8,404 research outputs found

    The development and application of an opto-electronic technique for analysis of body movements

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    The relative merits of the most commonly used techniques for analysis of human movement are briefly outlined. The performance characteristics of the ideal instrumentation for recording movement are listed. As a consequence of these considerations, an opto-electronic technique which makes use of polarised light is developed for measuring angular orientation of limb and body segments. This technique is used to study patterns of upper arm abduction during a simple lifting task, and to study the relationship between, arm and trunk movements during a task which requires reaching to various distances on a table surface. The merits of the opto-electronic technique are discussed in the light of the experience gained from the experimental applications, and recommendations are made for the future development of the instrumentation

    Automatic Scaffolding Productivity Measurement through Deep Learning

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    This study developed a method to automatically measure scaffolding productivity by extracting and analysing semantic information from onsite vision data

    Exploring and developing methods of assessing sedentary behaviour in children

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    Evidence suggests that sedentary behaviour (SB) is associated with adverse health outcomes. Children’s SB is a complex set of behaviours that includes different types of activities taking place in a variety of settings. Therefore, assessing children’s SB is challenging and currently no single method exists that captures the behaviour as a whole. This thesis aims to explore and develop new and existing methods of assessing children’s SB, by employing a range of quantitative and qualitative methods. Accelerometry has become a widely used method of estimating sedentary time (ST). Study 1 identified raw acceleration thresholds to classify children’s sedentary and stationary behaviours, using two accelerometer brands across three placements. Thresholds however, do not account for the postural element of SB, as per its definition. Study 2 validated the Sedentary Sphere method in children, allowing for the most likely posture classification from wrist-worn accelerometers. Study 3 added contextual information to accelerometer data by using a digitalised data capturing tool, the Digitising Children’s Data Collection (DCDC) for Health application (app). Children used the app to report their SBs daily through photos, drawings, voice recordings as well as answering a multiple-choice questionnaire. Results from the DCDC app identified specific SBs to be targeted in future interventions. Data showed distinct differences between boys and girls’ screen-based behaviours, suggesting gender-specific interventions are needed to reduce screen time. Using the DCDC app in combination with accelerometry often explained patterns of SB and physical activity observed in accelerometer data. Study 4 added information about parents’ perceptions of the factors that influence their children’s SBs. This study identified parents/carers as a target for future interventions in view of perceptions reported about PA and SB and their need for support to help reduce the time children spend using screen-based devices

    Non-invasive Electronic Biosensor Circuits and Systems

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    An aging population has lead to increased demand for health-care and an interest in moving health care services from the hospital to the home to reduce the burden on society. One enabling technology is comfortable monitoring and sensing of bio-signals. Sensors can be embedded in objects that people interact with daily such as a computer, chair, bed, toilet, car, telephone or any portable personal electronic device. Moreover, the relatively recent and wide availability of microelectronics that provide the capabilities of embedded software, open access wireless protocols and long battery life has led many research groups to develop wearable, wireless bio-sensor systems that are worn on the body and integrated into clothing. These systems are capable of interaction with other devices that are nowadays commonly in our possession such as a mobile phone, laptop, PDA or smart multifunctional MP3 player. The development of systems for wireless bio-medical long term monitoring is leading to personal monitoring, not just for medical reasons, but also for enhancing personal awareness and monitoring self-performance, as with sports-monitoring for athletes. These developments also provide a foundation for the Brain Computer Interface (BCI) that aims to directly monitor brain signals in order to control or manipulate external objects. This provides a new communication channel to the brain that does not require activation of muscles and nerves. This innovative and exciting research field is in need of reliable and easy to use long term recording systems (EEG). In particular we highlight the development and broad applications of our own circuits for wearable bio-potential sensor systems enabled by the use of an amplifier circuit with sufficiently high impedance to allow the use of passive dry electrodes which overcome the significant barrier of gel based contacts

    Reconnaissance de l'Ă©motion thermique

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    Pour améliorer les interactions homme-ordinateur dans les domaines de la santé, de l'e-learning et des jeux vidéos, de nombreux chercheurs ont étudié la reconnaissance des émotions à partir des signaux de texte, de parole, d'expression faciale, de détection d'émotion ou d'électroencéphalographie (EEG). Parmi eux, la reconnaissance d'émotion à l'aide d'EEG a permis une précision satisfaisante. Cependant, le fait d'utiliser des dispositifs d'électroencéphalographie limite la gamme des mouvements de l'utilisateur. Une méthode non envahissante est donc nécessaire pour faciliter la détection des émotions et ses applications. C'est pourquoi nous avons proposé d'utiliser une caméra thermique pour capturer les changements de température de la peau, puis appliquer des algorithmes d'apprentissage machine pour classer les changements d'émotion en conséquence. Cette thèse contient deux études sur la détection d'émotion thermique avec la comparaison de la détection d'émotion basée sur EEG. L'un était de découvrir les profils de détection émotionnelle thermique en comparaison avec la technologie de détection d'émotion basée sur EEG; L'autre était de construire une application avec des algorithmes d'apprentissage en machine profonds pour visualiser la précision et la performance de la détection d'émotion thermique et basée sur EEG. Dans la première recherche, nous avons appliqué HMM dans la reconnaissance de l'émotion thermique, et après avoir comparé à la détection de l'émotion basée sur EEG, nous avons identifié les caractéristiques liées à l'émotion de la température de la peau en termes d'intensité et de rapidité. Dans la deuxième recherche, nous avons mis en place une application de détection d'émotion qui supporte à la fois la détection d'émotion thermique et la détection d'émotion basée sur EEG en appliquant les méthodes d'apprentissage par machine profondes - Réseau Neuronal Convolutif (CNN) et Mémoire à long court-terme (LSTM). La précision de la détection d'émotion basée sur l'image thermique a atteint 52,59% et la précision de la détection basée sur l'EEG a atteint 67,05%. Dans une autre étude, nous allons faire plus de recherches sur l'ajustement des algorithmes d'apprentissage machine pour améliorer la précision de détection d'émotion thermique.To improve computer-human interactions in the areas of healthcare, e-learning and video games, many researchers have studied on recognizing emotions from text, speech, facial expressions, emotion detection, or electroencephalography (EEG) signals. Among them, emotion recognition using EEG has achieved satisfying accuracy. However, wearing electroencephalography devices limits the range of user movement, thus a noninvasive method is required to facilitate the emotion detection and its applications. That’s why we proposed using thermal camera to capture the skin temperature changes and then applying machine learning algorithms to classify emotion changes accordingly. This thesis contains two studies on thermal emotion detection with the comparison of EEG-base emotion detection. One was to find out the thermal emotional detection profiles comparing with EEG-based emotion detection technology; the other was to implement an application with deep machine learning algorithms to visually display both thermal and EEG based emotion detection accuracy and performance. In the first research, we applied HMM in thermal emotion recognition, and after comparing with EEG-base emotion detection, we identified skin temperature emotion-related features in terms of intensity and rapidity. In the second research, we implemented an emotion detection application supporting both thermal emotion detection and EEG-based emotion detection with applying the deep machine learning methods – Convolutional Neutral Network (CNN) and LSTM (Long- Short Term Memory). The accuracy of thermal image based emotion detection achieved 52.59% and the accuracy of EEG based detection achieved 67.05%. In further study, we will do more research on adjusting machine learning algorithms to improve the thermal emotion detection precision

    A Model To Integrate Sustainability Into The User-centered Design Process

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    With concerns for the environment becoming more prevalent in business and the government, it is increasingly important to re-evaluate and update processes to include sustainability considerations early in the design process. In response to this charge, this research effort was designed to integrate sustainability factors into the usercentered design process. The results of this research highlight the benefits of sustainability requirement planning, as well as those derived from integrating sustainability into the current user-centered design mode

    STATIC SHOULDER ELEVATION WITH OR WITHOUT LIMITED RANGE OF ARM MOTION

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    Purpose: The purpose of this study was to investigate the effect of two similar low force, upper arm holding tasks on shoulder muscle fatigue. One task involves static holding, while the comparison task involves limited up and down motions, dynamic rolling. Both tasks require the shoulder muscles to hold the combined weight of the arm, hand, and a roller brush in a manner similar to that of a painter standing on a portable ladder painting a wall. Methods: Twenty volunteer participants from undergraduate classes performed two similar tasks. One was holding their dominant arm above their shoulder while holding a roller paint brush against a target (static holding). The other tasks was similar except the participant moved the roller brush up and down on a defined target (dynamic rolling). During each task, participants wore surface electromyography sensors placed on anterior, medial, and posterior deltoids, and on the triceps muscle of their dominant arm in the direction of the muscle fibers. For each participant, the maximum voluntary contraction of each muscle was assessed and normalized to their muscle activity for a static holding and dynamic rolling task. Muscle fatigue was assessed throughout the task by performing a median frequency analysis on the muscle activity data. Discomfort ratings were measured verbally over the task period on a 0–100 scale. The task was performed up to a rating of 80, indication of extreme discomfort. Results: Analyses based on of median frequency recordings showed no significant difference in the rate of fatigue development between all four muscles. Rate of fatigue was also not significantly different between static holding and dynamic rolling tasks. Static holding and dynamic rolling endurance times were significantly different from one another (p = 0.0066). Analyses based on discomfort ratings showed static holding had a maximum endurance time of 9.5 minutes and dynamic rolling had a maximum endurance time of 6.5 minutes. Endurance times were also compared to 20 minutes—a time mentioned in the notes of the Upper Limb Localized Fatigue Guidelines of the American Conference of Governmental Industrial Hygienist saying static exertions of the upper limbs “would not be expected to exceed 20 minutes.” There was a significant difference in static holding and dynamic rolling endurance times against a 20-minute note (p = 0.000). Conclusion: The results demonstrated that shoulder muscle fatigue and discomfort were present during the tasks. The endurance times differed between static holding and dynamic rolling tasks. The endurance times never exceeded 20-minutes, thereby supporting the comment within the Upper Limb Localized Fatigue Guidelines
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