Quantitative evidence of kinematics and functional differences in different graded trigger fingers.

BACKGROUND: Clinical diagnosis and classification of trigger fingers is traditionally based on physical examinations and certain obvious symptoms. However, it might lack quantitative evidence to describe the different graded trigger digits. This study provides quantitative evidence of kinematics and functional differences among different graded trigger fingers based on Froimson's classification. METHODS: Forty-seven patients with fifty-five trigger fingers and graded twenty-three, eleven, and twenty-one fingers as grades II, III, and IV, respectively. The QuickDASH questionnaire evaluated the subject's self-perception of hand symptoms and functions. The study measured maximal workspace of the fingertip motion and range of motion of the finger joints during an assigned tendon-gliding task using an electromagnetic tracking device. In addition, R(alpha), defined as the ratio range of angular acceleration during finger extension to the range during finger flexion of each joint, quantified the triggering effect. FINDINGS: The QuickDASH score results show that functional performances have significant differences among three grades (P<0.05). Workspace, range of motion of proximal interphalangeal joint and R(alpha) of proximal interphalangeal and distal interphalangeal joint of trigger fingers also significantly differ among three grades (P<0.05). These findings quantitatively show that trigger fingers in different impairment levels have different kinematics and functional performances. INTERPRETATION: The results serve as evidence-based knowledge for clinics. The more practical and immediate application of this study would be to facilitate the assessment, design and execution of rehabilitation for patients with trigger fingers.

Model analyses of visual biofeedback training for EEG-based brain-computer interface.

The primary goal of this study was to construct a simulation model of a biofeedback brain-computer interface (BCI) system to analyze the effect of biofeedback training on BCI users. A mathematical model of a man-machine visual-biofeedback BCI system was constructed to simulate a subject using a BCI system to control cursor movements. The model consisted of a visual tracking system, a thalamo-cortical model for EEG generation, and a BCI system. The BCI system in the model was realized for real experiments of visual biofeedback training. Ten sessions of visual biofeedback training were performed in eight normal subjects during a 3-week period. The task was to move a cursor horizontally across a screen, or to hold it at the screen's center. Experimental conditions and EEG data obtained from real experiments were then simulated with the model. Three model parameters, representing the adaptation rate of gain in the visual tracking system and the relative synaptic strength between the thalamic reticular and thalamo-cortical cells in the Rolandic areas, were estimated by optimization techniques so that the performance of the model best fitted the experimental results. The serial changes of these parameters over the ten sessions, reflecting the effects of biofeedback training, were analyzed. The model simulation could reproduce results similar to the experimental data. The group mean success rate and information transfer rate improved significantly after training (56.6 to 81.1% and 0.19 to 0.76 bits/trial, respectively). All three model parameters displayed similar and statistically significant increasing trends with time. Extensive simulation with systematic changes of these parameters also demonstrated that assigning larger values to the parameters improved the BCI performance. We constructed a model of a biofeedback BCI system that could simulate experimental data and the effect of training. The simulation results implied that the improvement was achieved through a quicker adaptation rate in visual tracking gain and a larger synaptic gain from the visual tracking system to the thalamic reticular cells. In addition to the purpose of this study, the constructed biofeedback BCI model can also be used both to investigate the effects of different biofeedback paradigms and to test, estimate, or predict the performances of other newly developed BCI signal processing algorithms.

Functional workspace for precision manipulation between thumb and fingers in normal hands.

Prehensile functions of hand are based on thumb-finger relationships which are regarded as an essential element in various manipulations of our daily living activities. Although the maximal workspace provides clinicians a way to comprehend the ranges of digital movements, little is known about the "functional workspace" based on thumb-finger relationships. This study defines the functional workspace of the precision thumb-finger grasp as the range of all possible positions in which thumb-tip and each fingertip can simultaneously contact each other. We present a quantitative method for measuring the functional workspace of the human hand. The maximal motion trajectories of thumb-tip and fingertips of twenty subjects were recorded using a video-capture system. The functional workspace of the precision manipulation was calculated via numerical methods based on the maximal workspaces obtained of the thumb-tip and fingertip motions. The ratios of the functional workspace with respect to the maximal workspace of the index, middle, ring and little fingers were calculated as 33.7%, 27.1%, 23.5% and 19.1%, respectively. Although the present approach is still a descriptive work which might require more validations or evidences to justify, the results obtained may become normal standards for practical use in objective handicapped authentications, insurance claims, and rehabilitation programs as well as criteria for ergonomic design considerations in the near future.