Remote-Use Applications of the Amyotrophic Lateral Sclerosis Functional Rating Scale-Revised Clinical Outcome Assessment Tool: A Scoping Review.

OBJECTIVES: In 2021, the US Congress passed the Accelerating Access to Critical Therapies for Amyotrophic Lateral Sclerosis Act. The law encourages development of "tools, methods, and processes" to improve clinical trial efficiency for neurodegenerative diseases. The Amyotrophic Lateral Sclerosis Functional Rating Scale-Revised (ALSFRS-R) is an outcome measure administered during in-person clinic visits and used to support investigational studies for persons living with amyotrophic lateral sclerosis. Availability of a standardized, remote-use version of the ALSFRS-R may promote more inclusive, decentralized clinical trials. A scoping literature review was conducted to identify existing remote-use ALSFRS-R tools, synthesize feasibility and comparability of administration modes, and summarize barriers and facilitators to inform development of a standardized remote-use ALSFRS-R tool. METHODS: Included studies reported comparisons between remote and in-person, clinician-reported, ALSFRS-R administration and were published in English (2002-2022). References were identified by searching peer-reviewed and gray literature. Twelve studies met the inclusion criteria and were analyzed to compare findings within and across modes of administration. RESULTS: Remote modes of ALSFRS-R administration were categorized into 4 nonmutually exclusive categories: telephone (n = 6), videoconferencing (n = 3), computer or online platforms (n = 3), mobile applications and wearables (n = 2), and 1 unspecified telemedicine modality (n = 1). Studies comparing in-person to telephone or videoconferencing administration reported high ALSFRS-R rating correlations and nonsignificant between-mode differences. CONCLUSIONS: There is insufficient information in the ALSFRS-R literature to support remote clinician administration for collecting high quality data. Future research should engage persons living with amyotrophic lateral sclerosis, care partners, and providers to develop a standardized remote-use ALSFRS-R version.

Usability and acceptability of virtual reality for chronic pain management among diverse patients in a safety-net setting: a qualitative analysis.

Objective: The aim of this study was to understand the usability and acceptability of virtual reality (VR) among a racially and ethnically diverse group of patients who experience chronic pain. Materials and Methods: Using the Technology Acceptance Model theory, we conducted semistructured interviews and direct observation of VR use with English-speaking patients who experience chronic pain treated in a public healthcare system (n = 15), using a commercially available VR technology platform. Interviews included questions about current pain management strategies, technology use, experiences and opinions with VR, and motivators for future use. Results: Before the study, none of the 15 participants had heard about or used VR for pain management. Common motivators for VR use included a previous history of substance use and having exhausted many other options to manage their pain and curiosity. Most participants had a positive experience with VR and 47% found that the VR modules distracted them from their pain. When attempting the navigation-based usability tasks, most participants (73%-92%) were able to complete them independently. Discussion: VR is a usable tool for diverse patients with chronic pain. Our findings suggest that the usability of VR is not a barrier and perhaps a focus on improving the accessibility of VR in safety-net settings is needed to reduce disparities in health technology use. Conclusions: The usability and acceptability of VR are rarely studied in diverse patient populations. We found that participants had a positive experience using VR, showed interest in future use, and would recommend VR to family and friends.

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.

Evaluation Challenges for the Application of Extended Reality Devices in Medicine.

Augmented and virtual reality devices are being actively investigated and implemented for a wide range of medical uses. However, significant gaps in the evaluation of these medical devices and applications hinder their regulatory evaluation. Addressing these gaps is critical to demonstrating the devices' safety and effectiveness. We outline the key technical and clinical evaluation challenges discussed during the US Food and Drug Administration's public workshop, "Medical Extended Reality: Toward Best Evaluation Practices for Virtual and Augmented Reality in Medicine" and future directions for evaluation method development. Evaluation challenges were categorized into several key technical and clinical areas. Finally, we highlight current efforts in the standards communities and illustrate connections between the evaluation challenges and the intended uses of the medical extended reality (MXR) devices. Participants concluded that additional research is needed to assess the safety and effectiveness of MXR devices across the use cases.

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.

Understanding the Relationship Between Patient-Reported Function and Actual Function in the Upper Limb Prosthesis User Population: A Preliminary Study.

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.

Evaluating the Impact of IMU Sensor Location and Walking Task on Accuracy of Gait Event Detection Algorithms.

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.

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.

Application of machine learning to the identification of joint degrees of freedom involved in abnormal movement during upper limb prosthesis use.

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.

Optimization of IMU Sensor Placement for the Measurement of Lower Limb Joint Kinematics.

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.

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.

Computational musculoskeletal modeling of compensatory movements in the upper limb.

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.

Kinematic analysis of motor learning in upper limb body-powered bypass prosthesis training.

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.

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.

Creating a standardized, quantitative training protocol for upper limb bypass prostheses.

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.

Deployment of Mobile EEG Technology in an Art Museum Setting: Evaluation of Signal Quality and Usability.

Electroencephalography (EEG) has emerged as a powerful tool for quantitatively studying the brain that enables natural and mobile experiments. Recent advances in EEG have allowed for the use of dry electrodes that do not require a conductive medium between the recording electrode and the scalp. The overall goal of this research was to gain an understanding of the overall usability and signal quality of dry EEG headsets compared to traditional gel-based systems in an unconstrained environment. EEG was used to collect Mobile Brain-body Imaging (MoBI) data from 432 people as they experienced an art exhibit in a public museum. The subjects were instrumented with either one of four dry electrode EEG systems or a conventional gel electrode EEG system. Each of the systems was evaluated based on the signal quality and usability in a real-world setting. First, we describe the various artifacts that were characteristic of each of the systems. Second, we report on each system's usability and their limitations in a mobile setting. Third, to evaluate signal quality for task discrimination and characterization, we employed a data driven clustering approach on the data from 134 of the 432 subjects (those with reliable location tracking information and usable EEG data) to evaluate the power spectral density (PSD) content of the EEG recordings. The experiment consisted of a baseline condition in which the subjects sat quietly facing a white wall for 1 min. Subsequently, the participants were encouraged to explore the exhibit for as long as they wished (piece-viewing). No constraints were placed upon the individual in relation to action, time, or navigation of the exhibit. In this freely-behaving approach, the EEG systems varied in their capacity to record characteristic modulations in the EEG data, with the gel-based system more clearly capturing stereotypical alpha and beta-band modulations.