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

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

Smart Technology for Telerehabilitation: A Smart Device Inertial-sensing Method for Gait Analysis

The aim of this work was to develop and validate an iPod Touch (4th generation) as a potential ambulatory monitoring system for clinical and non-clinical gait analysis. This thesis comprises four interrelated studies, the first overviews the current available literature on wearable accelerometry-based technology (AT) able to assess mobility-related functional activities in subjects with neurological conditions in home and community settings. The second study focuses on the detection of time-accurate and robust gait features from a single inertial measurement unit (IMU) on the lower back, establishing a reference framework in the process. The third study presents a simple step length algorithm for straight-line walking and the fourth and final study addresses the accuracy of an iPod’s inertial-sensing capabilities, more specifically, the validity of an inertial-sensing method (integrated in an iPod) to obtain time-accurate vertical lower trunk displacement measures. The systematic review revealed that present research primarily focuses on the development of accurate methods able to identify and distinguish different functional activities. While these are important aims, much of the conducted work remains in laboratory environments, with relatively little research moving from the “bench to the bedside.” This review only identified a few studies that explored AT’s potential outside of laboratory settings, indicating that clinical and real-world research significantly lags behind its engineering counterpart. In addition, AT methods are largely based on machine-learning algorithms that rely on a feature selection process. However, extracted features depend on the signal output being measured, which is seldom described. It is, therefore, difficult to determine the accuracy of AT methods without characterizing gait signals first. Furthermore, much variability exists among approaches (including the numbers of body-fixed sensors and sensor locations) to obtain useful data to analyze human movement. From an end-user’s perspective, reducing the amount of sensors to one instrument that is attached to a single location on the body would greatly simplify the design and use of the system. With this in mind, the accuracy of formerly identified or gait events from a single IMU attached to the lower trunk was explored. The study’s analysis of the trunk’s vertical and anterior-posterior acceleration pattern (and of their integrands) demonstrates, that a combination of both signals may provide more nuanced information regarding a person’s gait cycle, ultimately permitting more clinically relevant gait features to be extracted. Going one step further, a modified step length algorithm based on a pendulum model of the swing leg was proposed. By incorporating the trunk’s anterior-posterior displacement, more accurate predictions of mean step length can be made in healthy subjects at self-selected walking speeds. Experimental results indicate that the proposed algorithm estimates step length with errors less than 3% (mean error of 0.80 ± 2.01cm). The performance of this algorithm, however, still needs to be verified for those suffering from gait disturbances. Having established a referential framework for the extraction of temporal gait parameters as well as an algorithm for step length estimations from one instrument attached to the lower trunk, the fourth and final study explored the inertial-sensing capabilities of an iPod Touch. With the help of Dr. Ian Sheret and Oxford Brookes’ spin-off company ‘Wildknowledge’, a smart application for the iPod Touch was developed. The study results demonstrate that the proposed inertial-sensing method can reliably derive lower trunk vertical displacement (intraclass correlations ranging from .80 to .96) with similar agreement measurement levels to those gathered by a conventional inertial sensor (small systematic error of 2.2mm and a typical error of 3mm). By incorporating the aforementioned methods, an iPod Touch can potentially serve as a novel ambulatory monitor system capable of assessing gait in clinical and non-clinical environments

A review of abnormal behavior detection in activities of daily living

Abnormal behavior detection (ABD) systems are built to automatically identify and recognize abnormal behavior from various input data types, such as sensor-based and vision-based input. As much as the attention received for ABD systems, the number of studies on ABD in activities of daily living (ADL) is limited. Owing to the increasing rate of elderly accidents in the home compound, ABD in ADL research should be given as much attention to preventing accidents by sending out signals when abnormal behavior such as falling is detected. In this study, we compare and contrast the formation of the ABD system in ADL from input data types (sensor-based input and vision-based input) to modeling techniques (conventional and deep learning approaches). We scrutinize the public datasets available and provide solutions for one of the significant issues: the lack of datasets in ABD in ADL. This work aims to guide new research to understand the field of ABD in ADL better and serve as a reference for future study of better Ambient Assisted Living with the growing smart home trend

A Review of Techniques on Gait-Based Person Re-Identification

Copyright (c) 2023 Babak Rahi, Maozhen Li and Man Qi. Person re-identification at a distance across multiple non-overlapping cameras has been an active research area for years. In the past ten years, short-term Person re-identification techniques have made great strides in accuracy using only appearance features in limited environments. However, massive intra-class variations and inter-class confusion limit their ability to be used in practical applications. Moreover, appearance consistency can only be assumed in a short time span from one camera to the other. Since the holistic appearance will change drastically over days and weeks, the technique, as mentioned above, will be ineffective. Practical applications usually require a long-term solution in which the subject's appearance and clothing might have changed after the elapse of a significant period. Facing these problems, soft biometric features such as Gait has stirred much interest in the past years. Nevertheless, even Gait can vary with illness, ageing and emotional states, walking surfaces, shoe types, clothes types, carried objects (by the subject) and even environment clutters. Therefore, Gait is considered as a temporal cue that could provide biometric motion information. On the other hand, the shape of the human body could be viewed as a spatial signal which can produce valuable information. So extracting discriminative features from both spatial and temporal domains would benefit this research. This article examines the main approaches used in gait analysis for re-identification over the past decade. We identify several relevant dimensions of the problem and provide a taxonomic analysis of current research. We conclude by reviewing the performance levels achievable with current technology and providing a perspective on the most challenging and promising research directions.This research received no external funding

Context-aware home monitoring system for Parkinson's disease patients : ambient and wearable sensing for freezing of gait detection

Tesi en modalitat de cotutela: Universitat Politècnica de Catalunya i Technische Universiteit Eindhoven. This PhD Thesis has been developed in the framework of, and according to, the rules of the Erasmus Mundus Joint Doctorate on Interactive and Cognitive Environments EMJD ICE [FPA no. 2010-0012]Parkinson’s disease (PD). It is characterized by brief episodes of inability to step, or by extremely short steps that typically occur on gait initiation or on turning while walking. The consequences of FOG are aggravated mobility and higher affinity to falls, which have a direct effect on the quality of life of the individual. There does not exist completely effective pharmacological treatment for the FOG phenomena. However, external stimuli, such as lines on the floor or rhythmic sounds, can focus the attention of a person who experiences a FOG episode and help her initiate gait. The optimal effectiveness in such approach, known as cueing, is achieved through timely activation of a cueing device upon the accurate detection of a FOG episode. Therefore, a robust and accurate FOG detection is the main problem that needs to be solved when developing a suitable assistive technology solution for this specific user group. This thesis proposes the use of activity and spatial context of a person as the means to improve the detection of FOG episodes during monitoring at home. The thesis describes design, algorithm implementation and evaluation of a distributed home system for FOG detection based on multiple cameras and a single inertial gait sensor worn at the waist of the patient. Through detailed observation of collected home data of 17 PD patients, we realized that a novel solution for FOG detection could be achieved by using contextual information of the patient’s position, orientation, basic posture and movement on a semantically annotated two-dimensional (2D) map of the indoor environment. We envisioned the future context-aware system as a network of Microsoft Kinect cameras placed in the patient’s home that interacts with a wearable inertial sensor on the patient (smartphone). Since the hardware platform of the system constitutes from the commercial of-the-shelf hardware, the majority of the system development efforts involved the production of software modules (for position tracking, orientation tracking, activity recognition) that run on top of the middle-ware operating system in the home gateway server. The main component of the system that had to be developed is the Kinect application for tracking the position and height of multiple people, based on the input in the form of 3D point cloud data. Besides position tracking, this software module also provides mapping and semantic annotation of FOG specific zones on the scene in front of the Kinect. One instance of vision tracking application is supposed to run for every Kinect sensor in the system, yielding potentially high number of simultaneous tracks. At any moment, the system has to track one specific person - the patient. To enable tracking of the patient between different non-overlapped cameras in the distributed system, a new re-identification approach based on appearance model learning with one-class Support Vector Machine (SVM) was developed. Evaluation of the re-identification method was conducted on a 16 people dataset in a laboratory environment. Since the patient orientation in the indoor space was recognized as an important part of the context, the system necessitated the ability to estimate the orientation of the person, expressed in the frame of the 2D scene on which the patient is tracked by the camera. We devised method to fuse position tracking information from the vision system and inertial data from the smartphone in order to obtain patient’s 2D pose estimation on the scene map. Additionally, a method for the estimation of the position of the smartphone on the waist of the patient was proposed. Position and orientation estimation accuracy were evaluated on a 12 people dataset. Finally, having available positional, orientation and height information, a new seven-class activity classification was realized using a hierarchical classifier that combines height-based posture classifier with translational and rotational SVM movement classifiers. Each of the SVM movement classifiers and the joint hierarchical classifier were evaluated in the laboratory experiment with 8 healthy persons. The final context-based FOG detection algorithm uses activity information and spatial context information in order to confirm or disprove FOG detected by the current state-of-the-art FOG detection algorithm (which uses only wearable sensor data). A dataset with home data of 3 PD patients was produced using two Kinect cameras and a smartphone in synchronized recording. The new context-based FOG detection algorithm and the wearable-only FOG detection algorithm were both evaluated with the home dataset and their results were compared. The context-based algorithm very positively influences the reduction of false positive detections, which is expressed through achieved higher specificity. In some cases, context-based algorithm also eliminates true positive detections, reducing sensitivity to the lesser extent. The final comparison of the two algorithms on the basis of their sensitivity and specificity, shows the improvement in the overall FOG detection achieved with the new context-aware home system.Esta tesis propone el uso de la actividad y el contexto espacial de una persona como medio para mejorar la detección de episodios de FOG (Freezing of gait) durante el seguimiento en el domicilio. La tesis describe el diseño, implementación de algoritmos y evaluación de un sistema doméstico distribuido para detección de FOG basado en varias cámaras y un único sensor de marcha inercial en la cintura del paciente. Mediante de la observación detallada de los datos caseros recopilados de 17 pacientes con EP, nos dimos cuenta de que se puede lograr una solución novedosa para la detección de FOG mediante el uso de información contextual de la posición del paciente, orientación, postura básica y movimiento anotada semánticamente en un mapa bidimensional (2D) del entorno interior. Imaginamos el futuro sistema de consciencia del contexto como una red de cámaras Microsoft Kinect colocadas en el hogar del paciente, que interactúa con un sensor de inercia portátil en el paciente (teléfono inteligente). Al constituirse la plataforma del sistema a partir de hardware comercial disponible, los esfuerzos de desarrollo consistieron en la producción de módulos de software (para el seguimiento de la posición, orientación seguimiento, reconocimiento de actividad) que se ejecutan en la parte superior del sistema operativo del servidor de puerta de enlace de casa. El componente principal del sistema que tuvo que desarrollarse es la aplicación Kinect para seguimiento de la posición y la altura de varias personas, según la entrada en forma de punto 3D de datos en la nube. Además del seguimiento de posición, este módulo de software también proporciona mapeo y semántica. anotación de zonas específicas de FOG en la escena frente al Kinect. Se supone que una instancia de la aplicación de seguimiento de visión se ejecuta para cada sensor Kinect en el sistema, produciendo un número potencialmente alto de pistas simultáneas. En cualquier momento, el sistema tiene que rastrear a una persona específica - el paciente. Para habilitar el seguimiento del paciente entre diferentes cámaras no superpuestas en el sistema distribuido, se desarrolló un nuevo enfoque de re-identificación basado en el aprendizaje de modelos de apariencia con one-class Suport Vector Machine (SVM). La evaluación del método de re-identificación se realizó con un conjunto de datos de 16 personas en un entorno de laboratorio. Dado que la orientación del paciente en el espacio interior fue reconocida como una parte importante del contexto, el sistema necesitaba la capacidad de estimar la orientación de la persona, expresada en el marco de la escena 2D en la que la cámara sigue al paciente. Diseñamos un método para fusionar la información de seguimiento de posición del sistema de visión y los datos de inercia del smartphone para obtener la estimación de postura 2D del paciente en el mapa de la escena. Además, se propuso un método para la estimación de la posición del Smartphone en la cintura del paciente. La precisión de la estimación de la posición y la orientación se evaluó en un conjunto de datos de 12 personas. Finalmente, al tener disponible información de posición, orientación y altura, se realizó una nueva clasificación de actividad de seven-class utilizando un clasificador jerárquico que combina un clasificador de postura basado en la altura con clasificadores de movimiento SVM traslacional y rotacional. Cada uno de los clasificadores de movimiento SVM y el clasificador jerárquico conjunto se evaluaron en el experimento de laboratorio con 8 personas sanas. El último algoritmo de detección de FOG basado en el contexto utiliza información de actividad e información de texto espacial para confirmar o refutar el FOG detectado por el algoritmo de detección de FOG actual. El algoritmo basado en el contexto influye muy positivamente en la reducción de las detecciones de falsos positivos, que se expresa a través de una mayor especificida

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