la aceptabilidad desde el punto de vista del paciente. Asumiendo esta posición, en primer lugar, se presenta un análisis de los distintos métodos existentes en la literatura para la detección de pasos y, además, se presenta una nueva técnica de detección. Los métodos se han testado en usuarios sanos y en pacientes con Parkinson, mostrando que el nuevo método obtiene un porcentaje de acierto en la detección más alto que el resto de métodos. En segundo lugar, se han seleccionado aquellos métodos de estimación de la longitud de paso que fueron desarrollados mediante un sensor situado en el centro de la espalda, cerca de las vértebras L4-L5. Estos métodos fueron modificados con el fin de ser adaptados a la nueva posición del sensor y validados en señales obtenidas de 25 pacientes con EP. Además, se propone un factor de corrección genérico, el cual se compara con el mejor de los métodos obtenidos, para ser usado en lugar de una calibración individual. A pesar de la variabilidad en la velocidad de la marcha debida a las fluctuaciones motoras, el nuevo método alcanza un 96,76% de precisión en la detección de pasos y, respecto la estimación de la longitud del paso, los métodos modificados obtienen mayor precisión que los originales. El mejor de los métodos obtenidos consiste en el uso de un factor de corrección multiplicador que considera los pasos de cada lado por separado, proporcionando un error medio de 0,03 m. Finalmente, se presenta un nuevo modelo de la marcha representada como un péndulo invertido modificado que se emplea para analizar las señales de acelerometría obtenidas desde el lateral izquierdo de la cintura. De forma más concreta, este modelo considera el desplazamiento vertical de la cadera como un péndulo invertido durante el paso derecho (lado contrario del sensor). Para el paso izquierdo, el desplazamiento durante la fase single support y double support se model iza como un péndulo invertido y un péndulo simple, respectivamente. Los resultados obtenidos en 25 pacientes con EP son presentados y discutidos. La validez y fiabilidad del nuevo modelo son comparados con tres modelos distintos. Los resultados experimentales obtenidos muestran que el nuevo modelo, llamado ICE-CETpD, estima la longitud del paso con una precisión mayor que el resto de métodos seleccionados de la literatura. El error promedio de este método durante el estado OFF y ON es de 0,021 m. y 0,029 m., respectivamente, con una correlación intraclase superior a 0.9 en ambos estados motores. La calibración de los parámetros del modelo en cada estado motor ha sido evaluada, concluyendo que una calibración en ON proporciona más precisión en los resultados. En consecuencia, la ventaja de la aproximación propuesta residiría en no requerir señales en OFF de los pacientes con EP, por lo cual no sería necesario que los pacientes prescindieran de tomas de medicación.La malaltia de Parkinson (MP) és una malaltia neurodegenerativa que altera de forma predominant la capacitat motora dels pacients i, a més, afecta la velocitat, l’automatització i la fluïdesa dels moviments naturals. Després de diversos anys, els pacients fluctuen entre uns períodes en els quals poden moure’s de forma quasi normal i que duren vàries hores (períodes o estats ON) i períodes on els desordres del moviment apareixen (períodes o estats OFF). Entre altres símptomes, els pacients amb la MP sofreixen una reducció de la longitud del pas i una inhabilitació de la marxa. La monitorització de la longitud del pas contribueix a inferir l’estat motor del pacient i a conèixer les fluctuacions durant la seva vida diària permetent als neuròlegs, en conseqüència, realitzar un seguiment de l’evolució de la malaltia i millorar la pauta terapèutica. En aquest sentit, els acceleròmetres MEMS poden ser utilitzats per tal de detectar passes i estimar la longitud del pas fora de les instal·lacions dels laboratoris, és a dir, en entorns no controlats. Aquesta tesis presenta les contribucions originals de l’autor en el camp de l’anàlisi del moviment humà basat en acceleròmetres MEMS, específicament en la detecció de passes i l’estimació de la longitud del pas en pacients amb MP. En aquesta tesis, s’ha seleccionat una posició amigable en la qual localitzar un acceleròmetre MEMS triaxial. La posició, que consisteix en el lateral de la cintura prop de la cresta ilíaca, va ser seleccionada per maximitzar la comoditat i l’acceptabilitat des del punt de vista del pacient. Assumint aquesta posició, en primer lloc, es presenta un anàlisi dels diferents mètodes existents a la literatura en detecció de passes i, a més, es presenta una nova tècnica de detecció basada en acceleròmetres. Tots els mètodes han estat provats en usuaris sans i en pacients amb la MP; els resultats mostren que el nou mètode obté un percentatge d’encert en la detecció de passes més alt que la resta de mètodes. En segon lloc, s’han seleccionat aquells mètodes d’estimació de la longitud de pas que van ser desenvolupats per a tractar les senyals d’un sensor situat prop de les vèrtebres L4-L5. Aquests mètodes van ser modificats amb la fi de ser adaptats a la nova posició del sensor. Tots ells van ser validats en senyals obtingudes de 25 pacients amb la MP. A més, es proposa un factor de correcció genèric, el qual es compara amb el millor dels mètodes obtinguts per tal de ser usat en lloc d’una calibració individual. A pesar de la variabilitat en la velocitat de la marxa deguda a les fluctuacions motores, el nou mètode assoleix un 96,76% de precisió en la detecció de passes i, respecte l’estimació de la longitud de pas, els mètodes modificats obtenen una major precisió que els originals. El millor d’ells consisteix en un factor de correcció multiplicador que considera les passes de cada costat per separat, proporcionant un error mig de 0,033 m. Finalment, es presenta un nou model de la marxa representada com un pèndul invertit modificat que és utilitzat per analitzar les senyals d’accelerometria obtingudes des del lateral esquerra de la cintura. De forma més concreta, aquest model considera el desplaçament vertical del maluc com un pèndul invertit durant la passa dreta (costat contrari al del sensor). Durant la passa esquerra, el desplaçament durant la fase single suport i double suport es modelitza com un pèndul invertit i un pèndul simple, respectivament. Els resultats obtinguts en 25 pacients amb MP són presentats i discutits. La validesa i fiabilitat del nou model són comparats amb els de tres models diferents. Els resultats experimentals obtinguts mostren que el nou model, anomenat ICE—CETpD, estima la longitud de la passa amb una major precisió que la resta de mètodes seleccionats de la literatura. L’error mitjà d’aquest mètode durant l’estat OFF i ON és de 0, 021 i 0,029 m., respectivament, amb una correlació intraclasse superior a 0,9 en ambdós estats motors. La calibració dels paràmetres del model en cada estat motor ha estat avaluada, obtenint que una calibració en ON proporciona més precisió en els resultats. D’aquesta manera, l’avantatge de l’aproximació proposada residiria en no requerir de senyals en OFF dels pacients amb MP, per la qual cosa no seria necessari que els pacients prescindissin de preses de medicació

An investigation of the contribution of axial rigidity to turning deficits in individuals with Parkinson’s disease

Difficulty in turning is prominent in individuals with Parkinson’s disease (PD) and the resulting postural instability increases their risk of falling. Therefore, understanding the mechanisms underpinning turning deficits in PD is highly important for rehabilitation and fall prevention. The first aim of this thesis was to clarify the mechanisms of increased fall risk during turning in PD by emulating: head and neck rigidity in healthy adults, and then observing the effects on eye movements and whole-body coordination while turning on the spot. The results revealed that experimentally inducing head and neck rigidity had multiple effects on eye movement characteristics, step amplitude, and total steps taken to complete the turn. The resultant behaviour was similar to that previously observed in individuals with PD. The second aim of this thesis was to validate the use of Inertial Measurement Units (IMUs) in combination with mobile electrooculography (EOG) for measuring eye, head and whole body coordination during turning with a view to developing a methodology that could be used to assess the effects of exercise intervention on turning characteristics of PD patients in a clinical setting. The results showed excellent reliability when compared with measures obtained using a Vicon motion analysis system suggesting that IMUs provide a viable alternative to camera-based motion capture for accurately measuring turning behaviour. The third aim of the study was to conduct a scoping review to determine whether exercise-based rehabilitation is effective in reducing axial rigidity in individuals with PD. Four out of eleven studies eligible for inclusion focused explicitly on exercise-based treatment for axial rigidity in individuals with PD. The results of the scoping review were used to design a modified exercise programme aimed at improving axial rigidity and turning dysfunction in individuals with PD. The final aim was to carry out a pilot Randomized Control Trial to study the effects of a modified exercise programme on various markers of functional mobility and turning performance in PD patients; e.g., Unified Parkinson’s Disease Rating Scale (UPDRS) functional reach test, step size, total steps and improvement in fall efficacy scale in individuals with PD. These preliminary results support the notion that targeting axial deficits may be an effective rehabilitation approach for improving mobility and reducing falls in PD