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A Numerical Vortex Approach To Aerodynamic Modeling of SUAV/VTOL AircraftHunsaker, Douglas F. 02 January 2007 (has links) (PDF)
A combined wing and propeller model is presented as a low-cost approach to preliminary modeling of slipstream effects on a finite wing. The wing aerodynamic model employs a numerical lifting-line method utilizing the 3D vortex lifting law along with known 2D airfoil data to predict the lift distribution across a wing for a prescribed upstream flowfield. The propeller/slipstream model uses blade element theory combined with momentum conservation equations. This model is expected to be of significant importance in the design of tail-sitter vertical take-off and landing (VTOL) aircraft, where the propeller slipstream is the primary source of air flow past the wings in some flight conditions. The algorithm is presented, and results compared with published experimental data.
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Exploring Baxter Robot and Development of Python algorithms to Execute Holding, Lifting and Positioning TasksAndersson, Rabé January 2019 (has links)
The greatest feature of using a Baxter robot among other industrial robots is the ability to train this robot conveniently. The training of the robot could be done within a few minutes and it does not need so much knowledge of programming. However, this type of training feature is limited in functionality and needs frequent updating of the software and the license from the manufactural company. As the manufacturer of Baxter Robot no longer exists due to a merger, the thesis has twofold aims, (1) Exploring different functional, installation, calibration, troubleshooting and hardware features of the Baxter robot and (2) demonstrate the functionality of Baxter to perform general tasks of holding, lifting and moving of test objects from one desired position to another position using custom-made programs in Python. Owing to this, details about different software and hardware aspects of Baxter robot is presented in this thesis. Additionally, systematic laboratory tutorials are also presented in appendices for students who want to learn and operate the robot from simple to complicated tasks. In order to keep the Baxter operational for students and researchers in future, when there is no more help available from its manufacturer, this thesis endeavour to cover all these aspects. Thus, the thesis presents a brief understanding of how to use the Baxter Robot in a simple and efficient way to perform a basic industrial task. The kinematics part will show the concepts of forward and inverse kinematics and the DH (the Denavit–Hartenberg) parameters that are important to understand the end-effector position according to the world frame that will give the knowledge of those who are interested in the kinematics part of Baxter robot. The work of the thesis will make it easier to understand how to program a Baxter robot by using Python language and using the simplest way to move the arm to certain positions. The ROS principles, kinematics and Python language programs will provide a good platform to understand the usability of Baxter robot. Furthermore, easy to use laboratory tutorials are devised and are presented in the appendices. These laboratory tutorials will improve the understanding of the readers and provide a step-by-step guide of operating Baxter robot according to the principles of Robotics. In addition to all these points above, the thesis shows useful functions that are built in ROS (Robot Operating System) that make it easier to program the robot in an untraditional way which is one of a contribution of this thesis itself. The usual way to program the robots, in general, is to study the robot kinematics and calculate the position of the end-effector or the tool according to some frame or the world coordinate frame. This calculation can be done by the forward kinematics or the inverse kinematics. The set of programming Baxter robot in this thesis is not the complex calculation of the forward or the inverse kinematics. The tf (transform)tool in ROS has made it easier to reach the joint angles and program Baxter robot using Python.
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Towards an Automated Weight Lifting Coach: Introducing LIFTLady, Michael Andrew 01 June 2014 (has links) (PDF)
The fitness device market is young and rapidly growing. More people than ever before take count of how many steps they walk, how many calories they burn, their heart rate over time, and even their quality of sleep. New, and as of yet, unreleased fitness devices have promised the next evolution of functionality with exercise technique analysis. These next generation of fitness devices have wrist and armband style form factors, which may not be optimal for barbell exercises such as back squat, bench press, and overhead press where a sensor on one arm may not provide the most relevant data about a lift. Barbell path analysis is a well-known visual tool to help diagnose weightlifting technique deficiencies, but requires a camera pointed at the athlete that is integrated with motion-tracking software. This camera set up is not available at most gyms, so this motivates the use of a small, unobtrusive sensor to obtain data about an athlete's weightlifting technique. Researchers have shown that an accelerometer attached to a barbell while the athlete is lifting yields just as accurate acceleration information as a camera. The LIFT (Leveraging Information For Training) automated weight lifting coach attempts to implement a simple, unobtrusive system for analyzing and providing feedback on barbell weight lifting technique.
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The effects of oral supplementation of the amino acid arginine on body composition and muscle function during energy restriction in male weight liftersHawkins, Colleen E. 14 March 2009 (has links)
Manufacturers of amino acid supplements claim that they can act as natural stercoids. Eighteen experienced male weight lifters were studied to test this hypothesis for the amino acid arginine. / Master of Science
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Electrical control handle for liftingsystem / Elektriskt manöverhandtag för lyftsystemLerin, Elin, Toftered, Ulrika January 2022 (has links)
The purpose of this bachelor’s thesis is to investigate and construct a computerized electric control handle for a lifting system. The goal is to construct a prototype that can enable lifts with a weightless feeling. Initially, a literature study was conducted to investigate different possible solutions. Three different sensors were evaluated in the literature study. A load cell proved to be best suited for this project. Furthermore, a prototype was constructed where a signal from a load cell could regulate the speed and direction of an electric DC motor with a worm gear. After the prototype was constructed, it was assessed by 17 people. Overall, they all agreed that the lifting experience was good or decent. Finally, it was found that it is possible to create an electric lifting system that enables lifting with a feeling of weightlessness with the help of a load cell and an electric DC motor with a worm gear. There are also opportunities for further development such as better regulation with control engineering theories. Since this project had both a strict schedule and a tight budget to relate to, it has been difficult to develop the concept to its full potential. / Syftet med denna kandidatuppsats är att undersöka och konstruera ett datoriserat elektriskt manöverhandtag för ett lyftsystem. Målet är att konstruera en prototyp som kan möjliggöra lyft med en viktlös känsla. Inledningsvis gjordes en litteraturstudie för att undersöka olika möjliga lösningar. Tre olika sensorer utvärderades i litteraturstudien. En lastcell visade sig vara bäst lämpad för detta projekt. Vidare konstruerades en prototyp där en signal från en lastcell kunde reglera hastighet och riktning på en elektrisklik strömsmotor med snäckväxel. Efter att prototypen konstruerats bedömdes den av 17 personer. Sammantaget var de alla överens om att lyftupplevelsen var bra eller okej. Slutligen fann man att det går att skapa ett elektriskt lyftsystem som möjliggör lyft med en känsla av viktlöshet med hjälp av en lastcell och en elektrisk likströmsmotor med snäckväxel. Det finns också möjligheter till vidareutveckling såsom bättre reglering med reglertekniska teorier. Eftersom detta projekt haft både ett strikt schema och en stram budget att förhålla sig till har det varit svårt att utveckla konceptet till sin fulla potential.
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Armband EMG-based Lifting Detection and Load Classification Algorithms using Static and Dynamic Lifting TrialsTaori, Sakshi Pranay 08 June 2023 (has links)
The high prevalence of work-related musculoskeletal disorders in occupational settings necessitates the development of economic, accurate, and convenient methods for quantifying biomechanical risk exposures. In terms of lifting, the occupational work environment does not provide resources for recording the start and end times of lifting tasks performed by individual workers. As a result, automatic detection of lift starts and ends is required for practical purposes. Occupational lifting styles vary depending on the asymmetry angle, which is the degree of shoulder or trunk rotation required by the lifting task. Predictive or machine learning (ML) algorithms have been increasingly used in the ergonomics field to identify occupational risk factors, such as lifting loads. However, such algorithms are often developed and validated using the dataset collected from the same lab-based experimental set-up, which limits their external validity. The recent development of wearable armbands with surface electromyography (sEMG) electrodes provides a low-cost, wireless, and non-invasive way to collect EMG data beyond laboratory settings. Despite their tremendous potential for field-based workload estimation, these armbands have not been widely implemented yet in automated lift detection and occupational workload estimation. The objective of this study was to evaluate the performance of machine learning (ML) algorithms in the automatic detection of lifts and classification of hand loads during manual lifting tasks from the data acquired by a wearable armband sensor with eight surface electromyography (sEMG) electrodes. Twelve healthy participants (six male and six female) performed repetitive symmetric (S), asymmetric (A), and free dynamic (F) lifts with three different hand-load levels (5 lb, 10 lb and 15 lb) at two origin (24" and 36") and two destination heights (6" and 36"). Three ML algorithms were utilized: Random Forest (RF), Support Vector Machines (SVM) and Gaussian Naïve Bayes (GNB). For lift detection, a subset of four participants was analyzed as a preliminary investigation. RF showed the best performance with the mean start and end errors of 0.53 ± 0.25 seconds and 0.76 ± 0.28 seconds, respectively. The accuracy score of 84.3 ± 3.3% was reported for lift start and 83.3 ± 9.9% for lift end. For hand-load classification, prediction models were developed using four different lifting datasets (S, A, S+A, and F) and were cross-validated using F as the test dataset. Mean classification accuracy was significantly lower in models developed with the S dataset (78.8 ± 7.3%) compared to A (83.3 ± 7.2%), S+A (82.1 ± 7.3%), and F (83.4 ± 8.1%). Overall, findings indicate that the implementation of ML algorithms with wearable EMG armbands for automatic lift detection in occupational settings can be promising. In hand-load classification, models developed with only controlled symmetric lifts were less accurate in predicting loads of more dynamic, unconstrained lifts, which is common in real-world settings. However, since both A and S+A demonstrated equivalent model accuracy with F, EMG armbands possess strong potential for estimating the hand loads of free-dynamic lifts using constrained lift trials involving asymmetric lifts. / Master of Science / Naturalistic occupational settings involve prolonged, frequent, and physically heavy lifting-lowering tasks that are associated with a high prevalence of musculoskeletal disorders. This necessitates the development of economic, accurate, and convenient methods for quantifying risk exposures such as load magnitude, repetitiveness and duration. In terms of lifting, the occupational work environment does not provide resources for recording the start and end of lifting tasks performed by individual workers for analysis. As a result, automatic detection of lift starts and ends is required for practical purposes. Occupational lifting styles vary depending on the asymmetry angle, which is the degree of shoulder or trunk rotation required by the lifting task. Predictive or machine learning (ML) algorithms have been increasingly used in the ergonomics field to identify occupational risk factors, such as lifting loads. However, such algorithms are often developed and validated using the dataset collected from the same lab-based experimental set-up, which limits their external validity. Electromyographic (EMG) signals representing the neuromuscular activity associated with muscular contractions can be valuable for exposure assessment. The recent development of wearable armbands with surface electromyography (sEMG) electrodes provides a low-cost, wireless, and non-invasive way to collect EMG data beyond laboratory settings. Despite their tremendous potential for field-based workload estimation, these armbands have not been widely implemented yet in automated lift detection and occupational workload estimation. The objective of this study was to evaluate the performance of machine learning (ML) algorithms in the automatic detection of lifts and classification of hand loads during manual lifting tasks from the data acquired by a wearable armband sensor with eight surface electromyography (sEMG) electrodes. Twelve healthy participants (six male and six female) performed repetitive symmetric (S), asymmetric (A), and free dynamic (F) lifts with three different hand-load levels (5 lb, 10 lb and 15 lb) at two origin (24" and 36") and two destination heights (6" and 36"). Three ML algorithms were utilized: Random Forest (RF), Support Vector Machines (SVM) and Gaussian Naïve Bayes (GNB). For lift detection, a subset of four participants was analyzed as a preliminary investigation. RF showed the best performance with the mean start and end errors of 0.53 ± 0.25 seconds and 0.76 ± 0.28 seconds, respectively. The accuracy score of 84.3 ± 3.3% was reported for lift start and 83.3 ± 9.9% for lift end. For hand-load classification, prediction models were developed using four different lifting datasets (S, A, S+A, and F) and were cross-validated using F as the test dataset. Mean classification accuracy was significantly lower in models developed with the S dataset (78.8 ± 7.3%) compared to A (83.3 ± 7.2%), S+A (82.1 ± 7.3%), and F (83.4 ± 8.1%). Overall, findings indicate that the implementation of ML algorithms with wearable EMG armbands for automatic lift detection in occupational settings can be promising. In hand-load classification, models developed with only controlled symmetric lifts were less accurate in predicting loads of more dynamic, unconstrained lifts, which is common in real-world settings. However, since both A and S+A demonstrated equivalent model accuracy with F, EMG armbands possess strong potential for estimating the hand loads of free-dynamic lifts using constrained lift trials involving asymmetric lifts.
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Patient Lifting Device Use by Caregivers in a Hospital Setting: A proposed research programKawaja, Kathy January 2022 (has links)
The literature cites several recurrent barriers that contribute to the under-utilization of patient lifting devices (PLDs) by caregivers (CGs), resulting in the profession being at high-risk for musculoskeletal injury. There is considerable evidence that training is a barrier to PLD use, due to the staff shortages and time constraints that result when CGs attend (provincially mandated) off-site hands-on practical training. Therefore, the current research program aims to contribute towards a better understanding of the barriers to the chronic concern of low PLD use by CGs, and, to evaluate an alternative approach that could be used to reduce the time CGs spend off the floor and enhance musculoskeletal health and well-being. Study 1 will conduct focus groups and administer a Theory of Planned Behaviour (TPB)-based questionnaire to better understand the barriers between (a) CGs’ knowledge (training/education) and intent to use PLDs, and (b) CGs’ intent to use PLDs and actual PLD use (i.e., behaviour). Also, patients and their family members will be interviewed to better understand the role of the patient as a potential barrier to PLD use. Study 2 will conduct focus groups with: (i) hospital staff who design, develop and deliver PLD training programs, (ii) unit managers, and (iii) new CG hires. Via questionnaire, Preceptors will evaluate the impact of the barrier subcategories identified on the perceived overall effectiveness of a PLD training program. Study 3 will explore the feasibility of implementing vicarious learning through observation (two intervention groups) as an effective alternative to off-site hands-on learning (control group) for new CG hires, with Preceptors evaluating the three groups’ effectiveness via a questionnaire. It is hypothesized that (a) training is an important barrier to the under-utilization of PLDs by CGs (Study 1), (b) there is a need for an effective alternative to off-site hands-on learning that does not remove CGs from units (Study 2), and (c) vicarious learning through observation is as effective as hands-on learning for the utilization of PLDs by new CG hires. / Thesis / Master of Science in Kinesiology
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Marietta College's Strength Training ProgramHaines, Brian Paul 12 April 2007 (has links)
No description available.
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Understanding Behavioral and Physiological Changes associated with Repetitive Lifting and Vibration ExposureMehta, Jay Paresh January 2013 (has links)
No description available.
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Theoretical Basis for General Mixed Object Handling Equations Based on Mechanical Work RequiredRavelo, Emilio M. January 2004 (has links)
No description available.
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