Combine Shapelets

Sensor-based human activity recognition has become an important research field within pervasive and ubiquitous computing. Techniques for recognizing atomic activities such as gestures or actions are mature for now, but complex activity recognition still remains a challenging issue. I was a candidate in an activity classification thesis. It collected 4 activities, which included walking on the sidewalk for a set distance, walking up and down a set of stairs, walking on the treadmill at 2.5 mph for 2 minutes, and jogging on the treadmill at 5.5 mph for 1 minute. It took 30 minutes to collect one candidate data. If complex activity data can be made up with atomic activities data, the data collecting process will be simplified. In this thesis, I used methods to mimic a complex activity shapelet by combing atomic activity shapelets. I first collect two candidates walk, jump and skip time series data, in which walk and jump are considered the atomic activities of skip. Time series patterns, shapelets, are extracted using tsshapelet package. Shapelets are small sub-series, or parts of the time-series, that are informative or discriminative for a certain class. They can be used to transform the time-series to features by calculating the distance for each of the time-series you want to classify to a shapelet. In order to create skip representative shapelet, Barycenter Dynamic Time Warping and Weighted Dynamic Time Warping are used to average walk and jump shapelet, and then compare the euclidean distance between skip shapelet with walk shapelet, jump shapelet and, combined-shapelet. Experimental result show that the combined-shapelet is closer to skip shapelet than single walk or jump shapelet. Then I use three evaluation methods to mathematically and statistically show that combined-shapelet and real skip shapelet are similar. Evaluation methods include sliding window, cycle comparison and random comparison. To verify whether combined-shapelet can substitute real skip shapelet, a new labeled time series data is introduced, the result shows that both shapelets have the label accuracy around 70%, accuracy difference is less than 1%

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

Biometric walk recognizer. Research and results on wearable sensor-based gait recognition

Gait is a biometric trait that can allow user authentication, though being classified as a "soft" one due to a certain lack in permanence, and to sensibility to specific conditions. The earliest research relies on computer vision-based approaches, especially applied in video surveillance. More recently, the spread of wearable sensors, especially those embedded in mobile devices, which are able to capture the dynamics of the walking pattern through simpler 1D signals, has spurred a different research line. This capture modality can avoid some problems related to computer vision-based techniques, but suffers from specific limitations. Related research is still in a less advanced phase with respect to other biometric traits. However, the promising results achieved so far, the increasing accuracy of sensors, the ubiquitous presence of mobile devices, and the low cost of related techniques, make this biometrics attractive and suggest to continue the investigations in this field. The first Chapters of this thesis deal with an introduction to biometrics, and more specifically to gait trait. A comprehensive review of technologies, approaches and strategies exploited by gait recognition proposals in the state-of-the-art is also provided. After such introduction, the contributions of this work are presented in details. Summarizing, it improves preceding result achieved during my Master Degree in Computer Science course of Biometrics and extended in my following Master Degree Thesis. The research deals with different strategies, including preprocessing and recognition techniques, applied to the gait biometrics, in order to allow both an automatic recognition and an improvement of the system accuracy

Mobile depth sensing technology and algorithms with application to occupational therapy healthcare

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThe UK government is striving to shift its current healthcare delivery model from clini-cian–oriented services, to that of patient and self–care–oriented intervention strategies. It seeks to do so through Information Communication (ICT) and Computer Mediated Re-ality Technologies (CMRT) as a key strategy to overcome the ever–increasing scarcity of healthcare resources and costs. To this end, in the UK the use of paper–based information systems have exhibited their limitations in providing apposite care. At the national level, The Royal College of Occupational Therapists (RCOT) identify home visits and modifica-tions as key levers in a multifactorial health programme to evaluate interventions for older people with a history of falling or are identified as being prone to falling. Prescribing Assistive Equipment (AE) is one such mechanism that seeks to reduce the risk of falling whilst promoting the continued independence of physical dexterity and mobility in older adults at home. In the UK, the yearly cost of falls is estimated at £2.3 billion. Further evidence places a 30% to 60% abandonment rate on prescribed AE by and large due to a ‘poor fit’ and measurement inaccuracies. To remain aligned with the national strategy, and assist in the eradication of measurement inaccuracies, this thesis employs Mobile Depth Sensing and Motion Track-ing Devices (MDSMTDs) to assist OTs in in the process of digitally measuring the extrin-sic fall–risk factors for the provision of AE. The quintessential component in this assess-ment lies in the measurement of fittings and furniture items in the home. To digitise and aid in this process, the artefact presented in this thesis employs stereo computer–vision and camera calibration algorithms to extract edges in 3D space. It modifies the Sobel–Feldman convolution filter by reducing the magnitude response and employs the camera intrinsic parameters as a mechanism to calculate the distortion matrix for interpolation between the edges and the 3D point cloud. Further Augmented Reality User Experience (AR-UX) facets are provided to digitise current state of the art clinical guidance and over-lay its instructions onto the real world (i.e., 3D space). Empirical mixed methods assessment revealed that in terms of accuracy, the arte-fact exhibited enhanced performance gains over current paper–based guidance. In terms of accuracy consistency, the artefact can rectify measurement consistency inaccuracies, but there are still a wide range of factors that can influence the integrity of the point-cloud in respect of the device’s point-of-view, holding positions and measurement speed. To this end, OTs usability, and adoption preferences materialise in favour of the artefact. In conclusion, this thesis demonstrates that MDSMTDs are a promising alterna-tive to existing paper–based measurement practices as OTs appear to prefer the digital–based system and that they can take measurements more efficiently and accurately

Smartphone Gait Authentication: Recognizing Smartphone Users based on their Gait

Gait recognition using smartphone motion sensors such as accelerometers and gyroscopes is relatively underdeveloped compared to those using machine vision. This project explored the various state of the art neural networks-based approaches for accelerometer and gyroscope-based gait analysis and evaluated them. CNN and LSTM neural networks architectures proposed in prior work are replicated to achieve similar results on a gait dataset gathered in the wild. Prior work focused deep learning models for gait recognition on data gathered in controlled user studies

A Unified Local–Global Feature Extraction Network for Human Gait Recognition Using Smartphone Sensors

Smartphone-based gait recognition has been considered a unique and promising technique for biometric-based identification. It is integrated with multiple sensors to collect inertial data while a person walks. However, captured data may be affected by several covariate factors due to variations of gait sequences such as holding loads, wearing types, shoe types, etc. Recent gait recognition approaches either work on global or local features, causing failure to handle these covariate-based features. To address these issues, a novel weighted multi-scale CNN (WMsCNN) architecture is designed to extract local to global features for boosting recognition accuracy. Specifically, a weight update sub-network (Ws) is proposed to increase or reduce the weights of features concerning their contribution to the final classification task. Thus, the sensitivity of these features toward the covariate factors decreases using the weight updated technique. Later, these features are fed to a fusion module used to produce global features for the overall classification. Extensive experiments have been conducted on four different benchmark datasets, and the demonstrated results of the proposed model are superior to other state-of-the-art deep learning approaches

Improved class binarization model with data oversampling in gait recognition

Gait is a process of a complete cycle of walking that consist of two-step cycles. It can be said that gait has a high degree of biometric which means that every person has its own unique style of walking. Gait recognition using smartphone accelerometer has been widely used in many research and applications due to the cheap assembly, durability and reliability of the Inertial Measurement Unit (IMU) Microelectromechanical System (MEMS) technology. Gait recognition has been used in many areas such as biomechanics, neuro-rehabilitation, sports medicine, security and many others. Latest research achievement in gait recognition approach is the ability to sufficiently recognize a person with small variations and single data enrolment. In the standard gait recognition, there are four main workflows or levels that include data acquisition, pre-processing, features extraction and classification. However, most of the current research is concentrated on the data acquisition and features extractions with a minimal concentration on other workflows, hence the best accuracy is not fully achieved and optimized. In this thesis, we found several problems at the data acquisition stage, pre-processing stage, and classification stage. At the data acquisition stage, gait data is obtained from predefined places such as pocket, pouch, trousers and other parts of the body. However, due to the limitation of the clothes and culture, the mentioned places may not be suitable for smartphone placement. At the pre-processing stage, linear interpolation is widely used by researchers in order to create a fix sampling rate between data points. However, they never examine the best interpolation rate for usage as the rate affects the number of data and this would significantly affect the overall accuracy. At the classification stage, there are two problems that were observed. The first problem is the single classifier mapping applied by the current researchers which are not suitable because the gait recognition involved many classes and possible of overlapped classes boundary is high, hence multiclass classification or binarization of classes should be adopted. However, some researcher does apply one-vs-all (OVA) and one-vs-one (OVO) multiclass methods but the classes are not widely spread and it is not well distributed among class comparison. The second problem in the classification stage is the imbalance class when binarization dataset is performed after the multiclass classification mapping is applied. To overcome the problems mentioned above, we proposed new methods to tackle the problems at the mentioned stages. At the data acquisition stage, we proposed a method that uses hand as the position of the smartphone. At the pre-processing stage, Linear Interpolation Factor Determinator (LIFD) is proposed by using decision tree and cross-validation evaluation in-order to determine the best linear interpolation rate. At the classification stage, we proposed the used of Random Correction Code (RCC) as the main multiclass classifier mapping. RCC is an extension of Error-correcting Output Code (ECOC) that is used for multiclass classification. To tackle the imbalance class problem, a new oversampling method, Self-adjusted Synthetic Minority Over-sampling Technique (SA-SMOTE) is proposed to automatically assign number of samples on the minority class without human intervention. For the experimentation, gait data using hands (HHScD) is collected from 30 subjects with three different poses. Then it is investigated whether it is viable for the gait recognition process. The dataset was compared with the largest gait database from Osaka University (OU-ISIR-2) which the data was captured from smartphone clipped to the waist belt from 408 subjects. Then our proposed methods was applied to the dataset and comparison with the existing method was evaluated. Our experimental results show improvements of the accuracy in comparison with the previous study