The Investigation of Motor Primitives During Human Reaching Movements and the Quantification of Post-Stroke Motor Impairment

Movement is a complex task, requiring precise and coordinated muscle contractions. The forces and torques produced during multi-segmental movement of the upper limbs in humans, must be controlled, in order for movement to be achieved successfully. Although a critical aspect of everyday life, there remain questions regarding the specific controller used by the central nervous system to govern movement. Furthermore, how this system is affected by neurological injuries such as stroke also remains in question. It was the goal of this thesis to examine the neurological control of movement in healthy individuals and apply these findings to the further investigation of chronically motor impaired stroke patients. Additionally, this work aimed at providing clinicians with a more reliable, easy to use, and inexpensive approach to quantify post-stroke motor impairment

Markerless Kinematics of Pediatric Manual Wheelchair Mobility

Pediatric manual wheelchair users face substantial risk of orthopaedic injury to the upper extremities, particularly the shoulders, during transition to wheelchair use and during growth and development. Propulsion strategy can influence mobility efficiency, activity participation, and quality of life. The current forefront of wheelchair biomechanics research includes translating findings from adult to pediatric populations, improving the quality and efficiency of care under constrained clinical funding, and understanding injury mechanisms and risk factors. Typically, clinicians evaluate wheelchair mobility using marker-based motion capture and instrumentation systems that are precise and accurate but also time-consuming, inconvenient, and expensive for repeated assessments. There is a substantial need for technology that evaluates and improves wheelchair mobility outside of the laboratory to provide better outcomes for wheelchair users, enhancing clinical data. Advancement in this area gives physical therapists better tools and the supporting research necessary to improve treatment efficacy, mobility, and quality of life in pediatric wheelchair users. This dissertation reports on research studies that evaluate the effect of physiotherapeutic training on manual wheelchair mobility. In particular, these studies (1) develop and characterize a novel markerless motion capture-musculoskeletal model systems interface for kinematic assessment of manual wheelchair propulsion biomechanics, (2) conduct a longitudinal investigation of pediatric manual wheelchair users undergoing intensive community-based therapy to determine predictors of kinematic response, and (3) evaluate propulsion pattern-dependent training efficacy and musculoskeletal behavior using visual biofeedback.Results of the research studies show that taking a systems approach to the kinematic interface produces an effective and reliable system for kinematic assessment and training of manual wheelchair propulsion. The studies also show that the therapeutic outcomes and orthopaedic injury risk of pediatric manual wheelchair users are significantly related to the propulsion pattern employed. Further, these subjects can change their propulsion pattern in response to therapy even in the absence of wheelchair-based training, and have pattern-dependent differences in joint kinematics, musculotendon excursion, and training response. Further clinical research in this area is suggested, with a focus on refining physiotherapeutic training strategies for pediatric manual wheelchair users to develop safer and more effective propulsion patterns

Human Gait Model Development for Objective Analysis of Pre/Post Gait Characteristics Following Lumbar Spine Surgery

Although multiple advanced tools and methods are available for gait analysis, the gait and its related disorders are usually assessed by visual inspection in the clinical environment. This thesis aims to introduce a gait analysis system that provides an objective method for gait evaluation in clinics and overcomes the limitations of the current gait analysis systems. Early identification of foot drop, a common gait disorder, would become possible using the proposed methodology

Low-Cost Sensors and Biological Signals

Many sensors are currently available at prices lower than USD 100 and cover a wide range of biological signals: motion, muscle activity, heart rate, etc. Such low-cost sensors have metrological features allowing them to be used in everyday life and clinical applications, where gold-standard material is both too expensive and time-consuming to be used. The selected papers present current applications of low-cost sensors in domains such as physiotherapy, rehabilitation, and affective technologies. The results cover various aspects of low-cost sensor technology from hardware design to software optimization

Gait analysis with the Kinect v2: normative study with healthy individuals and comprehensive study of its sensitivity, validity, and reliability in individuals with stroke

[EN] Background: Gait is usually assessed by clinical tests, which may have poor accuracy and be biased, or instrumented systems, which potentially solve these limitations at the cost of being time-consuming and expensive. The different versions of the Microsoft Kinect have enabled human motion tracking without using wearable sensors at a low-cost and with acceptable reliability. This study aims: First, to determine the sensitivity of an open-access Kinect v2-based gait analysis system to motor disability and aging; Second, to determine its concurrent validity with standardized clinical tests in individuals with stroke; Third, to quantify its inter and intra-rater reliability, standard error of measurement, minimal detectable change; And, finally, to investigate its ability to identify fall risk after stroke. Methods: The most widely used spatiotemporal and kinematic gait parameters of 82 individuals post-stroke and 355 healthy subjects were estimated with the Kinect v2-based system. In addition, participants with stroke were assessed with the Dynamic Gait Index, the 1-min Walking Test, and the 10-m Walking Test. Results: The system successfully characterized the performance of both groups. Significant concurrent validity with correlations of variable strength was detected between all clinical tests and gait measures. Excellent inter and intra-rater reliability was evidenced for almost all measures. Minimal detectable change was variable, with poorer results for kinematic parameters. Almost all gait parameters proved to identify fall risk. Conclusions: Results suggest that although its limited sensitivity to kinematic parameters, the Kinect v2-based gait analysis could be used as a low-cost alternative to laboratory-grade systems to complement gait assessment in clinical settings.This study was funded by project VALORA, grant 201701-10 of the Fundacio la Marato de la TV3 (Barcelona, Spain), and grant "Ayuda a Primeros Proyectos de Investigacion (PAID-06-18), Vicerrectorado de Investigacion, Innovacion y Transferencia de la Universitat Politecnica de Valencia" (Valencia, Spain).Latorre, J.; Colomer, C.; Alcañiz Raya, ML.; Llorens Rodríguez, R. (2019). Gait analysis with the Kinect v2: normative study with healthy individuals and comprehensive study of its sensitivity, validity, and reliability in individuals with stroke. Journal of NeuroEngineering and Rehabilitation. 16:1-11. https://doi.org/10.1186/s12984-019-0568-yS11116Balaban B, Tok F. Gait disturbances in patients with stroke. PM&R. 2014;6(7):635–42.Woolley SM. 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Markerless Analysis of Upper Extremity Kinematics during Standardized Pediatric Assessment

Children with hemiplegic cerebral palsy experience reduced motor performance in the affected upper extremity and are typically evaluated based on degree of functional impairment using activity-based assessments such as the Shriners Hospitals for Children Upper Extremity Evaluation (SHUEE), a validated clinical measure, to describe performance prior to and following rehabilitative or surgical interventions. Evaluations rely on subjective therapist scoring techniques and lack sensitivity to detect change. Objective clinical motion analysis systems are an available but time-consuming and cost-intensive alternative, requiring uncomfortable application of markers to the patient. There is currently no available markerless, low-cost system that quantitatively assesses upper extremity kinematics to improve sensitivity of evaluation during standardized task performance. A motion analysis system was developed, using Microsoft Kinect hardware to track motion during broad arm and subtle hand and finger movements. Algorithms detected and recorded skeletal position and calculated angular kinematics. Lab-developed articulating hand model and elbow fixation devices were used to evaluate accuracy, intra-trial, and inter-trial reliability of the Kinect platform. Results of technical evaluation indicate reasonably accurate detection and differentiation between hand and arm positions. Twelve typically-developing adolescent subjects were tested to characterize and evaluate performance scores obtained from the SHUEE and Kinect motion analysis system. Feasibility of the platform was determined in terms of kinematics and as an enhancement of quantitative kinematic reporting to the SHUEE, and a population mean of typically developing subject kinematics obtained for future development of performance scoring algorithms. The system was observed to be easily operable and clinically effective in subject testing. The Kinect motion analysis platform developed to quantify upper extremity motion during standardized tasks is a low-cost, portable, accurate, and reliable system in kinematic reporting, and has demonstrated quality of results in both technical evaluation of the system and a study of its applicability to standardized task-based evaluation, but has hardware and software limitations which will be resolved in future improvements of the system. The SHUEE benefits from improved quantitative data, and the Kinect system provides enhanced sensitivity in clinical upper extremity analysis for children with hemiplegic cerebral palsy

Dynamic Calibration of EMG Signals for Control of a Wearable Elbow Brace

Musculoskeletal injuries can severely inhibit performance of activities of daily living. In order to regain function, rehabilitation is often required. Assistive devices for use in rehabilitation are an avenue explored to increase arm mobility by guiding therapeutic exercises or assisting with motion. Electromyography (EMG), which are the muscle activity signals, may be able to provide an intuitive interface between the patient and the device if appropriate classification models allow smart systems to relate these signals to the desired device motion. Unfortunately, there is a gap in the accuracy of pattern recognition models classifying motion in constrained laboratory environments, and large reductions in accuracy when used for detecting dynamic unconstrained movements. An understanding of combinations of motion factors (limb positions, forces, velocities) in dynamic movements affecting EMG, and ways to use information about these motion factors in control systems is lacking. The objectives of this thesis were to quantify how various motion factors affect arm muscle activations during dynamic motion, and to use these motion factors and EMG signals for detecting interaction forces between the person and the environment during motion. To address these objectives, software was developed and implemented to collect a unique dataset of EMG signals while healthy individuals performed unconstrained arm motions with combinations of arm positions, interaction forces with the environment, velocities, and types of motion. An analysis of the EMG signals and their use in training classification models to predict characteristics (arm positions, force levels, and velocities) of intended motion was completed. The results quantify how EMG features change significantly with variations in arm positions, interaction forces, and motion velocities. The results also show that pattern recognition models, usually used to detect movements, were able to detect intended characteristics of motion based solely on EMG signals, even during complex activities of daily living. Arm position during elbow flexion--extension was predicted with 83.02 % accuracy by a support vector machine model using EMG signal inputs. Prediction of force, the motion characteristic that cannot be measured without impeding motion, was improved from 76.85 % correct to 79.17 % accurate during elbow flexion--extension by providing measurable arm position and velocity information as additional inputs to a linear discriminant analysis model. The accuracy of force prediction was improved by 5.2 % (increased from 59.38 % to 64.58 %) during an activity of daily living when motion speeds were included as an input to a linear discriminant analysis model in addition to EMG signals. Future work should expand on using motion characteristics and EMG signals to identify interactions between a person and the environment, in order to guide high level tuning of control models working towards controlling wearable elbow braces during dynamic movements

VRShape: A Virtual Reality Tool for Shaping Movement Compensation

The majority of persons living with chronic stroke experience some form of upper extremity motor impairment that affects their functional movement, performance of meaningful activities, and participation in the flow of daily life. Stroke survivors often compensate for these impairments by adapting their movement patterns to incorporate additional degrees of freedom at new joints and body segments. One of the most common compensatory movements is the recruitment of excessive trunk flexion when reaching with the affected upper extremity. Long-term use of these compensations may lead to suboptimal motor recovery and chronic pain or injury due to overuse. Rehabilitation focuses on repetitive practice with the impaired limb to stimulate motor learning and neuroplasticity; however, few interventions achieve the required repetition dose or address the possible negative effects of compensatory movements. Virtual reality (VR) is an emerging tool in rehabilitation science that may be capable of (1) objectively measuring compensation during upper extremity movement, (2) motivating persons to perform large doses of repetitive practice through the integration of virtual environments and computer games, and (3) providing the basis for a motor intervention aimed at improving motor performance and incrementally reducing, or shaping, compensation. The purpose of this project was to develop and test a VR tool with these capabilities for shaping movement compensation for persons with chronic stroke, and to achieve this we performed three separate investigations (Chapters 2-4).First, we investigated the validity and reliability of two generations of an off-the-shelf motion sensor, namely the Microsoft Kinect, for measuring trunk compensations during reaching (Chapter 2). A small group of healthy participants performed various reaching movements on two separate days while simultaneously being recorded by the two sensors and a third considered to be the gold standard. We found that the second generation Kinect sensor was more accurate and showed greater validity for measuring trunk flexion relative to the gold standard, especially during extended movements, and therefore recommended that sensor for future VR development. Research with a more heterogeneous and representative population, such as persons with stroke, will further improve the evaluation of these sensors in future work.Second, we tested a newly-designed VR tool, VRShape, for use during a single session of upper extremity movement practice (Chapter 3). VRShape integrates the Microsoft Kinect and custom software to convert upper extremity movements into the control of various virtual environments and computer games while providing real-time feedback about compensation. A small group of participants with stroke used VRShape to repetitively perform reaching movements while simultaneously receiving feedback concerning their trunk flexion relative to a calibrated threshold. Our tool was able to elicit a large number of successful reaches and limit the amount of trunk flexion used during a single practice session while remaining usable, motivating, and safe. However, areas of improvement were identified relative to the efficiency of the software and the variety of virtual environments available. Third, we implemented VRShape over the course of a motor intervention for persons with stroke and evaluated its feasibility and effect on compensation during reaching tasks (Chapter 4). A small group of participants took part in 18 interventions session using VRShape for repetitive reaching practice with incrementally shaped trunk compensation. Trunk flexion decreased significantly and reaching kinematics improved significantly as a result of the intervention. Even with extended use, participants were able to complete intense practice and thousands of repetitions while continually rating the system as usable, motivating, engaging, and safe. Our VR tool demonstrated feasibility and preliminary efficacy within a small study, but future work is needed to identify its ideal applications and address its limitations. In summary, this project shows that use of a VR tool incorporating an accurate sensor (Chapter 2) and feedback from initial testing (Chapter 3) is capable of changing the amount of trunk flexion used during reaching movements for persons with stroke (Chapter 4). More research is needed to establish its efficacy and effectiveness, but improvements in motor recovery and associated decreases in compensation associated with the use of VRShape are important rehabilitation goals that may lead to improved participation and quality of life for persons living with long-term impairments due to chronic stroke

Shear-promoted drug encapsulation into red blood cells: a CFD model and μ-PIV analysis

The present work focuses on the main parameters that influence shear-promoted encapsulation of drugs into erythrocytes. A CFD model was built to investigate the fluid dynamics of a suspension of particles flowing in a commercial micro channel. Micro Particle Image Velocimetry (μ-PIV) allowed to take into account for the real properties of the red blood cell (RBC), thus having a deeper understanding of the process. Coupling these results with an analytical diffusion model, suitable working conditions were defined for different values of haematocrit

Hand motion analysis during the execution of the action research arm test using multiple sensors

The Action Research Arm Test (ARAT) is a standardized outcome measure that can be improved by integrating sensors for hand motion analysis. The purpose of this study is to measure the flexion angle of the finger joints and fingertip forces during the performance of three subscales (Grasp, Grip, and Pinch) of the ARAT, using a data glove (CyberGlove II®) and five force-sensing resistors (FSRs) simultaneously. An experimental study was carried out with 25 healthy subjects (right-handed). The results showed that the mean flexion angles of the finger joints required to perform the 16 activities were Thumb (Carpometacarpal Joint (CMC) 28.56°, Metacarpophalangeal Joint (MCP) 26.84°, and Interphalangeal Joint (IP) 13.23°), Index (MCP 46.18°, Index Proximal Interphalangeal Joint (PIP) 38.89°), Middle (MCP 47.5°, PIP 42.62°), Ring (MCP 44.09°, PIP 39.22°), and Little (MCP 31.50°, PIP 22.10°). The averaged fingertip force exerted in the Grasp Subscale was 8.2 N, in Grip subscale 6.61 N and Pinch subscale 3.89 N. These results suggest that the integration of multiple sensors during the performance of the ARAT has clinical relevance, allowing therapists and other health professionals to perform a more sensitive, objective, and quantitative assessment of the hand function.Postprint (published version