Esta pesquisa busca delinear o contexto histórico e teórico sobre as tecnologias vestíveis de moda, enfatizando suas criações por meio da plataforma Arduino em mídia digital. Priorizam-se as possíveis relações com a reconfiguração dos espaços urbanos devidos à coexistência física dos lugares com o recente território informacional. Abrange-se, também, uma perspectiva sobre a relação corpo e tecnologia, na qual este último foi, e ainda é, essencial na gestão dos indivíduos nas grandes cidades. Por meio deste trabalho, acredita-se tanto na possibilidade de fomentar uma nova área de saber de moda quanto na reflexão de uma nova imagem de subjetividade que considere, simetricamente, a atuação de humanos e não-humanos, assim como as instâncias físicas e virtuais / This research aims to delineate the historical and theoretical context about fashionable wearable technology, emphasizing their creations through the Arduino platform in digital media. Possible relations with the reconfiguration of urban spaces due to physical coexistence of the places with the recent informational territory are given priority. It covers is also a perspective on the relationship body and technology, in which the latter was and still is essential in the management of individuals in large cities. Through this work, it is believed either the possibility of fostering a new area of knowledge of fashion, as the reflection of a new image of subjectivity to consider symmetrically the performance of humans and non-humans, as well as the physical and virtual instances

Article No.27

Thesis (Ph. D.)--University of Oregon, 2002. / Typescript. Includes vita and abstract. Includes bibliographical references (leaves 242-256). Also available for download via the World Wide Web; free to University of Oregon users.

The occurrence of total knee replacements is increasing in the United States for persons over the age of 45 because they are inexpensive and a very effective method for treating degenerative joint diseases. Rehabilitation requires regular access to a wide variety of resources and personnel and, as the demand for post-operative, rehabilitative care increases, the ability to marginally relieve the healthcare system by offloading resources to the patient is necessary. Tools to enable tracking a patient’s rehabilitative progress at home are an essential method to help unload the healthcare system. The purpose of this project is to design and develop a wearable home rehabilitation device for knee replacement. This thesis utilizes design ethnography tools such as expert interviews, rehabilitation observation, a participatory design workshop, iterative development, and an idea feedback study. Leveraging advancements in technology and the field of eTextiles, this study investigates the product feasibility and acceptance of discreet on-body sensors to provide a product that enables patients to better perform rehabilitation on their own, but also to allow for a feedback loop for physicians and therapists to view patient progress.

Esta pesquisa busca delinear o contexto histórico e teórico sobre as tecnologias vestíveis de moda, enfatizando suas criações por meio da plataforma Arduino em mídia digital. Priorizam-se as possíveis relações com a reconfiguração dos espaços urbanos devidos à coexistência física dos lugares com o recente território informacional. Abrange-se, também, uma perspectiva sobre a relação corpo e tecnologia, na qual este último foi, e ainda é, essencial na gestão dos indivíduos nas grandes cidades. Por meio deste trabalho, acredita-se tanto na possibilidade de fomentar uma nova área de saber de moda quanto na reflexão de uma nova imagem de subjetividade que considere, simetricamente, a atuação de humanos e não-humanos, assim como as instâncias físicas e virtuais / This research aims to delineate the historical and theoretical context about fashionable wearable technology, emphasizing their creations through the Arduino platform in digital media. Possible relations with the reconfiguration of urban spaces due to physical coexistence of the places with the recent informational territory are given priority. It covers is also a perspective on the relationship body and technology, in which the latter was and still is essential in the management of individuals in large cities. Through this work, it is believed either the possibility of fostering a new area of knowledge of fashion, as the reflection of a new image of subjectivity to consider symmetrically the performance of humans and non-humans, as well as the physical and virtual instances

The goal of this master's thesis is to describe the possibilites of devices with operating system Android Wear, there is a description of Android Wear API and components, which are nowadays widely used in smart wearable devices. The thesis contains a description of recognition of dynamic gestures with the use of machine learning methods applied on data, which are provided by a smart device. In the practical part of this master's thesis is described an implemented library, which allows to train gestures and recognize them using FastDTW algorithm and inform a connected device about the recognized movement. Use of the library is shown on a demo application.

The Quantified Self movement has led to an increased routine use of consumer wearables, generating large amounts of person-generated wearable device data. This has become an opportunity to researchers to conduct research with large-scale person-generated wearable device data without having to collect data in a costly and time-consuming way. However, there are known challenges of wearable device data such as missing data or inaccurate data which raises the need to assess the quality of data before conducting research. Currently, there is a lack of in-depth understanding on data quality challenges of using person-generated wearable device data for research purposes, and how data quality assessment should be conducted. Data quality assessment could be especially a burden to those without the domain knowledge on a specific data type, which might be the case for emerging biomedical data sources. The goal of this dissertation is to advance the knowledge on data quality challenges and assessment of person-generated wearable device data and facilitate data quality assessment for those without the domain knowledge on the emerging data type. The dissertation consists of two aims: (1) identifying data quality dimensions important for assessing the quality of person-generated wearable device data for research purposes, (2) designing and evaluating an interactive data quality characterization tool that supports researchers in assessing the fitness-for-use of fitness tracker data. In the first aim, a multi-method approach was taken, conducting literature review, survey, and focus group discussion sessions. We found that intrinsic data quality dimensions applicable to electronic health record data such as conformance, completeness, and plausibility are applicable to person-generated wearable device data. In addition, contextual/fitness-for-use dimensions such as breadth and density completeness, and temporal data granularity were identified given the fact that our focus was on assessing data quality for research purposes. In the second aim, we followed an iterative design process from understanding informational needs to designing a prototype, and evaluating the usability of the final version of a tool. The tool allows users to customize the definition of data completeness (fitness-for-use measures), and provides data summarization on the cohort that meets that definition. We found that the interactive tool that incorporates fitness-for-use measures and allows customization on data completeness, can support assessing fitness-for-use assessment more accurately and in less time than a tool that only presents information on intrinsic data quality measures.

Human activity recognition based on wearable sensors’ data is quite an attractive subject due to its wide application in the fields of healthcare, wellbeing and smart environments. This research is also focussed on predictive performance comparison of machine learning algorithms for activity recognition from wearable sensors’ (MHEALTH) data while employing a comprehensive process. The framework is adapted from well-laid data science practices which addressed the data analyses requirements quite successfully. Moreover, an Analysis Tool is also developed to support this work and to make it repeatable for further work. A detailed comparative analysis is presented for five multi-class classifier algorithms on MHEALTH dataset namely, Linear Discriminant Analysis (LDA), Classification and Regression Trees (CART), Support Vector Machines (SVM), K-Nearest Neighbours (KNN) and Random Forests (RF). Beside using original MHEALTH data as input, reduced dimensionality subsets and reduced features subsets were also analysed. The comparison is made on overall accuracies, class-wise sensitivity and specificity of each algorithm, class-wise detection rate and detection prevalence in comparison to prevalence of each class, positive and negative predictive values etc. The resultant statistics have also been compared through visualizations for ease of understanding and inference. All five ML algorithms were applied for classification using the three sets of input data. Out of all five, three performed exceptionally well (SVM, KNN, RF) where RF was best with an overall accuracy of 99.9%. Although CART did not perform well as a classification algorithm, however, using it for ranking inputs was a better way of feature selection. The significant sensors using CART ranking were found to be accelerometers and gyroscopes; also confirmed through application of predictive ML algorithms. In dimensionality reduction, the subset data based on CART-selected features yielded better classification than the subset obtained from PCA technique.

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The objective of this study was to assess the biomechanical and subjective measures of elementary school teachers while wearing active and/or passive wearable devices during the average workday. Five elementary school teachers wore a harness that held an Upright GO 2 posture tracking device and a Vicon Blue Trident sensor on the participant's upper back for two school days. Haptic feedback was on for one day and off for the other. Data from the Vicon wearable was analyzed to determine participants’ trunk flexion severity, frequency, and duration. Surveys were used to determine perceived exertion and perception of wearable technology. This study proved that teachers are undergoing severe trunk flexion throughout the day; however, there was not consistent improvement in trunk flexion when haptic feedback was applied. Results also indicated that perceived exertion levels of teachers did not always correlate to the frequency of trunk flexion measured through the wearable device.