Factors influencing perceived function in the upper limb prosthesis user population.

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 < .001) and pain (p = .031) scores (with higher pain scores signifying less pain), the mean of perceived function increases by 0.66 and 0.47 units, respectively. Conversely, for individuals with elbow disarticulation, transhumeral, shoulder disarticulation, and interscapulothoracic amputations, the mean of perceived function decreases by 22.02 units (p = .006). CONCLUSIONS: Based on our sample, perceived function is significantly associated with satisfaction, pain, and amputation level. These findings could potentially help to inform initial clinical approach and targeted outcomes for patients based on these factors.

Comparison of Motion Analysis Systems in Tracking Upper Body Movement of Myoelectric Bypass Prosthesis Users.

Current literature lacks a comparative analysis of different motion capture systems for tracking upper limb (UL) movement as individuals perform standard tasks. To better understand the performance of various motion capture systems in quantifying UL movement in the prosthesis user population, this study compares joint angles derived from three systems that vary in cost and motion capture mechanisms: a marker-based system (Vicon), an inertial measurement unit system (Xsens), and a markerless system (Kinect). Ten healthy participants (5F/5M; 29.6 ± 7.1 years) were trained with a TouchBionic i-Limb Ultra myoelectric terminal device mounted on a bypass prosthetic device. Participants were simultaneously recorded with all systems as they performed standardized tasks. Root mean square error and bias values for degrees of freedom in the right elbow, shoulder, neck, and torso were calculated. The IMU system yielded more accurate kinematics for shoulder, neck, and torso angles while the markerless system performed better for the elbow angles. By evaluating the ability of each system to capture kinematic changes of simulated upper limb prosthesis users during a variety of standardized tasks, this study provides insight into the advantages and limitations of using different motion capture technologies for upper limb functional assessment.

Quantifying Signal Quality From Unimodal and Multimodal Sources: Application to EEG With Ocular and Motion Artifacts.

With prevalence of electrophysiological data collected outside of the laboratory from portable, non-invasive modalities growing at a rapid rate, the quality of these recorded data, if not adequate, could affect the effectiveness of medical devices that depend of them. In this work, we propose novel methods to evaluate electrophysiological signal quality to determine how much of the data represents the physiological source of interest. Data driven models are investigated through Bayesian decision and deep learning-based methods to score unimodal (signal and noise recorded on same device) and multimodal (signal and noise each recorded from different devices) data, respectively. We validate these methods and models on three electroencephalography (EEG) data sets (N = 60 subjects) to score EEG quality based on the presence of ocular artifacts with our unimodal method and motion artifacts with our multimodal method. Further, we apply our unimodal source method to compare the performance of two different artifact removal algorithms. Our results show we are able to effectively score EEG data using both methods and apply our method to evaluate the performance of other artifact removal algorithms that target ocular artifacts. Methods developed and validated here can be used to assess data quality and evaluate the effectiveness of certain noise-reduction algorithms.

Deep learning and feature based medication classifications from EEG in a large clinical data set.

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.

Comparison of DEKA Arm and Body-Powered Upper Limb Prosthesis Joint Kinematics.

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.

Assessing kinematic variability during performance of Jebsen-Taylor Hand Function Test.

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 < 0.07; SDmax > 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.

Capacity Assessment of Prosthetic Performance for the Upper Limb (CAPPFUL): Characterization of Normative Kinematics and Performance.

BACKGROUND: 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. OBJECTIVE: To enhance the scoring approach for the maladaptive compensatory movement domain of the CAPPFUL by defining normative kinematic movement and characterizing variability and repeatability. DESIGN: Clinical measurement. SETTING: Laboratories at the U.S. Food and Drug Administration (FDA) and University of Texas-Arlington. PARTICIPANTS: Convenience sample of 20 participants with no upper limb (UL) disability or impairment. INTERVENTIONS: Not applicable. MAIN OUTCOME MEASUREMENTS: 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. RESULTS: 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 was <20°. The approach for completing CAPPFUL 1 - Weights in crate and CAPPFUL 4 - Pick up dice was generally similar across participants (CMCadj >0.4). For other tasks, however, different approaches across participants at the torso and shoulder joint can be seen. CONCLUSION(S): 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.

Consistency of quantitative electroencephalography features in a large clinical data set.

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.

Evaluation of Performance-Based Outcome Measures for the Upper Limb: A Comprehensive Narrative Review.

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.

An Integrated Movement Analysis Framework to Study Upper Limb Function: A Pilot Study.

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.

Your Brain on Art: Emergent Cortical Dynamics During Aesthetic Experiences.

The brain response to conceptual art was studied with mobile electroencephalography (EEG) to examine the neural basis of aesthetic experiences. In contrast to most studies of perceptual phenomena, participants were moving and thinking freely as they viewed the exhibit The Boundary of Life is Quietly Crossed by Dario Robleto at the Menil Collection-Houston. The brain activity of over 400 subjects was recorded using dry-electrode and one reference gel-based EEG systems over a period of 3 months. Here, we report initial findings based on the reference system. EEG segments corresponding to each art piece were grouped into one of three classes (complex, moderate, and baseline) based on analysis of a digital image of each piece. Time, frequency, and wavelet features extracted from EEG were used to classify patterns associated with viewing art, and ranked based on their relevance for classification. The maximum classification accuracy was 55% (chance = 33%) with delta and gamma features the most relevant for classification. Functional analysis revealed a significant increase in connection strength in localized brain networks while subjects viewed the most aesthetically pleasing art compared to viewing a blank wall. The direction of signal flow showed early recruitment of broad posterior areas followed by focal anterior activation. Significant differences in the strength of connections were also observed across age and gender. This work provides evidence that EEG, deployed on freely behaving subjects, can detect selective signal flow in neural networks, identify significant differences between subject groups, and report with greater-than-chance accuracy the complexity of a subject's visual percept of aesthetically pleasing art. Our approach, which allows acquisition of neural activity "in action and context," could lead to understanding of how the brain integrates sensory input and its ongoing internal state to produce the phenomenon which we term aesthetic experience.

Corrigendum: Your Brain on Art: Emergent Cortical Dynamics During Aesthetic Experiences.

[This corrects the article on p. 626 in vol. 9, PMID: 26635579.].