1,067 research outputs found

    Correlations Between Shoulder Rotational Motion, Strength Measures and Throwing Biomechanics in Collegiate Baseball Pitchers

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    Pitching involves high stresses to the arm that may alter soft tissue responsible for controlling biomechanics. It has been hypothesized that imbalances in strength and flexibility of the dominant shoulder lead to decreased performance and increased injury risk, but it is not fully known what specific pitching biomechanics are altered. There is a critical need to determine correlations between shoulder rotational strength, range of motion and pitching kinetics. Without such knowledge, identifying potential for injury from shoulder imbalances will likely remain difficult and invasive. The goal of this study was to determine correlations between shoulder rotational strength and range of motion and kinetics. Twelve collegiate pitchers participated in this IRB approved study. The clinical measures session tested shoulder rotational range of motion and strength and grip strength. The motion analysis session tested pitching biomechanics. Paired t-tests investigated differences in strength and range of motion between arms. Linear regression was performed to determine correlations between clinical measures, kinetics and pitch velocity. Regression learner neural networks were created to predict pitch velocity and elbow varus torque using clinical measures as inputs. The dominant arm had significantly higher external rotation and total range of motion than the nondominant arm. The nondominant arm normalized external rotation peak torque was significantly greater than the dominant arm at 0˚ external rotation. Correlations were found between elbow varus torque and isometric external/internal rotation ratio, and between shoulder posterior shear force and isokinetic eccentric external rotation/internal rotation ratios. Correlations to velocity included grip strength, concentric external rotation peak torque, isometric internal rotation peak torques, and isometric external rotation peak torques. The neural network accurately predicted velocity, with the standard deviation of the error equal to 2.29 (2.97%). These correlations associate two testing methods to identify injury risk. Increasing external/internal rotation ratios may decrease elbow varus torque and shoulder posterior shear force. Increasing external rotation, internal rotation, and grip strength may lead to velocity gains. Velocity can be predicted using clinical measures and a neural network

    ASSOCIATIONS BETWEEN GLENOHUMERAL ROTATION STRENGTH AND SELECT KINETIC PARAMETERS DURING THE BASEBALL PITCH IN ADOLESCENT BASEBALL PITCHERS

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    The purpose of this study was to examine the associations between isometric glenohumeral rotation strength and select biomechanical parameters during the pitching motion in adolescent baseball pitchers. Glenohumeral (GH) rotation strength and pitching kinetic data were assessed in 28 (14.2 ±0.94 yrs; 66.5 ±11.7 kg; 175 ±10.8 cm) adolescent baseball pitchers. Spearman’s rank correlations were used to assess relationships between GH rotation strength and upper extremity torques during the pitching motion. Peak GH internal rotation torque during the pitch was negatively correlated with the ratio of throwing arm external rotation strength to non-throwing arm external rotation strength (r= -0.552, p \u3c 0.05). These results provide evidence for a potential mechanism behind the increased injury risk seen in pitchers who exhibit GH external rotation weakness

    Humanoid Robots

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    For many years, the human being has been trying, in all ways, to recreate the complex mechanisms that form the human body. Such task is extremely complicated and the results are not totally satisfactory. However, with increasing technological advances based on theoretical and experimental researches, man gets, in a way, to copy or to imitate some systems of the human body. These researches not only intended to create humanoid robots, great part of them constituting autonomous systems, but also, in some way, to offer a higher knowledge of the systems that form the human body, objectifying possible applications in the technology of rehabilitation of human beings, gathering in a whole studies related not only to Robotics, but also to Biomechanics, Biomimmetics, Cybernetics, among other areas. This book presents a series of researches inspired by this ideal, carried through by various researchers worldwide, looking for to analyze and to discuss diverse subjects related to humanoid robots. The presented contributions explore aspects about robotic hands, learning, language, vision and locomotion

    A 6-DOF haptic manipulation system to verify assembly procedures on CAD models

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    During the design phase of products and before going into production, it is necessary to verify the presence of mechanical plays, tolerances, and encumbrances on production mockups. This work introduces a multi-modal system that allows verifying assembly procedures of products in Virtual Reality starting directly from CAD models. Thus leveraging the costs and speeding up the assessment phase in product design. For this purpose, the design of a novel 6-DOF Haptic device is presented. The achieved performance of the system has been validated in a demonstration scenario employing state-of-the-art volumetric rendering of interaction forces together with a stereoscopic visualization setup

    Advancing Medical Technology for Motor Impairment Rehabilitation: Tools, Protocols, and Devices

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    Excellent motor control skills are necessary to live a high-quality life. Activities such as walking, getting dressed, and feeding yourself may seem mundane, but injuries to the neuromuscular system can render these tasks difficult or even impossible to accomplish without assistance. Statistics indicate that well over 100 million people are affected by diseases or injuries, such as stroke, Parkinson’s Disease, Multiple Sclerosis, Cerebral Palsy, peripheral nerve injury, spinal cord injury, and amputation, that negatively impact their motor abilities. This wide array of injuries presents a challenge to the medical field as optimal treatment paradigms are often difficult to implement due to a lack of availability of appropriate assessment tools, the inability for people to access the appropriate medical centers for treatment, or altogether gaps in technology for treating the underlying impairments causing the disability. Addressing each of these challenges will improve the treatment of movement impairments, provide more customized and continuous treatment to a larger number of patients, and advance rehabilitative and assistive device technology. In my research, the key approach was to develop tools to assess and treat upper extremity movement impairment. In Chapter 2.1, I challenged a common biomechanical[GV1] modeling technique of the forearm. Comparing joint torque values through inverse dynamics simulation between two modeling platforms, I discovered that representing the forearm as a single cylindrical body was unable to capture the inertial parameters of a physiological forearm which is made up of two segments, the radius and ulna. I split the forearm segment into a proximal and distal segment, with the rationale being that the inertial parameters of the proximal segment could be tuned to those of the ulna and the inertial parameters of the distal segment could be tuned to those of the radius. Results showed a marked increase in joint torque calculation accuracy for those degrees of freedom that are affected by the inertial parameters of the radius and ulna. In Chapter 2.2, an inverse kinematic upper extremity model was developed for joint angle calculations from experimental motion capture data, with the rationale being that this would create an easy-to-use tool for clinicians and researchers to process their data. The results show accurate angle calculations when compared to algebraic solutions. Together, these chapters provide easy-to-use models and tools for processing movement assessment data. In Chapter 3.1, I developed a protocol to collect high-quality movement data in a virtual reality task that is used to assess hand function as part of a Box and Block Test. The goal of this chapter is to suggest a method to not only collect quality data in a research setting but can also be adapted for telehealth and at home movement assessment and rehabilitation. Results indicate that the data collected in this protocol are good and the virtual nature of this approach can make it a useful tool for continuous, data driven care in clinic or at home. In Chapter 3.2 I developed a high-density electromyography device for collecting motor unit action potentials of the arm. Traditional surface electromyography is limited by its ability to obtain signals from deep muscles and can also be time consuming to selectively place over appropriate muscles. With this high-density approach, muscle coverage is increased, placement time is decreased, and deep muscle activity can potentially be collected due to the high-density nature of the device[GV2] . Furthermore, the high-density electromyography device is built as a precursor to a high-density electromyography-electrical stimulation device for functional electrical stimulation. The customizable nature of the prototype in Chapter 3.2 allows for the implementation both recording and stimulating electrodes. Furthermore, signal results show that the electromyography data obtained from the device are of high quality and are correlated with gold standard surface electromyography sensors. One key factor in a device that can record and then stimulate based on the information from the recorded signals is an accurate movement intent decoder. High-quality movement decoders have been designed by closed-loop device controllers in the past, but they still struggle when the user interacts with objects of varying weight due to underlying alterations in muscle signals. In Chapter 4, I investigate this phenomenon by administering an experiment where participants perform a Box and Block Task with objects of 3 different weights, 0 kg, 0.02 kg, and 0.1 kg. Electromyography signals of the participants right arm were collected and co-contraction levels between antagonistic muscles were analyzed to uncover alterations in muscle forces and joint dynamics. Results indicated contraction differences between the conditions and also between movement stages (contraction levels before grabbing the block vs after touching the block) for each condition. This work builds a foundation for incorporating object weight estimates into closed-loop electromyography device movement decoders. Overall, we believe the chapters in this thesis provide a basis for increasing availability to movement assessment tools, increasing access to effective movement assessment and rehabilitation, and advance the medical device and technology field

    Kinematic and Kinetic Comparisons of Arm and Hand Reaching Movements with Mild and Moderate Gravity-Supported, Computer-Enhanced Armeo®spring: A Case Study

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    Background: Stroke has been recognized as a leading cause of serious long-term disability in the United States (U.S.) with 795,000 people experience a new or recurrent stroke each year (Roger et al., 2011). The most apparent defect after stroke is motor impairments (Masiero, Armani, & Rosati, 2011). Statistically, half of stroke survivors suffer from upper extremity hemiparesis and approximately one quarter become dependent in activities of daily living (Sanchez et al., 2006). There is strong evidence that intensity and task specificity are the main drivers in an effective treatment program after stroke. In addition, this training should be repetitive, functional, meaningful, and challenging for a patient (Van Peppen et al., 2004). The use of robotic systems to complement standard poststroke multidisciplinary programs is a recent approach that looks very promising. Robotic devices can provide high-intensity, repetitive, task-specific, interactive treatment of the impaired limb and can monitor patients\u27 motor progress objectively and reliably, measuring changes in quantitative movement kinematics and forces (Masiero, Armani, & Rosati, 2011). Objective: The purpose of this study was to examine the role of Armeo®Spring (Hocoma, Inc.), a gravity-supported, computer-enhanced robotic devise, on reaching movements while using two different gravity-support levels (mild and moderate weight support) on individuals with stroke. Methods: One stroke subject and one gender-matched healthy control participated in this study after gaining their informed consent. Both subjects performed a computer-based game (picking apples successfully and placing them in a shopping cart) under two gravity weight-support conditions (mild and moderate) provided by the Armeo®Spring device. The game tasks were described as a reaching cycle which consisted of five phases (initiation, reaching, grasping, transporting, and releasing). Joint angles for the glenohumeral and elbow joints throughout the reaching cycle were found. Three kinematic parameters (completion time, moving velocity, acceleration) and one kinetic parameter (vertical force acting on the forearm) was calculated for various instances and phases of the reaching motion. In addition, the muscle activation patterns for anterior deltoid, middle deltoid, biceps, triceps, extensor digitorum, flexor digitorum, and brachioradialis were found and the mean magnitude of the electromyography (EMG) signal during each phase of the reaching cycle was found as a percentage of the subject\u27s maximum voluntary contraction (MVC). Results: Within the healthy control subject, results demonstrated no significant differences in mean completion time, moving velocity, or acceleration between mild to moderate gravity-support levels during all phases of the cycle. The stroke subject results revealed a significant decrease in the cycle mean completion time (p= 0.042) between the two gravity-support levels, specifically in mean completion time of the grasping phase. A significant increase was found in the initiation phase moving velocity (p=0.039) and a significant decrease was found in the grasping phase (p=0.048) between two gravity-support levels in the stroke subject. Between subjects, significant increase in the cycle mean completion time was found under both mild and moderate conditions (p\u3c.001 for both conditions). Additionally, significant decreases in the moving velocities were found in all phases of the cycle between the healthy control and the stroke subject under both conditions. With increasing weight support, the healthy control subject showed an increase in abduction and flexion degrees at the glenohumeral joint level, and an increase in flexion degrees of the elbow joint. On the other hand, the stroke subject showed a decrease in abduction degrees and an increase in flexion degrees at the glenohumeral joint level, and a decrease in flexion degrees of the elbow joint after increasing the weight-support level. Results demonstrated an increase in the mean of vertical forces when changing gravity-support levels from mild to moderate during all phases of the cycle in both stroke and healthy subjects. Last, the average EMG magnitude during the reaching cycle phases was reduced for muscles acting against gravity (anterior deltoid, middle deltoid, biceps, and brachioradialis) in both the healthy control and the stroke subject. Conclusion: The significant differences in movement performance between mild and moderate physical weight support suggested a preliminary result that the gravity-supported mechanism provides a mean to facilitate functional upper limb motor performance in individuals with stroke. Future studies should examine such effects with larger sample sizes

    Virtual Reality

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    At present, the virtual reality has impact on information organization and management and even changes design principle of information systems, which will make it adapt to application requirements. The book aims to provide a broader perspective of virtual reality on development and application. First part of the book is named as "virtual reality visualization and vision" and includes new developments in virtual reality visualization of 3D scenarios, virtual reality and vision, high fidelity immersive virtual reality included tracking, rendering and display subsystems. The second part named as "virtual reality in robot technology" brings forth applications of virtual reality in remote rehabilitation robot-based rehabilitation evaluation method and multi-legged robot adaptive walking in unstructured terrains. The third part, named as "industrial and construction applications" is about the product design, space industry, building information modeling, construction and maintenance by virtual reality, and so on. And the last part, which is named as "culture and life of human" describes applications of culture life and multimedia-technology

    Gesture Based Control of Semi-Autonomous Vehicles

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    The objective of this investigation is to explore the use of hand gestures to control semi-autonomous vehicles, such as quadcopters, using realistic, physics based simulations. This involves identifying natural gestures to control basic functions of a vehicle, such as maneuvering and onboard equipment operation, and building simulations using the Unity game engine to investigate preferred use of those gestures. In addition to creating a realistic operating experience, human factors associated with limitations on physical hand motion and information management are also considered in the simulation development process. Testing with external participants using a recreational quadcopter simulation built in Unity was conducted to assess the suitability of the simulation and preferences between a joystick approach and the gesture-based approach. Initial feedback indicated that the simulation represented the actual vehicle performance well and that the joystick is preferred over the gesture-based approach. Improvements in the gesture-based control are documented as additional features in the simulation, such as basic maneuver training and additional vehicle positioning information, are added to assist the user to better learn the gesture-based interface and implementation of active control concepts to interpret and apply vehicle forces and torques. Tests were also conducted with an actual ground vehicle to investigate if knowledge and skill from the simulated environment transfers to a real-life scenario. To assess this, an immersive virtual reality (VR) simulation was built in Unity as a training environment to learn how to control a remote control car using gestures. This was then followed by a control of the actual ground vehicle. Observations and participant feedback indicated that range of hand movement and hand positions transferred well to the actual demonstration. This illustrated that the VR simulation environment provides a suitable learning experience, and an environment from which to assess human performance; thus, also validating the observations from earlier tests. Overall results indicate that the gesture-based approach holds promise given the emergence of new technology, but additional work needs to be pursued. This includes algorithms to process gesture data to provide more stable and precise vehicle commands and training environments to familiarize users with this new interface concept

    Design of a Wearable Sensor System for Prevention of Fatigue-Induced Injuries in Baseball Pitching

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    Ulnar collateral ligament (UCL) injuries are increasingly common in baseball pitchers of all levels and often are career ending. The aim of this project was to develop a wearable sensor system to quantify risk of UCL injury in baseball pitchers through correlation with fatigue indicated by deviations in forces and torques in the throwing arm during pitching. The outcome of this project was a wearable sensor and data analysis system which could be applicable to predicting risk of injury
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