1,411 research outputs found

    Incorporating Modular Arrangement of Predetermined Time Standard with a Wearable Sensing Glove

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    “Performance” – a common watchword in the present age, and that which is optimized through the most functional methodology of investigating the work procedure. This encompassed the auditing, updating of the tasks, while at the same time, applied automation and mechanization. The Modular Arrangement of Predetermined Time Standard (MODAPTS) is a useful application of a work measurement technique that allow a greater variety of work for manufacturing, engineering, and administrative service activities to be measured quickly with ease and accuracy. The MODAPTS, however, made it extremely difficult for engineers to use because it required an ample amount of time to analyze and code the raw data. A new design was proposed to help resolve the conventional system\u27s inadequacy because in MODAPTS, each task cycle of a minute required about 2 hours to calculate and document, and also, the judgment of the analysts varied for the same task. This study aimed to reduce the time taken for the traditional MODAPTS documentation usually took and produced unified results by integrating MODAPTS with a Sensing Wearable Glove while maintaining the same performance. The objective was to introduce an easy, cost-effective solution, and to compare the accuracy of coding between manual and automated calculated MODAPTS while maintaining consistent performance. This study discusses the glove and accompanying software design that detected movements using flex sensors, gyroscopes, microcontrollers, and pressure sensors. These movements were translated into analog data used to create MODAPTS codes as an output, which then sent the data wirelessly using the Bluetooth module. The device designed in this study is capable of sensing gestures for various operations, and the traditional method was compared to the proposed method. This was in turn, validated using the two-way ANOVA analysis. It was observed that the sensor-based glove provided efficient and reliable results, just like the traditional method results while maintaining the same performance

    Master of Science

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    thesisComputing and data acquisition have become an integral part of everyday life. From reading emails on cell phones to kids playing with motion sensing game consoles, we are surrounded with sensors and mobile computing devices. As the availability of powerful computing devices increases, applications in previously limited environments become possible. Training devices in rehabilitation are becoming increasingly common and more mobile. Community based rehabilitative devices are emerging that embrace these mobile advances. To further the flexibility of devices used in rehabilitation, research has explored the use of smartphones as a means to process data and provide feedback to the user. In combination with sensor embedded insoles, smartphones provide a powerful tool for the clinician in gathering data and as a standalone training tool in rehabilitation. This thesis presents the continuing research of sensor based insoles, feedback systems and increasing the capabilities of the Adaptive Real-Time Instrumentation System for Tread Imbalance Correction, or ARTISTIC, with the introduction of ARTISTIC 2.0. To increase the capabilities of the ARTISTIC an Inertial Measurement Unit (IMU) was added, which gave the system the ability to quantify the motion of the gait cycle and, more specifically, determine stride length. The number of sensors in the insole was increased from two to ten, as well as placing the microprocessor and a vibratory motor in the insole. The transmission box weight was reduced by over 50 percent and the volume by over 60 percent. Stride length was validated against a motion capture system and found the average stride length to be within 2.7 ± 6.9 percent. To continue the improvement of the ARTISTIC 2.0, future work will include implementing real-time stride length feedback

    Updates of Wearing Devices (WDs) In Healthcare, And Disease Monitoring

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     With the rising pervasiveness of growing populace, aging and chronic illnesses consistently rising medical services costs, the health care system is going through a crucial change from the conventional hospital focused system to an individual-focused system. Since the twentieth century, wearable sensors are becoming widespread in medical care and biomedical monitoring systems, engaging consistent estimation of biomarkers for checking of the diseased condition and wellbeing, clinical diagnostics and assessment in biological fluids like saliva, blood, and sweat. Recently, the improvements have been centered around electrochemical and optical biosensors, alongside advances with the non-invasive monitoring of biomarkers, bacteria and hormones, etc. Wearable devices have created with a mix of multiplexed biosensing, microfluidic testing and transport frameworks incorporated with flexible materials and body connections for additional created wear ability and effortlessness. These wearables hold guarantee and are fit for a higher understanding of the relationships between analyte focuses inside the blood or non-invasive biofluids and feedback to the patient, which is fundamentally significant in ideal finding, therapy, and control of diseases. In any case, cohort validation studies and execution assessment of wearable biosensors are expected to support their clinical acceptance. In the current review, we discussed the significance, highlights, types of wearables, difficulties and utilizations of wearable devices for biological fluids for the prevention of diseased conditions and real time monitoring of human wellbeing. In this, we sum up the different wearable devices that are developed for health care monitoring and their future potential has been discussed in detail

    Design and Development of a Comprehensive and Interactive Diabetic Parameter Monitoring System - BeticTrack

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    A novel, interactive Android app has been developed that monitors the health of type 2 diabetic patients in real-time, providing patients and their physicians with real-time feedback on all relevant parameters of diabetes. The app includes modules for recording carbohydrate intake and blood glucose; for reminding patients about the need to take medications on schedule; and for tracking physical activity, using movement data via Bluetooth from a pair of wearable insole devices. Two machine learning models were developed to detect seven physical activities: sitting, standing, walking, running, stair ascent, stair descent and use of elliptical trainers. The SVM and decision tree models produced an average accuracy of 85% for these seven activities. The decision tree model is implemented in an app that classifies human activity in real-time

    An Efficient Fusion Scheme for Human Hand Trajectory Reconstruction Using Inertial Measurement Unit and Kinect Camera

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    The turn of 21st century has witnessed an evolving trend in wearable devices research and improvements in human-computer interfaces. In such systems, position information of human hands in 3-D space has become extremely important as various applications require knowledge of user’s hand position. A promising example of which is a wearable ring that can naturally and ubiquitously reconstruct handwriting based on motion of human hand in an indoor environment. A common approach is to exploit the portability and affordability of commercially available inertial measurement units (IMU). However, these IMUs suffer from drift errors accumulated by double integration of acceleration readings. This process accrues intrinsic errors coming from sensor’s sensitivity, factory bias, thermal noise, etc., which result in large deviation from position’s ground truth over time. Other approaches utilize optical sensors for better position estimation, but these sensors suffer from occlusion and environment lighting conditions. In this thesis, we first present techniques to calibrate IMU, minimizing undesired effects of intrinsic imperfection resided within cheap MEMS sensors. We then introduce a Kalman filter-based fusion scheme incorporating data collected from IMU and Kinect camera, which is shown to overcome each sensor’s disadvantages and improve the overall quality of reconstructed trajectory of human hands

    Earables: Wearable Computing on the Ears

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    Kopfhörer haben sich bei Verbrauchern durchgesetzt, da sie private Audiokanäle anbieten, zum Beispiel zum Hören von Musik, zum Anschauen der neuesten Filme während dem Pendeln oder zum freihändigen Telefonieren. Dank diesem eindeutigen primären Einsatzzweck haben sich Kopfhörer im Vergleich zu anderen Wearables, wie zum Beispiel Smartglasses, bereits stärker durchgesetzt. In den letzten Jahren hat sich eine neue Klasse von Wearables herausgebildet, die als "Earables" bezeichnet werden. Diese Geräte sind so konzipiert, dass sie in oder um die Ohren getragen werden können. Sie enthalten verschiedene Sensoren, um die Funktionalität von Kopfhörern zu erweitern. Die räumliche Nähe von Earables zu wichtigen anatomischen Strukturen des menschlichen Körpers bietet eine ausgezeichnete Plattform für die Erfassung einer Vielzahl von Eigenschaften, Prozessen und Aktivitäten. Auch wenn im Bereich der Earables-Forschung bereits einige Fortschritte erzielt wurden, wird deren Potenzial aktuell nicht vollständig abgeschöpft. Ziel dieser Dissertation ist es daher, neue Einblicke in die Möglichkeiten von Earables zu geben, indem fortschrittliche Sensorikansätze erforscht werden, welche die Erkennung von bisher unzugänglichen Phänomenen ermöglichen. Durch die Einführung von neuartiger Hardware und Algorithmik zielt diese Dissertation darauf ab, die Grenzen des Erreichbaren im Bereich Earables zu verschieben und diese letztlich als vielseitige Sensorplattform zur Erweiterung menschlicher Fähigkeiten zu etablieren. Um eine fundierte Grundlage für die Dissertation zu schaffen, synthetisiert die vorliegende Arbeit den Stand der Technik im Bereich der ohr-basierten Sensorik und stellt eine einzigartig umfassende Taxonomie auf der Basis von 271 relevanten Publikationen vor. Durch die Verbindung von Low-Level-Sensor-Prinzipien mit Higher-Level-Phänomenen werden in der Dissertation anschließ-end Arbeiten aus verschiedenen Bereichen zusammengefasst, darunter (i) physiologische Überwachung und Gesundheit, (ii) Bewegung und Aktivität, (iii) Interaktion und (iv) Authentifizierung und Identifizierung. Diese Dissertation baut auf der bestehenden Forschung im Bereich der physiologischen Überwachung und Gesundheit mit Hilfe von Earables auf und stellt fortschrittliche Algorithmen, statistische Auswertungen und empirische Studien vor, um die Machbarkeit der Messung der Atemfrequenz und der Erkennung von Episoden erhöhter Hustenfrequenz durch den Einsatz von In-Ear-Beschleunigungsmessern und Gyroskopen zu demonstrieren. Diese neuartigen Sensorfunktionen unterstreichen das Potenzial von Earables, einen gesünderen Lebensstil zu fördern und eine proaktive Gesundheitsversorgung zu ermöglichen. Darüber hinaus wird in dieser Dissertation ein innovativer Eye-Tracking-Ansatz namens "earEOG" vorgestellt, welcher Aktivitätserkennung erleichtern soll. Durch die systematische Auswertung von Elektrodenpotentialen, die um die Ohren herum mittels eines modifizierten Kopfhörers gemessen werden, eröffnet diese Dissertation einen neuen Weg zur Messung der Blickrichtung. Dabei ist das Verfahren weniger aufdringlich und komfortabler als bisherige Ansätze. Darüber hinaus wird ein Regressionsmodell eingeführt, um absolute Änderungen des Blickwinkels auf der Grundlage von earEOG vorherzusagen. Diese Entwicklung eröffnet neue Möglichkeiten für Forschung, welche sich nahtlos in das tägliche Leben integrieren lässt und tiefere Einblicke in das menschliche Verhalten ermöglicht. Weiterhin zeigt diese Arbeit, wie sich die einzigarte Bauform von Earables mit Sensorik kombinieren lässt, um neuartige Phänomene zu erkennen. Um die Interaktionsmöglichkeiten von Earables zu verbessern, wird in dieser Dissertation eine diskrete Eingabetechnik namens "EarRumble" vorgestellt, die auf der freiwilligen Kontrolle des Tensor Tympani Muskels im Mittelohr beruht. Die Dissertation bietet Einblicke in die Verbreitung, die Benutzerfreundlichkeit und den Komfort von EarRumble, zusammen mit praktischen Anwendungen in zwei realen Szenarien. Der EarRumble-Ansatz erweitert das Ohr von einem rein rezeptiven Organ zu einem Organ, das nicht nur Signale empfangen, sondern auch Ausgangssignale erzeugen kann. Im Wesentlichen wird das Ohr als zusätzliches interaktives Medium eingesetzt, welches eine freihändige und augenfreie Kommunikation zwischen Mensch und Maschine ermöglicht. EarRumble stellt eine Interaktionstechnik vor, die von den Nutzern als "magisch und fast telepathisch" beschrieben wird, und zeigt ein erhebliches ungenutztes Potenzial im Bereich der Earables auf. Aufbauend auf den vorhergehenden Ergebnissen der verschiedenen Anwendungsbereiche und Forschungserkenntnisse mündet die Dissertation in einer offenen Hard- und Software-Plattform für Earables namens "OpenEarable". OpenEarable umfasst eine Reihe fortschrittlicher Sensorfunktionen, die für verschiedene ohrbasierte Forschungsanwendungen geeignet sind, und ist gleichzeitig einfach herzustellen. Hierdurch werden die Einstiegshürden in die ohrbasierte Sensorforschung gesenkt und OpenEarable trägt somit dazu bei, das gesamte Potenzial von Earables auszuschöpfen. Darüber hinaus trägt die Dissertation grundlegenden Designrichtlinien und Referenzarchitekturen für Earables bei. Durch diese Forschung schließt die Dissertation die Lücke zwischen der Grundlagenforschung zu ohrbasierten Sensoren und deren praktischem Einsatz in realen Szenarien. Zusammenfassend liefert die Dissertation neue Nutzungsszenarien, Algorithmen, Hardware-Prototypen, statistische Auswertungen, empirische Studien und Designrichtlinien, um das Feld des Earable Computing voranzutreiben. Darüber hinaus erweitert diese Dissertation den traditionellen Anwendungsbereich von Kopfhörern, indem sie die auf Audio fokussierten Geräte zu einer Plattform erweitert, welche eine Vielzahl fortschrittlicher Sensorfähigkeiten bietet, um Eigenschaften, Prozesse und Aktivitäten zu erfassen. Diese Neuausrichtung ermöglicht es Earables sich als bedeutende Wearable Kategorie zu etablieren, und die Vision von Earables als eine vielseitige Sensorenplattform zur Erweiterung der menschlichen Fähigkeiten wird somit zunehmend realer

    Design and Validation of a Portable Wireless Data Acquisition System for Measuring Human Joint Angles in Medical Applications

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    A prototype sensor system to capture and measure human joint movements in medical applications was developed. An algorithm that uses measurements from two IMU sensors to estimate the angle of one human joint was developed. Custom-made hardware and software were developed. Validation results showed 0.67° maximum error in static condition, 1.56° maximum RMSE for dynamic measurements and 2.5° average error during fast movements’ tests. The prototype has been successfully used by medical teams

    Affective Computing for Emotion Detection using Vision and Wearable Sensors

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    The research explores the opportunities, challenges, limitations, and presents advancements in computing that relates to, arises from, or deliberately influences emotions (Picard, 1997). The field is referred to as Affective Computing (AC) and is expected to play a major role in the engineering and development of computationally and cognitively intelligent systems, processors and applications in the future. Today the field of AC is bolstered by the emergence of multiple sources of affective data and is fuelled on by developments under various Internet of Things (IoTs) projects and the fusion potential of multiple sensory affective data streams. The core focus of this thesis involves investigation into whether the sensitivity and specificity (predictive performance) of AC, based on the fusion of multi-sensor data streams, is fit for purpose? Can such AC powered technologies and techniques truly deliver increasingly accurate emotion predictions of subjects in the real world? The thesis begins by presenting a number of research justifications and AC research questions that are used to formulate the original thesis hypothesis and thesis objectives. As part of the research conducted, a detailed state of the art investigations explored many aspects of AC from both a scientific and technological perspective. The complexity of AC as a multi-sensor, multi-modality, data fusion problem unfolded during the state of the art research and this ultimately led to novel thinking and origination in the form of the creation of an AC conceptualised architecture that will act as a practical and theoretical foundation for the engineering of future AC platforms and solutions. The AC conceptual architecture developed as a result of this research, was applied to the engineering of a series of software artifacts that were combined to create a prototypical AC multi-sensor platform known as the Emotion Fusion Server (EFS) to be used in the thesis hypothesis AC experimentation phases of the research. The thesis research used the EFS platform to conduct a detailed series of AC experiments to investigate if the fusion of multiple sensory sources of affective data from sensory devices can significantly increase the accuracy of emotion prediction by computationally intelligent means. The research involved conducting numerous controlled experiments along with the statistical analysis of the performance of sensors for the purposes of AC, the findings of which serve to assess the feasibility of AC in various domains and points to future directions for the AC field. The AC experiments data investigations conducted in relation to the thesis hypothesis used applied statistical methods and techniques, and the results, analytics and evaluations are presented throughout the two thesis research volumes. The thesis concludes by providing a detailed set of formal findings, conclusions and decisions in relation to the overarching research hypothesis on the sensitivity and specificity of the fusion of vision and wearables sensor modalities and offers foresights and guidance into the many problems, challenges and projections for the AC field into the future

    From Wearable Sensors to Smart Implants – Towards Pervasive and Personalised Healthcare

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    <p>Objective: This article discusses the evolution of pervasive healthcare from its inception for activity recognition using wearable sensors to the future of sensing implant deployment and data processing. Methods: We provide an overview of some of the past milestones and recent developments, categorised into different generations of pervasive sensing applications for health monitoring. This is followed by a review on recent technological advances that have allowed unobtrusive continuous sensing combined with diverse technologies to reshape the clinical workflow for both acute and chronic disease management. We discuss the opportunities of pervasive health monitoring through data linkages with other health informatics systems including the mining of health records, clinical trial databases, multi-omics data integration and social media. Conclusion: Technical advances have supported the evolution of the pervasive health paradigm towards preventative, predictive, personalised and participatory medicine. Significance: The sensing technologies discussed in this paper and their future evolution will play a key role in realising the goal of sustainable healthcare systems.</p> <p> </p
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