It was demonstrated in recent studies that Complementary Limb Motion Estimation (CLME) is robust approach for controlling active knee prostheses. A wearable sensor system is then needed to provide inputs to the controller in a real-time platform. In the present work, a wearable sensor system based on magnetic and inertial measurement units (MIMU) together with a simple calibration procedure were proposed. This sensor system was intended to substitute and extend the capabilities of a previous device based on potentiometers and gyroscopes. The proposed sensor system and calibration were validated with an Optical Tracking System (OTS) in a standard gait lab and first results showed that the proposed solution had a performance comparable to similar studies in the literature.

Background: Numerous multidimensional assessment tools have been developed to measure frailty; however, the clinical feasibility of these tools is limited. We previously developed and validated an upper-extremity function (UEF) assessment method that incorporates wearable motion sensors. The purpose of the current study was to: 1) cross-sectionally validate the UEF method in a larger sample in comparison with the Fried index; 2) develop a UEF frailty index to predict frailty categories including non-frail, pre-frail, and frail based on UEF parameters and demographic information, using the Fried index as the gold standard; and 3) develop a UEF continuous score (points scores for each UEF parameter and a total frailty score) based on UEF parameters and demographic information, using the Fried index as the gold standard. Methods: We performed a cross-sectional validation and index development study within the Banner Medical Center, Tucson, and Banner Sun Health Research Institute, Sun City, Arizona. Community-dwelling and outpatient older adults (>= 60 years; n = 352; 132 non-frail, 175 pre-frail, and 45 frail based on Fried criteria) were recruited. For the UEF test, each participant performed a 20-s elbow flexion, within which they repetitively and rapidly flexed and extended their dominant elbow. Using elbow motion outcomes two UEF indexes were developed (categorical and score). The Fried index was measured as the gold standard. Results: For the categorical index, speed of elbow flexion, elbow range of motion, elbow moment, number of flexion, speed variability and reduction within 20 s, as well as body mass index (BMI) were included as the pre-frailty/frailty predictor parameters. Results from 10-fold cross-validation showed receiver operator characteristic area under the curve of 0.77 +/- 0.07 and 0.80 +/- 0.12 for predicting Fried pre-frailty and frailty, respectively. UEF score (0.1 to 1.0) was developed using similar UEF parameters. Conclusions: We present an objective, sensor-based frailty assessment tool based on physical frailty features including slowness, weakness, exhaustion (muscle fatigue), and flexibility of upper-extremity movements. Within the current study, the method was validated cross-sectionally using the Fried index as the gold standard and the UEF categorical index and UEF frailty score were developed for research purposes and potentially for future clinical use.

abstract: Respiratory behavior provides effective information to characterize lung functionality, including respiratory rate, respiratory profile, and respiratory volume. Current methods have limited capabilities of continuous characterization of respiratory behavior and are primarily targeting the measurement of respiratory rate, which has relatively less value in clinical application. In this dissertation, a wireless wearable sensor on a paper substrate is developed to continuously characterize respiratory behavior and deliver clinically relevant parameters, contributing to asthma control. Based on the anatomical analysis and experimental results, the optimum site for the wireless wearable sensor is on the midway of the xiphoid process and the costal margin, corresponding to the abdomen-apposed rib cage. At the wearing site, the linear strain change during respiration is measured and converted to lung volume by the wireless wearable sensor utilizing a distance-elapsed ultrasound. An on-board low-power Bluetooth module transmits the temporal lung volume change to a smartphone, where a custom-programmed app computes to show the clinically relevant parameters, such as forced vital capacity (FVC) and forced expiratory volume delivered in the first second (FEV1) and the FEV1/FVC ratio. Enhanced by a simple, yet effective machine-learning algorithm, a system consisting of two wireless wearable sensors accurately extracts respiratory features and classifies the respiratory behavior within four postures among different subjects, demonstrating that the respiratory behaviors are individual- and posture-dependent contributing to monitoring the posture-related respiratory diseases. The continuous and accurate monitoring of respiratory behaviors can track the respiratory disorders and diseases' progression for timely and objective approaches for control and management. / Dissertation/Thesis / Doctoral Dissertation Electrical Engineering 2020

Observa-se nos tempos atuais um crescente interesse e demanda por novas tecnologias de sensoriamento e interação. A monitoração, com o objetivo de reconhecimento de movimentos humanos, permite oferecer serviços personalizados em diferentes áreas, dentre elas a área de cuidados médicos. Essa monitoração pode ser realizada por meio de diferentes técnicas como o uso de câmeras de vídeo, instrumentação do ambiente onde o indivíduo habita, ou pelo uso de dispositivos pessoais acoplados ao corpo. Os dispositivos acoplados ao corpo apresentam vantagens como baixo custo, uso confortável, além de muitas vezes serem despercebidos pelo usuário, diminuindo a sensação de invasão de privacidade durante a monitoração. Além disso, o dispositivo sensor pode ser facilmente acoplado ao corpo pelo próprio usuário, tornando o seu uso efetivo. Deste modo, este trabalho apresenta o desenvolvimento de um sistema que emprega técnicas de inteligência computacional e um acelerômetro facilmente acoplado ao punho do usuário para efetuar, de maneira confortável e não invasiva, o reconhecimento de movimentos básicos da rotina de uma pessoa. Aplicando máquinas de vetores de suporte para classificar os sinais e a razão discriminante de Fisher para efetuar a seleção das características mais significativas, o sistema apresentou uma taxa de sucesso em torno de 93% no reconhecimento de movimentos básicos efetuados por indivíduos monitorados. O sistema apresenta potencialidade para ser integrado a um hardware embarcado de baixo custo, responsável pelo gerenciamento da aquisição dos dados e pelo encaminhamento das informações a um sistema de monitoramento ou armazenamento. As informações providas por este sistema podem ser destinadas à promoção da saúde e bem estar do indivíduo, bem como utilizadas em diagnósticos ou monitoramento remoto de pacientes em um ambiente de vida assistida. / Nowadays it is observed a growing interest and demand for new sensing technologies and interaction. Monitoring with the objective of recognizing human movements, allows us to offer personalized services in different areas, among them healthcare. This monitoring can be performed through the use of different techniques such as the use of video cameras, living environment instrumentation, or the use of personal devices attached to the body, also known as wearable devices. These wearable devices have some advantages such as low cost, comfortable to use, and are often unnoticed by the user, reducing the feeling of privacy invasion during the monitoring. In addition, the sensing device can be easily attached to the body by the user itself, making its use effective. Thus, this work presents the development of a system that uses computational intelligence techniques and an accelerometer which is easily attached to the users wrist to perform, in a comfortable and non-invasive manner, the recognition of basic movements of a persons routine. By applying support vector machines to classify the signals and Fishers discriminant ratio to select the most significant features, the system has shown a success rate of 93% in the recognition of basic movements performed by monitored individuals. The system has the potential to be integrated into a low-cost embedded hardware, which is responsible for managing the data acquisition and routing the movement data to a remote monitoring system or storage. The information provided by the system can be designed to promote the health and wellness of the individual, as well used in diagnostics or remote patient monitoring in an ambient assisted living (AAL).

Article No.27

Observa-se nos tempos atuais um crescente interesse e demanda por novas tecnologias de sensoriamento e interação. A monitoração, com o objetivo de reconhecimento de movimentos humanos, permite oferecer serviços personalizados em diferentes áreas, dentre elas a área de cuidados médicos. Essa monitoração pode ser realizada por meio de diferentes técnicas como o uso de câmeras de vídeo, instrumentação do ambiente onde o indivíduo habita, ou pelo uso de dispositivos pessoais acoplados ao corpo. Os dispositivos acoplados ao corpo apresentam vantagens como baixo custo, uso confortável, além de muitas vezes serem despercebidos pelo usuário, diminuindo a sensação de invasão de privacidade durante a monitoração. Além disso, o dispositivo sensor pode ser facilmente acoplado ao corpo pelo próprio usuário, tornando o seu uso efetivo. Deste modo, este trabalho apresenta o desenvolvimento de um sistema que emprega técnicas de inteligência computacional e um acelerômetro facilmente acoplado ao punho do usuário para efetuar, de maneira confortável e não invasiva, o reconhecimento de movimentos básicos da rotina de uma pessoa. Aplicando máquinas de vetores de suporte para classificar os sinais e a razão discriminante de Fisher para efetuar a seleção das características mais significativas, o sistema apresentou uma taxa de sucesso em torno de 93% no reconhecimento de movimentos básicos efetuados por indivíduos monitorados. O sistema apresenta potencialidade para ser integrado a um hardware embarcado de baixo custo, responsável pelo gerenciamento da aquisição dos dados e pelo encaminhamento das informações a um sistema de monitoramento ou armazenamento. As informações providas por este sistema podem ser destinadas à promoção da saúde e bem estar do indivíduo, bem como utilizadas em diagnósticos ou monitoramento remoto de pacientes em um ambiente de vida assistida. / Nowadays it is observed a growing interest and demand for new sensing technologies and interaction. Monitoring with the objective of recognizing human movements, allows us to offer personalized services in different areas, among them healthcare. This monitoring can be performed through the use of different techniques such as the use of video cameras, living environment instrumentation, or the use of personal devices attached to the body, also known as wearable devices. These wearable devices have some advantages such as low cost, comfortable to use, and are often unnoticed by the user, reducing the feeling of privacy invasion during the monitoring. In addition, the sensing device can be easily attached to the body by the user itself, making its use effective. Thus, this work presents the development of a system that uses computational intelligence techniques and an accelerometer which is easily attached to the users wrist to perform, in a comfortable and non-invasive manner, the recognition of basic movements of a persons routine. By applying support vector machines to classify the signals and Fishers discriminant ratio to select the most significant features, the system has shown a success rate of 93% in the recognition of basic movements performed by monitored individuals. The system has the potential to be integrated into a low-cost embedded hardware, which is responsible for managing the data acquisition and routing the movement data to a remote monitoring system or storage. The information provided by the system can be designed to promote the health and wellness of the individual, as well used in diagnostics or remote patient monitoring in an ambient assisted living (AAL).

With the growing phenomenon of an aging population, an increasing numberof older people are living alone for domestic and social reasons. Based on thisfact, falling accidents become one of the most important factors in threateningthe lives of the elderly. Therefore, it is necessary to set up an application to de-tect the daily activities of the elderly. However, falling detection is difficult to recognize because the "falling" motion is an instantaneous motion and easy to confuse with others.In this thesis, three data mining methods were employed on wearable sensors' value; first which contains the continuous data set concerning eleven activities of daily living, and then an analysis of the different results was performed. Not only could the fall be detected, but other activities could also be classified. In detail, three methods including Back Propagation Neural Network, Support Vector Machine and Hidden Markov Model are applied separately to train the data set.What highlights the project is that a new  idea is put forward, the aim of which is to design a methodology of accurate classification in the time-series data set. The proposed approach, which includes obtaining of classifier parts and the application parts allows the generalization of classification. The preliminary results indicate that the new method achieves the high accuracy of classification,and significantly performs better than other data mining methods in this experiment.

Human knee kinematics, especially during gait, are an important analysis tool. The current "gold standard" for kinematics measurement is a multi-camera, marker-based motion capture system with 3D position tracking. These systems are accurate but expensive and their use is limited to a confined laboratory environment. High deflection strain gauges (HDSG) are a novel class of sensors that have the potential to measure kinematics and can be inexpensive, low profile, and are not limited to measurements within a calibrated volume. However, many HDSG sensors can have a non-linear and non-monotonic response. This thesis explores using a nanocomposite HDSG sensor system for measuring knee kinematics in walking gait and overcoming the non-monotonic sensor response found in HDSGs through advanced modeling techniques. Nanocomposite HDSG sensors were placed across the knee joint in nine subjects during walking gait at three speeds and three inclines. The piezoresistive response of the sensors was obtained by including the sensors in a simple electrical circuit and recorded using a low-cost microcontroller. The voltage response from the system was used in four models. The first two models included a physics-based log-normal model and statistical functional data analysis model that estimated continuous knee angles. The third model was a discrete linear regression model that estimated the inflection points on the knee flexion/extension cycle. Finally, a machine learning approach helped to predict subject speed and incline of the walking surface. The models showed the sensor has the capability to provide knee kinematic data to a degree of accuracy comparable to similar kinematic sensors. The log-normal model had a 0.45 r-squared and was unsuitable as a stand-alone continuous angle predictor. After running a 10-fold cross validation the functional data analysis (FDA) model had an overall RMSE of 3.4° and could be used to predict the entire knee flexion/extension angle cycle. The discrete linear regression model predicted the inflection points on the knee kinematics graph during each gait cycle with an average RMSE of 1.92° for angle measures and 0.0332 seconds for time measures. In every estimate, the discrete linear regression model performed better than the FDA model at those points. The 10-fold cross validation of the machine learning approach using the discrete voltages could predict the categorical incline 90% of the time and the RMSE for the speed model was 0.23 MPH. The use of a HDSG as a knee kinematics sensor was shown as a viable alternative to existing motion capture technology. In future work, it is recommended that a calibration method be developed that would allow this sensor to be used independent of a motion capture system. With these advancements, this inexpensive and low profile HDSG will advance understanding of human gait and kinematics in a more affordable and scope enhancing way.

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