Mobile Quantification and Therapy Course Tracking for Gait Rehabilitation

This paper presents a novel autonomous quality metric to quantify the rehabilitations progress of subjects with knee/hip operations. The presented method supports digital analysis of human gait patterns using smartphones. The algorithm related to the autonomous metric utilizes calibrated acceleration, gyroscope and magnetometer signals from seven Inertial Measurement Unit attached on the lower body in order to classify and generate the grading system values. The developed Android application connects the seven Inertial Measurement Units via Bluetooth and performs the data acquisition and processing in real-time. In total nine features per acceleration direction and lower body joint angle are calculated and extracted in real-time to achieve a fast feedback to the user. We compare the classification accuracy and quantification capabilities of Linear Discriminant Analysis, Principal Component Analysis and Naive Bayes algorithms. The presented system is able to classify patients and control subjects with an accuracy of up to 100\%. The outcomes can be saved on the device or transmitted to treating physicians for later control of the subject's improvements and the efficiency of physiotherapy treatments in motor rehabilitation. The proposed autonomous quality metric solution bears great potential to be used and deployed to support digital healthcare and therapy.Comment: 5 Page

Towards a wearable system for predicting the freezing of gait in people affected by Parkinson's disease

Some wearable solutions exploiting on-body acceleration sensors have been proposed to recognize Freezing of Gait (FoG) in people affected by Parkinson Disease (PD). Once a FoG event is detected, these systems generate a sequence of rhythmic stimuli to allow the patient restarting the march. While these solutions are effective in detecting FoG events, they are unable to predict FoG to prevent its occurrence. This paper fills in the gap by presenting a machine learning-based approach that classifies accelerometer data from PD patients, recognizing a pre-FOG phase to further anticipate FoG occurrence in advance. Gait was monitored by three tri-axial accelerometer sensors worn on the back, hip and ankle. Gait features were then extracted from the accelerometer's raw data through data windowing and non-linear dimensionality reduction. A k-nearest neighbor algorithm (k-NN) was used to classify gait in three classes of events: pre-FoG, no-FoG and FoG. The accuracy of the proposed solution was compared to state of-the-art approaches. Our study showed that: (i) we achieved performances overcoming the state-of-the-art approaches in terms of FoG detection, (ii) we were able, for the very first time in the literature, to predict FoG by identifying the pre-FoG events with an average sensitivity and specificity of, respectively, 94.1% and 97.1%, and (iii) our algorithm can be executed on resource-constrained devices. Future applications include the implementation on a mobile device, and the administration of rhythmic stimuli by a wearable device to help the patient overcome the FoG

GAIT RECOGNITION PROGRESS IN RECOGNIZING IMAGE CHARACTERISTICS

We present a humans credentials system centered on ambulation characteristics. This problem is as eminent as acoustic gait recognition. The objective of the scheme is to explore sounds radiated by walking persons (largely the musical note sounds) and identifies those folks. A cyclic model topology is engaged to denote individual gait cycles. This topology permits modeling and detecting individual steps, leading to very favorable identification rates

First advances in near fall detection and prediction when using a walker

Dissertação de mestrado integrado em Engenharia Biomédica (área de especialização em Eletrónica Médica)Falls are a major concern to society. Several injuries associated with falls need medical care, and in the worst-case scenario, a fall can lead to death. These consequences have a high cost for the population. In order to overcome these problems, a diversity of approaches for detection, prediction, and prevention of falls have been tackled. Walkers are often prescribed to subjects who present a higher risk of falling. Thus, it is essential to develop strategies to enhance the user's safety in an imminent danger situation. In this sense, this dissertation aims to develop a strategy to detect a near fall (NF) and its direction as well as the detection of incipient near fall (INF) while the subject uses a walker. Furthermore, it has the purpose of detecting two gait events, the heel strike (HS) and the toe-off (TO). The strategies established, in this work, were based on the data gathered through an inertial sensor placed on the lower trunk and force sensors placed on the insoles. Following data collection, the methodology adopted to identify the situations aforementioned was based on machine learning algorithms. In order to reach the model with best performance, many combinations of different classifiers were tested with three feature selection methods. Regarding the detection of NF, the results achieved presented a Matthews Correlation Coefficient (MCC) of 79.99% being possible to detect a NF 1.76±0.76s before its end. With the implementation of the post-processing algorithm, a large part of the false positives was eliminated being able to detect all NF 1.48±0.68s before its end. Concerning the models built to distinguish the direction of the NF, the best model presented accuracy of 58.97% being unable to reliably distinguish the three fall directions. The methodology followed, in this work, was unsuccessful to detect an INF. The best model presented MCC=23.87%, in this case. Lastly, with respect to the detection of HS and TO events the best model reached MCC=86.94%. With the application of the post-processing algorithm, part of misclassified samples was eliminated, however, a delay in the detection of the HS and TO events was introduced. With the post-processing it was possible to reach MCC=88.82%, not including the imposed delay.As quedas representam uma grande preocupação para a sociedade. Várias lesões associadas às quedas necessitam de cuidados médicos e, no pior dos casos, uma queda pode levar à morte. Estas consequências traduzem-se em custos elevados para a população. A fim de ultrapassar estes problemas, várias abordagens têm sido endereçadas para deteção, previsão e prevenção das quedas. Os andarilhos são muitas vezes prescritos a sujeitos que apresentam um risco de queda maior. Desta forma, é essencial desenvolver estratégias para aumentar a segurança do utilizador perante uma situação de perigo iminente. Neste sentido, esta dissertação visa desenvolver uma estratégia que permita a deteção de uma quase queda (NF) e a sua direção, assim como a deteção incipiente de uma NF (INF). Para além disso, tem o objetivo de detetar dois eventos de marcha, o heel strike (HS) e o toe-off (TO). As estratégias definidas, neste trabalho, basearam-se nos dados recolhidos através de um sensor inercial posicionado no tronco inferior e de sensores de força colocados nas palmilhas. Após a aquisição dos dados, a metodologia adotada para identificar as situações anteriormente referidas foi baseada em algoritmos de machine learning. Com o intuito de obter o modelo com melhor desempenho, várias combinações de diferentes classificadores foram testadas com três métodos de seleção de features. No que concerne à deteção da NF, os resultados alcançados apresentaram um Matthews Correlation Coefficient (MCC) de 79.99% sendo possível detetar uma NF 1.76±0.76s antes do seu final. Com a implementação do algoritmo de pós-processamento, grande parte dos falsos positivos foram eliminados, sendo possível detetar todas as NF 1.48±0.68s antes do seu final. Em relação aos modelos construídos para distinguir a direção da NF, o melhor modelo apresentou uma precisão (ACC) de 59.97%. A metodologia seguida neste trabalho não foi bem sucedida na deteção INF. O melhor modelo apresentou um MCC=23.87%. Relativamente à deteção dos eventos, HS e TO, o melhor modelo atingiu um MCC=86.94%. Com a aplicação do algoritmo de pós-processamento parte das amostras mal classificadas foram eliminadas, no entanto, foi introduzido um atraso na deteção do HS e do TO. Com o pós-processamento foi possível obter um MCC=88.82%, não incluindo o atraso imposto pelo pós-processamento

An IoT based Virtual Coaching System (VSC) for Assisting Activities of Daily Life

Nowadays aging of the population is becoming one of the main concerns of theworld. It is estimated that the number of people aged over 65 will increase from 461million to 2 billion in 2050. This substantial increment in the elderly population willhave significant consequences in the social and health care system. Therefore, in thecontext of Ambient Intelligence (AmI), the Ambient Assisted Living (AAL) has beenemerging as a new research area to address problems related to the aging of the population. AAL technologies based on embedded devices have demonstrated to be effectivein alleviating the social- and health-care issues related to the continuous growing of theaverage age of the population. Many smart applications, devices and systems have beendeveloped to monitor the health status of elderly, substitute them in the accomplishment of activities of the daily life (especially in presence of some impairment or disability),alert their caregivers in case of necessity and help them in recognizing risky situations.Such assistive technologies basically rely on the communication and interaction be-tween body sensors, smart environments and smart devices. However, in such contextless effort has been spent in designing smart solutions for empowering and supportingthe self-efficacy of people with neurodegenerative diseases and elderly in general. Thisthesis fills in the gap by presenting a low-cost, non intrusive, and ubiquitous VirtualCoaching System (VCS) to support people in the acquisition of new behaviors (e.g.,taking pills, drinking water, finding the right key, avoiding motor blocks) necessary tocope with needs derived from a change in their health status and a degradation of theircognitive capabilities as they age. VCS is based on the concept of extended mind intro-duced by Clark and Chalmers in 1998. They proposed the idea that objects within theenvironment function as a part of the mind. In my revisiting of the concept of extendedmind, the VCS is composed of a set of smart objects that exploit the Internet of Things(IoT) technology and machine learning-based algorithms, in order to identify the needsof the users and react accordingly. In particular, the system exploits smart tags to trans-form objects commonly used by people (e.g., pillbox, bottle of water, keys) into smartobjects, it monitors their usage according to their needs, and it incrementally guidesthem in the acquisition of new behaviors related to their needs. To implement VCS, thisthesis explores different research directions and challenges. First of all, it addresses thedefinition of a ubiquitous, non-invasive and low-cost indoor monitoring architecture byexploiting the IoT paradigm. Secondly, it deals with the necessity of developing solu-tions for implementing coaching actions and consequently monitoring human activitiesby analyzing the interaction between people and smart objects. Finally, it focuses on the design of low-cost localization systems for indoor environment, since knowing theposition of a person provides VCS with essential information to acquire information onperformed activities and to prevent risky situations. In the end, the outcomes of theseresearch directions have been integrated into a healthcare application scenario to imple-ment a wearable system that prevents freezing of gait in people affected by Parkinson\u2019sDisease

Features Mapping Based Human Gait Recognition

Gait recognition is the term used for detection of Human based on the features. The Feature extraction and Feature Mapping is the main aspect to recognize the Gestures from the Database of features. Recognition of any individual is a task to identify people. Human recognition methods such as face, fingerprints, and iris generally require a cooperative subject, physical contact or close proximity. These methods are not able to recognize an individual at a distance therefore recognition using gait is relatively new biometric technique without these disadvantages. Human identification using Gait is method to identify an individual by the way he walk or manner of moving on foot. Gait recognition is a type of biometric recognition and related to the behavioral characteristics of biometric recognition. Gait offers ability of distance recognition or at low resolution. This project aims to recognize an individual using his gait features. However the majority of current approaches are model free which is simple and fast but we will use model based approach for feature extraction and for matching of parameters with database sequences. After matching of Features, the Images have been identified and show the dataset from it matched. The Results are accurate and shows efficiency. In this firstly binary silhouette of a walking person is detected from each frame of an image. Then secondly, the features from each frame are extracted using the image processing operation. In the end SVM, K-MEANS and LDA are used for training and testing purpose. Every experiment and test is done on CASIA database. The results in this paper are better and improved from previous results by using SVM , K MEANS. DOI: 10.17762/ijritcc2321-8169.15067

Pathology detection mechanisms through continuous acquisition of biological signals

Mención Internacional en el título de doctorPattern identification is a widely known technology, which is used on a daily basis for both identification and authentication. Examples include biometric identification (fingerprint or facial), number plate recognition or voice recognition. However, when we move into the world of medical diagnostics this changes substantially. This field applies many of the recent innovations and technologies, but it is more difficult to see cases of pattern recognition applied to diagnostics. In addition, the cases where they do occur are always supervised by a specialist and performed in controlled environments. This behaviour is expected, as in this field, a false negative (failure to identify pathology when it does exists) can be critical and lead to serious consequences for the patient. This can be mitigated by configuring the algorithm to be safe against false negatives, however, this will raise the false positive rate, which may increase the workload of the specialist in the best case scenario or even result in a treatment being given to a patient who does not need it. This means that, in many cases, validation of the algorithm’s decision by a specialist is necessary, however, there may be cases where this validation is not so essential, or where this first identification can be treated as a guideline to help the specialist. With this objective in mind, this thesis focuses on the development of an algorithm for the identification of lower body pathologies. This identification is carried out by means of the way people walk (gait). People’s gait differs from one person to another, even making biometric identification possible through its use. however, when the people has a pathology, both physical or psychological, the gait is affected. This alteration generates a common pattern depending on the type of pathology. However, this thesis focuses exclusively on the identification of physical pathologies. Another important aspect in this thesis is that the different algorithms are created with the idea of portability in mind, avoiding the obligation of the user to carry out the walks with excessive restrictions (both in terms of clothing and location). First, different algorithms are developed using different configurations of smartphones for database acquisition. In particular, configurations using 1, 2 and 4 phones are used. The phones are placed on the legs using special holders so that they cannot move freely. Once all the walks have been captured, the first step is to filter the signals to remove possible noise. The signals are then processed to extract the different gait cycles (corresponding to two steps) that make up the walks. Once the feature extraction process is finished, part of the features are used to train different machine learning algorithms, which are then used to classify the remaining features. However, the evidence obtained through the experiments with the different configurations and algorithms indicates that it is not feasible to perform pathology identification using smartphones. This can be mainly attributed to three factors: the quality of the signals captured by the phones, the unstable sampling frequency and the lack of synchrony between the phones. Secondly, due to the poor results obtained using smartphones, the capture device is changed to a professional motion acquisition system. In addition, two types of algorithm are proposed, one based on neural networks and the other based on the algorithms used previously. Firstly, the acquisition of a new database is proposed. To facilitate the capture of the data, a procedure is established, which is proposed to be in an environment of freedom for the user. Once all the data are available, the preprocessing to be carried out is similar to that applied previously. The signals are filtered to remove noise and the different gait cycles that make up the walks are extracted. However, as we have information from several sensors and several locations for the capture device, instead of using a common cut-off frequency, we empirically set a cut-off frequency for each signal and position. Since we already have the data ready, a recurrent neural network is created based on the literature, so we can have a first approximation to the problem. Given the feasibility of the neural network, different experiments are carried out with the aim of improving the performance of the neural network. Finally, the other algorithm picks up the legacy of what was seen in the first part of the thesis. As before, this algorithm is based on the parameterisation of the gait cycles for its subsequent use and employs algorithms based on machine learning. Unlike the use of time signals, by parameterising the cycles, spurious data can be generated. To eliminate this data, the dataset undergoes a preparation phase (cleaning and scaling). Once a prepared dataset has been obtained, it is split in two, one part is used to train the algorithms, which are used to classify the remaining samples. The results of these experiments validate the feasibility of this algorithm for pathology detection. Next, different experiments are carried out with the aim of reducing the amount of information needed to identify a pathology, without compromising accuracy. As a result of these experiments, it can be concluded that it is feasible to detect pathologies using only 2 sensors placed on a leg.La identificación de patrones es una tecnología ampliamente conocida, la cual se emplea diariamente tanto para identificación como para autenticación. Algunos ejemplos de ello pueden ser la identificación biométrica (dactilar o facial), el reconocimiento de matrículas o el reconocimiento de voz. Sin embargo, cuando nos movemos al mundo del diagnóstico médico esto cambia sustancialmente. Este campo aplica muchas de las innovaciones y tecnologías recientes, pero es más difícil ver casos de reconocimiento de patrones aplicados al diagnóstico. Además, los casos donde se dan siempre están supervisados por un especialista y se realizan en ambientes controlados. Este comportamiento es algo esperado, ya que, en este campo, un falso negativo (no identificar la patología cuando esta existe) puede ser crítico y provocar consecuencias graves para el paciente. Esto se puede intentar paliar, configurando el algoritmo para que sea seguro frente a los falsos negativos, no obstante, esto aumentará la tasa de falsos positivos, lo cual puede aumentar el trabajo del especialista en el mejor de los casos o incluso puede provocar que se suministre un tratamiento a un paciente que no lo necesita. Esto hace que, en muchos casos sea necesaria la validación de la decisión del algoritmo por un especialista, sin embargo, pueden darse casos donde esta validación no sea tan esencial, o que se pueda tratar a esta primera identificación como una orientación de cara a ayudar al especialista. Con este objetivo en mente, esta tesis se centra en el desarrollo de un algoritmo para la identificación de patologías del tren inferior. Esta identificación se lleva a cabo mediante la forma de caminar de la gente (gait, en inglés). La forma de caminar de la gente difiere entre unas personas y otras, haciendo posible incluso la identificación biométrica mediante su uso. Sin embargo, esta también se ve afectada cuando se presenta una patología, tanto física como psíquica, que afecta a las personas. Esta alteración, genera un patrón común dependiendo del tipo de patología. No obstante, esta tesis se centra exclusivamente la identificación de patologías físicas. Otro aspecto importante en esta tesis es que los diferentes algoritmos se crean con la idea de la portabilidad en mente, evitando la obligación del usuario de realizar los paseos con excesivas restricciones (tanto de vestimenta como de localización). En primer lugar, se desarrollan diferentes algoritmos empleando diferentes configuraciones de teléfonos inteligentes para la adquisición de la base de datos. En concreto se usan configuraciones empleando 1, 2 y 4 teléfonos. Los teléfonos se colocan en las piernas empleando sujeciones especiales, de tal modo que no se puedan mover libremente. Una vez que se han capturado todos los paseos, el primer paso es filtrar las señales para eliminar el posible ruido que contengan. Seguidamente las señales se procesan para extraer los diferentes ciclos de la marcha (que corresponden a dos pasos) que componen los paseos. Una vez terminado el proceso de extracción de características, parte de estas se emplean para entrenar diferentes algoritmos de machine learning, los cuales luego son empleados para clasificar las restantes características. Sin embargo, las evidencias obtenidas a través de la realización de los experimentos con las diferentes configuración y algoritmos indican que no es viable realizar una identificación de patologías empleando teléfonos inteligentes. Principalmente esto se puede achacar a tres factores: la calidad de las señales capturadas por los teléfonos, la frecuencia de muestreo inestable y la falta de sincronía entre los teléfonos. Por otro lado, a raíz de los pobres resultados obtenidos empleado teléfonos inteligentes se cambia el dispositivo de captura a un sistema profesional de adquisición de movimiento. Además, se plantea crear dos tipos de algoritmo, uno basado en redes neuronales y otro basado en los algoritmos empleados anteriormente. Primeramente, se plantea la adquisición de una nueva base de datos. Para ellos se establece un procedimiento para facilitar la captura de los datos, los cuales se plantea han de ser en un entorno de libertad para el usuario. Una vez que se tienen todos los datos, el preprocesado que se realizar es similar al aplicado anteriormente. Las señales se filtran para eliminar el ruido y se extraen los diferentes ciclos de la marcha que componen los paseos. Sin embargo, como para el dispositivo de captura tenemos información de varios sensores y varias localizaciones, el lugar de emplear una frecuencia de corte común, empíricamente se establece una frecuencia de corte para cada señal y posición. Dado que ya tenemos los datos listos, se crea una red neuronal recurrente basada en la literatura, de este modo podemos tener una primera aproximación al problema. Vista la viabilidad de la red neuronal, se realizan diferentes experimentos con el objetivo de mejorar el rendimiento de esta. Finalmente, el otro algoritmo recoge el legado de lo visto en la primera parte de la tesis. Al igual que antes, este algoritmo se basa en la parametrización de los ciclos de la marcha, para su posterior utilización y emplea algoritmos basado en machine learning. A diferencia del uso de señales temporales, al parametrizar los ciclos, se pueden generar datos espurios. Para eliminar estos datos, el conjunto de datos se somete a una fase de preparación (limpieza y escalado). Una vez que se ha obtenido un conjunto de datos preparado, este se divide en dos, una parte se usa para entrenar los algoritmos, los cuales se emplean para clasificar las muestras restantes. Los resultados de estos experimentos validan la viabilidad de este algoritmo para la detección de patologías. A continuación, se realizan diferentes experimentos con el objetivo de reducir la cantidad de información necesaria para identificar una patología, sin perjudicar a la precisión. Resultado de estos experimentos, se puede concluir que es viable detectar patologías empleando únicamente 2 sensores colocados en una pierna.Programa de Doctorado en Ingeniería Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridPresidente: María del Carmen Sánchez Ávila.- Secretario: Mariano López García.- Vocal: Richard Matthew Gues