Derek J. Lura

Differences in myoelectric and body-powered upper-limb prostheses: Systematic literature review.

The choice of a myoelectric or body-powered upper-limb prosthesis can be determined using factors including control, function, feedback, cosmesis, and rejection. Although body-powered and myoelectric control strategies offer unique functions, many prosthesis users must choose one. A systematic review was conducted to determine differences between myoelectric and body-powered prostheses to inform evidence-based clinical practice regarding prescription of these devices and training of users. A search of 9 databases identified 462 unique publications. Ultimately, 31 of them were included and 11 empirical evidence statements were developed. Conflicting evidence has been found in terms of the relative functional performance of body-powered and myoelectric prostheses. Body-powered prostheses have been shown to have advantages in durability, training time, frequency of adjustment, maintenance, and feedback; however, they could still benefit from improvements of control. Myoelectric prostheses have been shown to improve cosmesis and phantom-limb pain and are more accepted for light=intensity work. Currently, evidence is insufficient to conclude that either system provides a significant general advantage. Prosthetic selection should be based on a patients individual needs and include personal preferences, prosthetic experience, and functional needs. This work demonstrates that there is a lack of empirical evidence regarding functional differences in upper-limb prostheses.

STAIR ASCENT AND RAMP GAIT TRAINING WITH THE GENIUM KNEE

Accepted May 1, 2013. Address correspondence to M. Jason Highsmith, School of Physical Therapy & Rehabilitation Sciences, Morsani College of Medicine, University of South Florida, 12901 Bruce B. Downs Blvd., MDC 077, Tampa, FL 33612-4799, USA. Tel: +1-813-974-3806 (office); Fax: +1-813-974-8915; E-mail: mhighsmi@health.usf.edu Technology and Innovation, Vol. 15, pp. 349–358, 2014 1949-8241/14

Perceived differences Between the Genium and the c-LeG m icro Processor Prosthetic Knees in Prosthetic-reLated function and QuaLity of Life

90.00 + .00 Printed in the USA. All rights reserved. DOI: http://dx.doi.org/10.3727/194982413X13844488879267 Copyright  2014 Cognizant Comm. Corp. E-ISSN 1949-825X www.cognizantcommunication.com

Robot kinematics based model to predict compensatory motion of transradial prosthesis while performing bilateral tasks

Microprocessor knees (MPKs) are a viable option for persons with transfemoral amputation (TFA). Studies have assessed biomechanics and physical function to quantify MPK functional performance. However, it is also essential to assess patient perception as part of evidence-based practice using valid and reliable measures. The Prosthesis Evaluation Questionnaire (PEQ) evaluates prosthetic-related function and quality of life. The PEQ has been used in MPK literature to compare perceptive response between C-Leg and non-microprocessor-controlled knee mechanisms. The Genium, a new MPK, has not been assessed for differences in perceived function. The purpose of this project was to report perceived differences in prosthetic function and quality of life following accommodation with a Genium compared with a C-Leg. Twenty people with TFA participated in this randomized crossover study. C-Leg users randomized to test first with their own C-Leg or a Genium then crossed over into the other condition for repeated testing. Nonknee prosthetic attributes were held constant. Participants completed the PEQ for each knee condition to compare perceived differences in prosthetic function and quality of life. Genium use resulted in significant improvements (p ≤ 0.05) in the following scales — Perceived Response, Social Burden, Utility, and WellBeing — as well as in individual items related to improved standing comfort, satisfaction with walking ability, and improved gait in tight spaces, hills, and slippery surfaces (p < 0.025). As a result of using the Genium, patients perceive improvements in prosthetic-related quality of life and function. Further, patients perceive improvements in very specific mobility functions related to ambulation on complex settings.

Robotic model for simulating upper body movement

In order to perform activities of daily living (ADL), a person with an amputation(s) must use a greater than normal range of movement from other anatomical body joints to compensate for the loss of movement caused by the amputation, this is called compensatory motion. By studying the compensatory motion of prosthetic users the mechanics of how they adapt to the loss of range of motion in a given limb for can be analyzed for select tasks. The purpose of this study is to create a robotic based kinematic model that can simulate the compensatory motion of a given task using given subject data. This paper reviews the use of the model to simulate compensatory motion of a transradial amputee performing two bilateral tasks: turning a steering wheel, and lifting a box. The simulation operates by changing a set of prosthetic configurations that are represented by parameters that consist of the joint degrees of freedom (DoF) provided by each prosthesis in the set. The task information is inputted into the model by defining a trajectory which the hand or prosthesis must follow to perform the task. The inclusion of the ability to model bilateral tasks is accomplished by giving control of the proximal joints to the prosthetic side. Analysis of tasks is completed by running the simulation with prosthetic and anatomical constraints attached to the left arm of the model, the right arm maintains an anatomical configuration. By running the model through this simulation the compensatory motions can be determined. Results obtained from the model can be used to select the best prosthesis for a given user, design prostheses that are more effective at selected tasks, further analyze previous studies, or to determine areas of interest for further human study.

Simulated Compensatory Motion of Transradial Prostheses

A model of the human upper limb was developed for predicting the motion of the human upper limb while performing activities of daily living. This study focuses on the effect of a joint limit function on the ability of the model to perform human like movements. This measurement was analysed by comparing the modelled joint movements with recorded movements of subjects while drinking from a cup, opening a door, turning a steering wheel, and lifting a box. The joint limit function was tested with four weighting factors: 0.00, 0.01, 0.05, and 0.10. The model showed that, for the joint limit function used, that the best results occurred when the weighting factor was 0.00. This shows that the joint limit function had a negative effect on the ability of the model to perform human like movements.

EFFECTS OF THE GENIUM KNEE SYSTEM ON FUNCTIONAL LEVEL, STAIR AMBULATION, PERCEPTIVE AND ECONOMIC OUTCOMES IN TRANSFEMORAL AMPUTEES

The prostheses used by the majority of persons with hand/arm amputations today have a very limited range of motion. Transradial (below the elbow) amputees lose the three degrees of freedom provided by the wrist and forearm. Some myoeletric prostheses currently allow for forearm pronation and supination (rotation about an axis parallel to the forearm) and the operation of a powered prosthetic hand. Older body-powered prostheses, incorporating hooks and other cable driven terminal devices, have even fewer degrees of freedom. In order to perform activities of daily living (ADL), a person with amputation(s) must use a greater than normal range of movement from other body joints to compensate for the loss of movement caused by the amputation. By studying the compensatory motion of prosthetic users we can understand the mechanics of how they adapt to the loss of range of motion in a given limb for select tasks. The purpose of this study is to create a biomechanical model that can predict the compensatory motion using given subject data. The simulation can then be used to select the best prosthesis for a given user, or to design prostheses that are more effective at selected tasks, once enough data has been analyzed. Joint locations necessary to accomplish the task with a given configuration are calculated by the simulation for a set of prostheses and tasks. The simulation contains a set of prosthetic configurations that are represented by parameters that consist of the degrees of freedom provided by the selected prosthesis. The simulation also contains a set of task information that includes joint constraints, and trajectories which the hand or prosthesis follows to perform the task. The simulation allows for movement in the wrist and forearm, which is dependent on the prosthetic configuration, elbow flexion, three degrees of rotation at the shoulder joint, movement of the shoulder joint about the sternoclavicular joint, and translation and rotation of the torso. All joints have definable restrictions determined by the prosthesis, and task.Copyright

Automatic Generation of a Subject Specific Upper Body Model From Motion Data

Compared to non-microprocessor knees, the C-Leg microprocessor knee (MPK) is bioenergentically and economically more efficient and safer for transfemoral amputation (TFA) patients. The Genium MPK has demonstrated improvements in perceived function, knee kinematics, and physical functional performance compared to C-Leg. Clinical and health economic analyses have not been conducted with the Genium knee system. The purpose of this study was to determine if laboratory determined benefits of Genium are detectable using common clinical assessments and if there are economic benefits associated with its use. This study utilized a randomized AB crossover study with 60 d follow-up including cost-effectiveness analysis. Twenty TFA patients tested with both knees in mobility and preference measures. Incremental cost-effectiveness ratios (ICER) were calculated based on performance measures. Stair Assessment Index scores improved with Genium. Mean stair completion times and descent stepping rate were not different between knees. Stair ascent stepping rate for C-Leg was greater compared with Genium (p = 0.04). Genium use decreased Four square step test completion time and increased functional level and step activity (p ≤ 0.05). Further, Genium use improved (p ≤ 0.05) function and safety in three out of five Activities of Daily Living (ADL) survey domains. Finally, more subjects preferred Genium following testing. Functional measures were used to calculate ICERs. ICER values for Genium fall within established likely-to-accept value ranges. Compared with C-Leg, Genium use improved stair walking performance, multi-directional stepping, functional level, and perceived function. In this group of community ambulators with TFA, Genium was preferred, and, while more costly, it may be worth funding due to significant improvements in functional performance with ADLs.

Validation of Functional Methods for Calculating Shoulder Joint Centers Using 3D Motion Analysis

This paper details an automated process to create a robotic model of a subject’s upper body using motion analysis data of a subject performing simple range of motion (RoM) tasks. The upper body model was created by calculating subject specific kinematics using functional joint center (FJC) methods, this makes the model highly accurate. The subjects’ kinematics were then used to find robotic parameters. This allowed the robotic model to be calculated directly from motion analysis data. The RoM tasks provide the joint motion necessary to ensure the accuracy of the FJC method. Model creation was tested using five healthy adult male subjects, with data collected using an eight camera Vicon© (Oxford, UK) motion analysis system. Common anthropometric measures were also taken manually for comparison to the FJC kinematic measures calculated from marker position data. The algorithms successfully generated models for each subject based on the recorded RoM task data. Analysis of the generated model parameters relative to the manual measures was performed to determine the correlations. Methods for replacing model parameters extracted from the motion analysis data with hand measurements are presented. The accuracy of the model generating algorithm was tested by reconstructing motion using the parameters and joint angles extracted from the RoM tasks data, correlated manual measurements, and height based correlations from literature data. Error was defined as the average difference between the recorded position and reconstructed positions and orientations of the hand. For all of the tested subjects the model generated using the RoM tasks data showed least average error over the tested trials. Each of the tested results were significantly different in position error with the FJC generated model being the most accurate, followed by the correlated measurement data, and finally the height based calculations. No difference was found between the end effector orientation of generated models. The models developed in this study will be used with additional subject tasks in order to better predict human motion.Copyright

Knee Angle Analysis Using a Wearable Motion Analysis System for Detection and Rehabilitation of Mild Traumatic Brain Injury

Research in upper body kinematics and kinetics requires accurate estimation of anatomical joints. Currently the use of regressive techniques using anatomical landmarks is the most common way of calculating upper limb joint centers. Research has shown that functional joint center methods can produce more accurate results than traditional regressive methods in the estimation of hip joint center. This paper investigates the use of functional methods for the estimation of the shoulder joint center using 3D motion analysis data. Three methods for calculating the functional joint center were tested: 1) a standard sphere fit regression, 2) a regression developed and tested for use finding the hip joint center (Piazza method) [1], and 3) a gradient method developed for this paper similar to the one used by Schonauer [2]. First the functional joint center methods were tested in MATLAB using data with random points rotating around a known joint center with varying amounts of noise. Using the MATLAB calculations the accuracy and repeatability of each method was analyzed. Functional joint centers were then calculated from two sets of motion analysis data. The first data set contained shoulder range of motion data, and the second set was gathered during activities of daily living (ADL). Both motion analysis sets used data collected from a healthy adult male subject using a Vicon motion analysis system. The repeatability of each method using the motion analysis data was then analyzed. The MATLAB tests show that the gradient method has the highest tolerance to noise in the data. Results from the motion analysis test show that, of the methods tested, no functional method was found to have consistent results for individual tasks. Each of the functional methods requires a range of motion not prevalent in most ADLs in order to generate a reliable joint center. Joint centers calculations improved in accuracy and reliability with a greater number of trials and larger range of motion. The functional methods are suitable for use in future studies that include a large range of motion.Copyright