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BACKGROUND Patient-reported outcomes (PROs) can be used to evaluate perceived capacity of an individual in executing tasks in a natural environment with their prosthetic device. According to the World Health Organization International Classification of Health, Functioning, and Disability (ICF) models, there may be specific factors of a person, factors of assistive prosthetic technology, or factors related to the health condition or body function that affect their functioning and disability. However, an understanding of factors affecting an upper limb prosthesis user's perception of their ability to execute tasks in a natural environment is not well established. OBJECTIVE To use the ICF model to identify which health condition-related, body function, environmental, and personal factors influence activity as measured by perceived function in the upper limb prosthesis user population. DESIGN Quantitative clinical descriptive study. SETTING Clinical offices within outpatient private practice (removed for blinding). PARTICIPANTS A sample of 101 participants with upper limb amputation who use a prosthetic device and were undergoing a prosthesis fitting process. INTERVENTIONS Not applicable. MAIN OUTCOME MEASURES PROs on pain with/without a prosthesis, satisfaction, and perceived function derived from the Comprehensive Arm Prosthesis and Rehabilitation Outcomes Questionnaire. RESULTS Model coefficients indicate that with a unit increase in satisfaction (p

This paper a will provide background and research outcomes for a student-led multi-University motion capture design project with the FDA Human Device Interaction Lab. This research project spawned from the FDA’s interest in studying mobile Apps used to collect information about a rehabilitation patient’s function outside of a laboratory setting. This is particularly important from a research perspective as it allows knowledge to be gathered about the reliability, safety, and efficacy of such devices, which can later inform human factors guidelines and regulatory oversight. While there is a wide range of applications for remote monitoring of patient function, this project focuses specifically on wearables used to track rehabilitative progress of individuals suffering from upper or lower limb impairment. These wearables may also be used to assess the efficacy of a new assistive technology intended to restore or augment function. The interdisciplinary student teams applied human-centered design principles to develop and prototype hardware and software components for a rotatable body-worn mobile device holder to remotely track rehabilitation progress. The design process and outcomes are presented along with insights on further development of system components.

The amount of freely available human phenotypic data is increasing daily, and yet little is known about the types of inferences or identifying characteristics that could reasonably be drawn from that data using new statistical methods. One data type of particular interest is electroencephalographical (EEG) data, collected noninvasively from humans in various behavioral contexts. The Temple University EEG corpus associates thousands of hours of de-identified EEG records with contemporaneous physician reports that include metadata that might be expected to show a measurable correlation with characteristics of the recorded signal. Given that machine learning methods applied to neurological signals are being used in emerging diagnostic applications, we leveraged this data source to test the confidence with which algorithms could predict, using a patient’s EEG record(s) as input, which medications were noted on the matching physician report. We comparatively assessed deep learning and feature-based approaches on their ability to distinguish between the assumed presence of Dilantin (phenytoin), Keppra (levetiracetam), or neither. Our methods could successfully distinguish between patients taking either anticonvulsant and those taking no medications; as well as between the two anticonvulsants. Further, we found different approaches to be most effective for different groups of classifications.

Evaluation of maladaptive compensatory movement is important to objectively identify the impact of prosthetic rehabilitative intervention on body mechanics. The Capacity Assessment of Prosthetic Performance for the Upper Limb (CAPPFUL) scores this type of compensation by comparing movements of the prosthesis user to movements of individuals with intact, sound upper limbs (ULs). However, expected movements of individuals with sound, intact ULs have not been studied for the set of tasks performed in the CAPPFUL.To enhance the scoring approach for the maladaptive compensatory movement domain of the CAPPFUL by defining normative kinematic movement and characterizing variability and repeatability.Clinical measurement.Laboratories at the U.S. Food and Drug Administration (FDA) and University of Texas-Arlington.Convenience sample of 20 participants with no upper limb (UL) disability or impairment.Not applicable.Kinematic trajectories, range of motion, maximum angle, and completion time were calculated. Repeatability and intersubject variability were assessed by calculating Pearson's correlation coefficient (R), adjusted coefficient of multiple correlation (CMCadj), and max SD (SDmax) for nine joint angles at the elbow, shoulder, neck, and torso.For most joints evaluated, repeatability was lower (R 0.8) for CAPPFUL 3-Zip vest, CAPPFUL 7-Cut w/ knife, and CAPPFUL 8-Squeeze water, implying inconsistent approaches within a subject from trial to trial for a given task. For most tasks, the joint angle SDmax across all participants was20°. The approach for completing CAPPFUL 1 - Weights in crate and CAPPFUL 4 - Pick up dice was generally similar across participants (CMCadj0.4). For other tasks, however, different approaches across participants at the torso and shoulder joint can be seen.This work established the expected movements of individuals with sound, intact ULs for tasks performed in the CAPPFUL that can be used to inform consistent, standardized scoring of the maladaptive compensatory movement domain.

Study Design Clinical measurement; 22 subjects with no upper limb disability completed the Jebsen-Taylor Hand Function Test (JHFT). Introduction To realize the potential of 3D motion capture to augment evaluation of individuals with upper limb disability/impairment, it is important to understand the expected kinematic motion that characterizes performance during functional evaluation. Purpose of the Study To assess kinematic variability and establish kinematic patterns for the JHFT. Methods Upper body joint kinematics were collected using a Vicon motion capture system. Average range of motion and maximum angle were calculated for all tasks. Intrasubject and intersubject variability were assessed by calculating Pearson's correlation coefficient, adjusted coefficient of multiple correlation (CMCadj), and standard deviation for 10 joint angles at the wrist, elbow, shoulder, and torso. Results The writing and picking up small objects tasks generally had high intrasubject variability, with most joint angles having median Pearson's correlation coefficients lower than 0.7. The CMCadj values were generally greater than 0.5 for elbow, shoulder, and torso joints during can-lifting tasks, indicating high consistency in those kinematic trajectories across subjects. Low consistency across subjects in all joint angles was observed for writing (CMCadj 10°). Discussion Kinematic patterns for the JHFT tasks were analyzed. Conclusions With kinematic patterns for the JHFT tasks analyzed, optimal patterns of activity performance can be defined, allowing for easier identification and adjustment of atypical motion. Results can be used to inform selection of tasks for kinematic evaluation and provide expected variability for comparison to patient populations, which is useful for regulatory review and clinical assessment.

Objective To understand how perceived function relates to actual function at a specific stage in the rehabilitation process for the population using upper limb prostheses. Design Quantitative clinical descriptive study. Setting Clinical offices. Participants A sample of 61 participants (N=61; mean age, 43.0±12.8y; 51 male/10 female) with upper limb amputation who use a prosthetic device and were in the definitive stage of a prosthesis fitting process. Interventions Not applicable. Main Outcome Measures A patient-reported outcome measure, the Disabilities of the Arm, Shoulder, and Hand questionnaire (DASH), and 2 performance-based outcome measures, Box and Blocks Test (BBT) and Capacity Assessment of Prosthesis Performance for the Upper Limb (CAPPFUL), were used as variables in multiple linear regression models. Results The multiple linear regression models, which controlled for prosthesis type and amputation level, did not show evidence that changes in the independent variable (DASH) are significantly associated with changes in the dependent variables (log(BBT) (B=−0.007; 95% confidence interval [CI], −0.015 to 0.001; P=.0937) and CAPPFUL (B=−0.083, 95% CI, −0.374 to 0.208; P=.5623)). In both models, individuals with elbow, transhumeral (above elbow), and shoulder disarticulation showed a significant negative association with the dependent variable (CAPPFUL or logBBT). In the CAPPFUL model, there was a significant negative association with individuals using a hybrid prosthesis (B=−20.252; 95% CI, −36.562 to −3.942; P=.0170). In the logBBT model, there was a significant positive association with individuals using body-powered prostheses (B=0.430; 95% CI, 0.089-0.771; P=.0157). Conclusions Although additional data and analyses are needed to more completely assess the association between self-reported measures and performance-based measures of functional abilities, these preliminary results indicate that patient-reported outcomes alone may not provide a complete assessment of an upper limb prosthesis users’ functional ability and should be accompanied by population-specific performance-based measures.

We aim to present a standard protocol for training able-bodied individuals to use a body-powered bypass prosthesis and assess training length and impact of prepositioning. The protocol design and subsequent analysis aims to facilitate controlled and efficient implementation of the able-bodied bypass user in the research setting. Six volunteers completed ten two-hour sessions with a body-powered bypass prosthesis. Each session included standardized training tasks: object manipulation, free training, and activities of daily living. Two outcome measures, a modified Southampton Hand Assessment Procedure and the Box and Blocks Test were used to score performance during each session. A standard learning curve was fitted to the scores to determine an optimal training length based on learning rate and learning plateau values; further tested through an effect size calculation. To assess prepositioning, scores were normalized and grouped by a measure of terminal device rotations. Scores then underwent a linear regression analysis. Optimal training lengths were found to be three and six sessions for modified Southampton Hand Assessment Procedure and Box and Blocks Test results respectively, with support from effect size calculations. Prepositioning and normalized score were weakly correlated, +0.38, and poorly fit, R2 = 0.016, contradictory to the expected strong correlation that would accompany the supposed performance benefits attributed to prepositioning. A lack of resources to guide the use of upper limb bypass prostheses is addressed with the presented standard, quantitatively assessed protocol. A framework for evaluating adequate training length and prepositioning is established and shared.

There is an increased interest in using wearable inertial measurement units (IMUs) in clinical contexts for the diagnosis and rehabilitation of gait pathologies. Despite this interest, there is a lack of research regarding optimal sensor placement when measuring joint kinematics and few studies which examine functionally relevant motions other than straight level walking. The goal of this clinical measurement research study was to investigate how the location of IMU sensors on the lower body impact the accuracy of IMU-based hip, knee, and ankle angular kinematics. IMUs were placed on 11 different locations on the body to measure lower limb joint angles in seven participants performing the timed-up-and-go (TUG) test. Angles were determined using different combinations of IMUs and the TUG was segmented into different functional movements. Mean bias and root mean square error values were computed using generalized estimating equations comparing IMU-derived angles to a reference optical motion capture system. Bias and RMSE values vary with the sensor position. This effect is partially dependent on the functional movement analyzed and the joint angle measured. However, certain combinations of sensors produce lower bias and RMSE more often than others. The data presented here can inform clinicians and researchers of placement of IMUs on the body that will produce lower error when measuring joint kinematics for multiple functionally relevant motions. Optimization of IMU-based kinematic measurements is important because of increased interest in the use of IMUs to inform diagnose and rehabilitation in clinical settings and at home.

Efforts into understanding neurological differences between populations is an active area of research. Deep learning has recently shown promising results using EEG as input to distinguish recordings of subjects based on neurological activity. However, only about one quarter of these studies investigate the underlying neurophysiological implications. This work proposes and validates a method to investigate frequency bands important to EEG-driven deep learning models. Easy perturbation EEG algorithm for spectral importance (easyPEASI) is simpler than previous methods and requires only perturbations to input data. We validate easyPEASI on EEG pathology classification using the Temple University Health EEG Corpus. easyPEASI is further applied to characterize the effects of patients' medications on brain rhythms. We investigate classifications of patients taking one of two anticonvulsant medications, Dilantin (phenytoin) and Keppra (levetiracetam), and subjects taking no medications. We find that for recordings of subjects with clinically-determined normal EEG that these medications effect the Theta and Alpha band most significantly. For recordings with clinically-determined abnormal EEG these medications affected the Delta, Theta, and Alpha bands most significantly. We also find the Beta band to be affected differently by the two medications. Results found here show promise for a method of obtaining explainable artificial intelligence and interpretable models from EEG-driven deep learning through a simpler more accessible method perturbing only input data. Overall, this work provides a fast, easy, and reproducible method to automatically determine salient spectral features of neural activity that have been learned by machine learning models, such as deep learning.

Motor learning and compensatory movement are important aspects of prosthesis training yet relatively little quantitative evidence supports our current understanding of how motor control and compensation develop in the novel body-powered prosthesis user. The goal of this study is to assess these aspects of prosthesis training through functional, kinematic, and kinetic analyses using a within-subject paradigm compared across two training time points. The joints evaluated include the left and right shoulders, torso, and right elbow. Six abled-bodied subjects (age 27 ± 3) using a body-powered bypass prosthesis completed the Jebsen-Taylor Hand Function Test and the targeted Box and Blocks Test after five training sessions and again after ten sessions. Significant differences in movement parameters included reduced times to complete tasks, reduced normalized jerk for most joints and tasks, and more variable changes in efficiency and compensation parameters for individual tasks and joints measured as range of motion, maximum angle, and average moment. Normalized jerk, joint specific path length, range of motion, maximum angle, and average moment are presented for the first time in this unique training context and for this specific device type. These findings quantitatively describe numerous aspects of motor learning and control in able-bodied subjects that may be useful in guiding future rehabilitation and training of body-powered prosthesis users.

There are several algorithms that use the 3D acceleration and/or rotational velocity vectors from IMU sensors to identify gait events (i.e., toe-off and heel-strike). However, a clear understanding of how sensor location and the type of walking task effect the accuracy of gait event detection algorithms is lacking. To address this knowledge gap, seven participants were recruited (4M/3F, 26.0 ± 4.0 y/o) to complete a straight walking task and obstacle navigation task while data were collected from IMUs placed on the foot and shin. Five different commonly used algorithms to identify the toe-off and heel-strike gait events were applied to each sensor location on a given participant. Gait metrics were calculated for each sensor/algorithm combination using IMUs and a reference pressure sensing walkway. Results show algorithms using medial-lateral rotational velocity and anterior-posterior acceleration are fairly robust against different sensor locations and walking tasks. Certain algorithms applied to heel and lower lateral shank sensor locations will result in degraded algorithm performance when calculating gait metrics for curved walking compared to straight overground walking. Understanding how certain types of algorithms perform for given sensor locations and tasks can inform robust clinical protocol development using wearable technology to characterize gait in both laboratory and real-world settings.

To evaluate movement quality of upper limb (UL) prosthesis users, performance-based outcome measures have been developed that examine the normalcy of movement as compared to a person with a sound, intact hand. However, the broad definition of “normal movement” and the subjective nature of scoring can make it difficult to know which areas of the body to evaluate, and the expected magnitude of deviation from normative movement. To provide a more robust approach to characterizing movement differences, the goals of this work are to identify degrees of freedom (DOFs) that will inform abnormal movement for several tasks using unsupervised machine learning (clustering methods) and elucidate the variations in movement approach across two upper-limb prosthesis devices with varying DOFs as compared to healthy controls. 24 participants with no UL disability or impairment were recruited for this study and trained on the use of a body-powered bypass (n = 6) or the DEKA limb bypass (n = 6) prosthetic devices or included as normative controls. 3D motion capture data were collected from all participants as they performed the Jebsen-Taylor Hand Function Test (JHFT) and targeted Box and Blocks Test (tBBT). Range of Motion, peak angle, angular path length, mean angle, peak angular velocity, and number of zero crossings were calculated from joint angle data for the right/left elbows, right/left shoulders, torso, and neck and fed into a K-means clustering algorithm. Results show right shoulder and torso DOFs to be most informative in distinguishing between bypass user and norm group movement. The JHFT page turning task and the seated tBBT elicit movements from bypass users that are most distinctive from the norm group. Results can be used to inform the development of movement quality scoring methodology for UL performance-based outcome measures. Identifying tasks across two different devices with known variations in movement can inform the best tasks to perform in a rehabilitation setting that challenge the prosthesis user’s ability to achieve normative movement.

Objectives To study the effects of advancements in upper-limb prosthesis technology on the user through biomechanical analyses at the joint level to quantitatively examine movement differences of individuals using an advanced upper-limb device, the DEKA Arm, and a conventional device, a body-powered Hosmer hook. Design Clinical measurement. Setting Laboratories at the United States Food and Drug Administration. Participants Convenience sample of participants (N=14) with no upper limb disability or impairment. Interventions All participants were trained on either an upper limb body-powered (n=6) or DEKA Arm (n=8) bypass device. Main Outcome Measures Participants completed the Jebsen-Taylor Hand Function Test (JHFT) and targeted Box and Blocks Test within a motion capture framework. Task completion times and joint angle trajectories for each degree of freedom of the right elbow, right shoulder, and torso were collected and analyzed for range of motion, mean angle, maximum angle, and angle path length during each task. Results Significant differences between devices were observed across metrics in at least one task for each degree of freedom. Completion times were significantly higher for DEKA users (eg, 30.51±19.29s vs 9.30±1.44s) for JHFT–simulated feeding. Some kinematic measures, such as angle path length, were significantly lower in DEKA users, with the greatest difference in the right elbow flexion path length during JHFT–Page Turning (0.29±0.14 units vs 0.11±0.04 units). Conclusions Results from this work elucidate the effect of the device on the user’s movement approach and performance, as well as emphasizing the importance of capturing movement quality into the assessment of function for advanced prosthetic technology to fully understand and evaluate potential benefits.

It is well documented that most upper limb amputees utilize compensatory movement strategies to accomplish everyday tasks when using a prosthetic device and that musculoskeletal complaints (MSCs) are more common in this population. However, little information is available on how the loss of distal degrees of freedom (DOFs) in the arm impact muscle force, thereby limiting our understanding of the mechanism by which these MSCs are manifesting. Knowledge of how a loss of DOFs may lead to MSCs can enable clinicians to provide more targeted guidance on how best to restore functional ability while addressing pain, and may serve as a tool for prescriptive decision-making when determining the impact of device selection on long-term clinical needs. 3D motion capture data were collected from 12 right-handed subjects with no upper limb disability using an 8-camera Vicon™ motion analysis system as they performed the targeted Box and Blocks test under normal and braced conditions to simulate a loss of DOFs in the wrist and fingers. Muscle force data were calculated using AnyBody Modeling Software™ for four different muscles: erector spinae, infraspinatus, deltoid, and trapezius. Linear mixed effects models were generated using the peak force data and mean force data for a given muscle fascicle. The fixed effect coefficient and 95% confidence intervals were reported for each muscle fascicle. Overall, a strong effect of condition on muscle force was seen for most muscles in the right side of the body (specifically deltoid and infraspinatus), with greater forces generated during the braced condition.

Objective Despite their increasing use and public health importance, little is known about the consistency and variability of the quantitative features of baseline electroencephalography (EEG) measurements in healthy individuals and populations. This study aims to investigate population consistency of EEG features. Approach We propose a non-parametric method of evaluating consistency of commonly used EEG features based on counts of non-significant statistical tests using a large data set. We first replicate stationarity results of absolute band powers using coefficients of variation. We then determine feature stationarity, intra-subject consistency, inter-subject consistency, and intra- versus inter-subject consistency across different epoch lengths for 30 features. Main results We find in general that features with normalizing constants are more stationary. We also find entropy, median, skew, and kurtosis of EEG to behave as baseline EEG metrics. However, other spectral and signal shape features have stronger intra-subject consistency and thus are better for distinguishing individuals. Significance These results provide data-driven non-parametric methods of identifying EEG features and their spatial characteristics ideal for various EEG applications, and determining future EEG feature consistencies using an existing EEG data set.

We aim to present a standard protocol for training able-bodied individuals to use a body-powered bypass prosthesis and assess training length and impact of prepositioning. The protocol design and subsequent analysis aims to facilitate controlled and efficient implementation of the able-bodied bypass user in the research setting. Six volunteers completed ten two-hour sessions with a body-powered bypass prosthesis. Each session included standardized training tasks: object manipulation, free training, and activities of daily living. Two outcome measures, a modified Southampton Hand Assessment Procedure and the Box and Blocks Test were used to score performance during each session. A standard learning curve was fitted to the scores to determine an optimal training length based on learning rate and learning plateau values; further tested through an effect size calculation. To assess prepositioning, scores were normalized and grouped by a measure of terminal device rotations. Scores then underwent a linear regression analysis. Optimal training lengths were found to be three and six sessions for modified Southampton Hand Assessment Procedure and Box and Blocks Test results respectively, with support from effect size calculations. Prepositioning and normalized score were weakly correlated, +0.38, and poorly fit, R 2 = 0.016, contradictory to the expected strong correlation that would accompany the supposed performance benefits attributed to prepositioning. A lack of resources to guide the use of upper limb bypass prostheses is addressed with the presented standard, quantitatively assessed protocol. A framework for evaluating adequate training length and prepositioning is established and shared.

Despite its increasing use and public health importance, very little is known about the consistency and variability of baseline electroencephalogram (EEG) measurements in healthy individuals and in patient populations. This research aims to investigate population variability of EEG features and their ability to classify patients based on patient characteristics. We first propose a data-driven method of evaluating consistency and variability of commonly used EEG features. Using a Kruskal-Wallis test we find normalized features compared across different time intervals within a given recording to be consistent. Further, we find certain features to have better intra-subject consistency than others, notably spectral entropy, skew, and kurtosis. Using these results we apply machine learning techniques to classify patients based on sex, age, medications taken, and clinical impressions. We find statistically significant classifications for age, medications taken, and clinical impressions, achieving accuracies above 86%, 72%, and 74%, respectively.

Purpose: For a given bowtie filter design, both the selection of material and the physical design control the energy fluence, and consequently the dose distribution, in the object. Using three previously described bowtie filter designs, the goal of this work is to demonstrate the effect that different materials have on the bowtie filter performance measures. Methods: Three bowtie filter designs that compensate for one or more aspects of the beam-modifying effects due to the differences in path length in a projection have been designed. The nature of the designs allows for their realization using a variety of materials. The designs were based on a phantom, 14 cm in diameter, composed of 40% fibroglandular and 60% adipose tissue. Bowtie design #1 is based on single material spectral matching and produces nearly uniform spectral shape for radiation incident upon the detector. Bowtie design #2 uses the idea of basis-material decomposition to produce the same spectral shape and intensity at the detector, using two different materials. With bowtie design #3, it is possible to eliminate the beam hardening effect in the reconstructed image by adjusting the bowtie filter thickness so that the effective attenuation coefficient for every ray is the same. Seven different materials were chosen to represent a range of chemical compositions and densities. After calculation of construction parameters for each bowtie filter design, a bowtie filter was created using each of these materials (assuming reasonable construction parameters were obtained), resulting in a total of 26 bowtie filters modeled analytically and in the penelope Monte Carlo simulation environment. Using the analytical model of each bowtie filter, design profiles were obtained and energy fluence as a function of fan-angle was calculated. Projection images with and without each bowtie filter design were also generated using penelope and reconstructed using FBP. Parameters such as dose distribution, noise uniformity, and scatter were investigated. Results: Analytical calculations with and without each bowtie filter show that some materials for a given design produce bowtie filters that are too large for implementation in breast CT scanners or too small to accurately manufacture. Results also demonstrate the ability to manipulate the energy fluence distribution (dynamic range) by using different materials, or different combinations of materials, for a given bowtie filter design. This feature is especially advantageous when using photon counting detector technology. Monte Carlo simulation results from penelope show that all studied material choices for bowtie design #2 achieve nearly uniform dose distribution, noise uniformity index less than 5%, and nearly uniform scatter-to-primary ratio. These same features can also be obtained using certain materials with bowtie designs #1 and #3. Conclusions: With the three bowtie filter designs used in this work, the selection of material is an important design consideration. An appropriate material choice can improve image quality, dose uniformity, and dynamic range.

PURPOSE: To design bowtie filters with improved properties for dedicated breast CT to improve image quality and reduce dose to the patient. METHODS: The authors present three different bowtie filters designed for a cylindrical 14-cm diameter phantom with a uniform composition of 40/60 breast tissue, which vary in their design objectives and performance improvements. Bowtie design #1 is based on single material spectral matching and produces nearly uniform spectral shape for radiation incident upon the detector. Bowtie design #2 uses the idea of basis material decomposition to produce the same spectral shape and intensity at the detector, using two different materials. Bowtie design #3 eliminates the beam hardening effect in the reconstructed image by adjusting the bowtie filter thickness so that the effective attenuation coefficient for every ray is the same. All three designs are obtained using analytical computational methods and linear attenuation coefficients. Thus, the designs do not take into account the effects of scatter. The authors considered this to be a reasonable approach to the filter design problem since the use of Monte Carlo methods would have been computationally intensive. The filter profiles for a cone-angle of 0° were used for the entire length of each filter because the differences between those profiles and the correct cone-beam profiles for the cone angles in our system are very small, and the constant profiles allowed construction of the filters with the facilities available to us. For evaluation of the filters, we used Monte Carlo simulation techniques and the full cone-beam geometry. Images were generated with and without each bowtie filter to analyze the effect on dose distribution, noise uniformity, and contrast-to-noise ratio (CNR) homogeneity. Line profiles through the reconstructed images generated from the simulated projection images were also used as validation for the filter designs. RESULTS: Examples of the three designs are presented. Initial verification of performance of the designs was done using analytical computations of HVL, intensity, and effective attenuation coefficient behind the phantom as a function of fan-angle with a cone-angle of 0°. The performance of the designs depends only weakly on incident spectrum and tissue composition. For all designs, the dynamic range requirement on the detector was reduced compared to the no-bowtie-filter case. Further verification of the filter designs was achieved through analysis of reconstructed images from simulations. Simulation data also showed that the use of our bowtie filters can reduce peripheral dose to the breast by 61% and provide uniform noise and CNR distributions. The bowtie filter design concepts validated in this work were then used to create a computational realization of a 3D anthropomorphic bowtie filter capable of achieving a constant effective attenuation coefficient behind the entire field-of-view of an anthropomorphic breast phantom. CONCLUSIONS: Three different bowtie filter designs that vary in performance improvements were described and evaluated using computational and simulation techniques. Results indicate that the designs are robust against variations in breast diameter, breast composition, and tube voltage, and that the use of these filters can reduce patient dose and improve image quality compared to the no-bowtie-filter case.

Objective performance-based outcome measures (OMs) have the potential to provide unbiased and reproducible assessments of limb function. However, very few of these performance-based OMs have been validated for upper limb (UL) prosthesis users. OMs validated in other clinical populations (eg, neurologic or musculoskeletal conditions) could be used to fill gaps in existing performance-based OMs for UL amputees. Additionally, a joint review might reveal consistent gaps across multiple clinical populations. Therefore, the objective of this review was to systematically characterize prominent measures used in both sets of clinical populations with regard to (1) location of task performance around the body, (2) possible grips employed, (3) bilateral versus unilateral task participation, and (4) details of scoring mechanisms. A systematic literature search was conducted in EMBASE, Medline, and Cumulative Index to Nursing and Allied Health electronic databases for variations of the following terms: stroke , musculoskeletal dysfunction , amputation , prosthesis , upper limb , outcome , assessments . Articles were included if they described performance-based OMs developed for disabilities of the UL. Results show most tasks were performed with 1 hand in the space directly in front of the participant. The tip, tripod, and cylindrical grips were most commonly used for the specific tasks. Few measures assessed sensation and movement quality. Overall, several limitations in OMs were identified. The solution to these limitations may be to modify and validate existing measures originally developed for other clinical populations as first steps to more aptly measure prosthesis use while more complete assessments for UL prosthesis users are being developed. Level of Evidence Level III.

The functional capabilities of individuals with upper limb disabilities are assessed throughout rehabilitation and treatment regimens using functional outcome measures. For the upper limb amputee population, there are none which quantitatively take into account the quality of movement while an individual is performing tasks. In this paper, we demonstrate the use of an integrated movement analysis framework, based on motion capture and ground reaction force data, to capture quantitative information about how subjects complete a commonly used functional outcome measure, the Box and Blocks Test (BBT). In order to test the usefulness of the integrated movement analysis framework in capturing the quality of movements during task performance, a motion restriction was induced in able-bodied participants that reproduces some of the limitations imposed by conventional prosthetics. Each subject performed the BBT under normal conditions and also under the motion restriction condition. The motion capture and ground force plates captured movement that significantly differed between the two conditions, with the largest differences seen in shoulder motion, in the range of motions of head tilt and elbow flexion, and in the area of the center of pressure trajectory. These preliminary results show the feasibility of incorporating standardized, quantitative movement analysis into the assessment of function for those with an upper limb disability.

Background The Box and Block Test (BBT) is a functional outcome measure that is commonly used across multiple clinical populations due to its benefits of ease and speed of implementation; reliable, objective measurement; and repetition of motion. In this study, we introduce a novel outcome measure called the targeted BBT that allows for the study of initiation, grasping, and transport of objects, and also of object release. These modifications to the existing test may increase the ecological validity of the measure while still retaining the previously stated benefits of the standard BBT. Methods 19 able-bodied subjects performed the targeted BBT and two other standard tests. Using an integrated movement analysis framework based on motion capture and ground force data, quantitative information about how subjects completed these tests were captured. Kinematic parameters at the wrist, elbow, shoulder, thorax, and head, as well as measures of postural control, were calculated and statistically compared across the three tests. Results In general, the targeted BBT required significantly higher RoM at the elbow, shoulder, thorax and head when compared to standard tests. Peak angles at these joints were also higher during performance of the targeted BBT. Peak angles and RoM values for the targeted BBT were close to those found in studies of movements of able-bodied individuals performing activities of daily living. Conclusion The targeted BBT allows analysis of repetitive movements, and may more closely model common real-world object manipulation scenarios in which a user is required to control a movement from pick-up to release.

The sense of prosthesis embodiment, or the feeling that the device has been incorporated into a user's body image, may be enhanced by emerging technology such as invasive electrical stimulation for sensory feedback. In turn, prosthesis embodiment may be linked to increased prosthesis use and improved functional outcomes. We describe the development of a tool to assay artificial hand embodiment in a quantitative way in people with intact limbs, and characterize its operation. The system delivers temporally coordinated visual and tactile stimuli at a programmable latency while recording limb temperature. When programmed to deliver visual and tactile stimuli synchronously, recorded latency between the two was 33 ± 24 ms in the final pilot subject. This system enables standardized assays of the conditions necessary for prosthesis embodiment.

Scatter contamination of projection images in cone-beam computed tomography (CT) degrades the image quality. The use of bowtie filters in dedicated breast CT can decrease this scatter contribution. Three bowtie filter designs that compensate for one or more aspects of the beam-modifying effects due to differences in path length in a projection were studied. These designs have been investigated in terms of their ability to reduce the scatter contamination in projection images acquired in a dedicated breast CT geometry. The scatter magnitude was measured as the scatter-to-primary ratio (SPR) using experimental and Monte Carlo techniques for various breast phantom diameters and tube voltages. The results show that a 55% reduction in the center SPR value could be obtained with the bowtie filter designs. On average, the bowtie filters reduced the center SPR by approximately 18% over all breast diameters. The distribution of the scatter was calculated at a range of different locations to produce scatter distribution maps for all three bowtie filter designs. With the inclusion of the bowtie filters, the scatter distribution was more uniform for all breast diameters. The results of this study will be useful in designing scatter correction methods and understanding the benefits of bowtie filters in dedicated breast CT.