Analyzing finger interdependencies during the Purdue Pegboard Test and comparative activities of daily living.

STUDY DESIGN: Bench and cross-sectional study. INTRODUCTION: Information obtained from dexterity tests is an important component of a comprehensive examination of the hand. PURPOSE OF THE STUDY: To analyze and compare finger interdependencies during the performance of the Purdue Pegboard Test (PBT) and comparative daily tasks. METHODS: A method based on the optoelectronic kinematic analysis of the precision grip style and on the calculation of cross-correlation coefficients between relevant joint angles, which provided measures of the degree of finger coordination, was conducted on 10 healthy participants performing the PBT and 2 comparative daily living tasks. RESULTS: Daily tasks showed identifiable interdependencies patterns between the metacarpophalangeal joints of the fingers involved in the grip. Tasks related to activities of daily living resulted in significantly higher cross-correlation coefficients across subjects and movements during the formation and manipulation phases of the tasks (0.7-0.9), whereas the release stage produced significantly lower movement correlation values (0.3-0.7). Contrarily, the formation and manipulation stages of the PBT showed low finger correlation across most subjects (0.2-0.6), whereas the release stage resulted in the highest values for all relevant movements (0.65-0.9). DISCUSSION: Interdependencies patterns were consistent for the activities of daily living but differ from the patterns observed from the PBT. CONCLUSIONS: The PBT does not compare well with the whole range of finger movements that account for hand performance during daily tasks. LEVEL OF EVIDENCE: Not applicable.

Simultaneous Force Regression and Movement Classification of Fingers via Surface EMG within a Unified Bayesian Framework.

This contribution presents a novel methodology for myolectric-based control using surface electromyographic (sEMG) signals recorded during finger movements. A multivariate Bayesian mixture of experts (MoE) model is introduced which provides a powerful method for modeling force regression at the fingertips, while also performing finger movement classification as a by-product of the modeling algorithm. Bayesian inference of the model allows uncertainties to be naturally incorporated into the model structure. This method is tested using data from the publicly released NinaPro database which consists of sEMG recordings for 6 degree-of-freedom force activations for 40 intact subjects. The results demonstrate that the MoE model achieves similar performance compared to the benchmark set by the authors of NinaPro for finger force regression. Additionally, inherent to the Bayesian framework is the inclusion of uncertainty in the model parameters, naturally providing confidence bounds on the force regression predictions. Furthermore, the integrated clustering step allows a detailed investigation into classification of the finger movements, without incurring any extra computational effort. Subsequently, a systematic approach to assessing the importance of the number of electrodes needed for accurate control is performed via sensitivity analysis techniques. A slight degradation in regression performance is observed for a reduced number of electrodes, while classification performance is unaffected.

Assessment of the Sheffield Support Snood, an innovative cervical orthosis designed for people affected by neck muscle weakness.

BACKGROUND: This study aimed at quantifying the biomechanical features of the Sheffield Support Snood, a cervical orthosis specifically designed for patients with neck muscle weakness. The orthosis is designed to be adaptable to a patient's level of functional limitation using adjustable removable supports, which contribute support and restrict movement only in desired anatomical planes. METHODS: The snood was evaluated along with two commercially available orthoses, the Vista and Headmaster, in a series of flexion, extension, axial-rotation and lateral flexion movements. Characterization was performed with twelve healthy participants with and without the orthoses. Two inertial-magneto sensors, placed on the forehead and sternum, were used to quantify the neck's range of motion. FINDINGS: In its less supportive configuration, the snood was effective in limiting movements to the desired planes, preserving free movement in other planes. The Headmaster was only effective in limiting flexion. The range of motion achieved with the snood in its rigid configuration was equivalent (P>0.05, effect size<0.4) to that achieved with the Vista, both in trials performed reaching the maximum amplitude (range of motion reduction: 25%-34% vs 24%-47%) and at maximum speed (range of motion reduction: 24%-29% vs 25%-43%). INTERPRETATION: The Sheffield Support Snood is effectively adaptable to different tasks and, in its most supportive configuration, offers a support comparable to the Vista, but providing a less bulky structure. The chosen method is suitable for the assessment of range of motions while wearing neck orthoses and is easily translatable in a clinical context.