A Comparison of Approaches for Segmenting the Reaching and Targeting Motion Primitives in Functional Upper Extremity Reaching Tasks

There is growing interest in the kinematic analysis of human functional upper extremity movement (FUEM) for applications such as health monitoring and rehabilitation. Deconstructing functional movements into activities, actions, and primitives is a necessary procedure for many of these kinematic analyses. Advances in machine learning have led to progress in human activity and action recognition. However, their utility for analyzing the FUEM primitives of reaching and targeting during reach-to-grasp and reach-to-point tasks remains limited. Domain experts use a variety of methods for segmenting the reaching and targeting motion primitives, such as kinematic thresholds, with no consensus on what methods are best to use. Additionally, current studies are small enough that segmentation results can be manually inspected for correctness. As interest in FUEM kinematic analysis expands, such as in the clinic, the amount of data needing segmentation will likely exceed the capacity of existing segmentation workflows used in research laboratories, requiring new methods and workflows for making segmentation less cumbersome. This paper investigates five reaching and targeting motion primitive segmentation methods in two different domains (haptics simulation and real world) and how to evaluate these methods. This work finds that most of the segmentation methods evaluated perform reasonably well given current limitations in our ability to evaluate segmentation results. Furthermore, we propose a method to automatically identify potentially incorrect segmentation results for further review by the human evaluator. Clinical impact: This work supports efforts to automate aspects of processing upper extremity kinematic data used to evaluate reaching and grasping, which will be necessary for more widespread usage in clinical settings

Factors associated with interest in novel interfaces for upper limb prosthesis control

<div><p>Background</p><p>Surgically invasive interfaces for upper limb prosthesis control may allow users to operate advanced, multi-articulated devices. Given the potential medical risks of these invasive interfaces, it is important to understand what factors influence an individual’s decision to try one.</p><p>Methods</p><p>We conducted an anonymous online survey of individuals with upper limb loss. A total of 232 participants provided personal information (such as age, amputation level, etc.) and rated how likely they would be to try noninvasive (myoelectric) and invasive (targeted muscle reinnervation, peripheral nerve interfaces, cortical interfaces) interfaces for prosthesis control. Bivariate relationships between interest in each interface and 16 personal descriptors were examined. Significant variables from the bivariate analyses were then entered into multiple logistic regression models to predict interest in each interface.</p><p>Results</p><p>While many of the bivariate relationships were significant, only a few variables remained significant in the regression models. The regression models showed that participants were more likely to be interested in all interfaces if they had unilateral limb loss (p ≤ 0.001, odds ratio ≥ 2.799). Participants were more likely to be interested in the three invasive interfaces if they were younger (p < 0.001, odds ratio ≤ 0.959) and had acquired limb loss (p ≤ 0.012, odds ratio ≥ 3.287). Participants who used a myoelectric device were more likely to be interested in myoelectric control than those who did not (p = 0.003, odds ratio = 24.958).</p><p>Conclusions</p><p>Novel prosthesis control interfaces may be accepted most readily by individuals who are young, have unilateral limb loss, and/or have acquired limb loss However, this analysis did not include all possible factors that may have influenced participant’s opinions on the interfaces, so additional exploration is warranted.</p></div

Summary of logistic regression model predicting interest in CI.

<p>Summary of logistic regression model predicting interest in CI.</p

Sample characteristics for all ordinal and nominal factors.

<p>Sample characteristics for all ordinal and nominal factors.</p

Median values of continuous and ordinal factors for interested and uninterested participants.

<p>Median values of continuous and ordinal factors for interested and uninterested participants.</p

Additional categories of functions that were not already included in the survey.

<p>Participants listed additional functions that they wanted to perform with a prosthesis that were not already included in the survey. In cases where a participant mentioned multiple functions that could be classified into a single category, the participant was counted only once for that category.</p

Summary of logistic regression model predicting interest in PNI.

<p>Summary of logistic regression model predicting interest in PNI.</p

Summary of logistic regression model predicting interest in TMR.

<p>Summary of logistic regression model predicting interest in TMR.</p

Distribution of interest in peripheral nerve interfaces depending the availability of self-selected functions.

<p>Participants indicated their interest in trying peripheral nerve interfaces if they could perform additional functions with a prosthesis that were not already included in the survey (vertical axis). These responses are presented in relation to the most interested response from the other six functions included in the survey (horizontal axis). The bubbles show the number of participants who gave each combination of responses. Dashed lines indicate a change in response. Green shading designates a change from an uninterested response (“very unlikely”, “unlikely” or “maybe”) to an interested response (“likely” or “very likely”). Red shading designates a change from an interested to uninterested response.</p

Discriminative power of the logistic regression models.

<p>(A) ROC curves for each regression model. The diagonal reference line indicates performance according to chance. (B) Area under the ROC curve for each regression model. The horizontal reference line indicates performance according to chance. Error bars represent 95% confidence intervals. (MYO = myoelectric control, TMR = targeted muscle reinnervation, PNI = peripheral nerve interfaces, CI = cortical interfaces).</p