Abstract-This paper proposes the CPG synergy model -a biomimetic rhythm generator model based on central pattern generators (CPGs) and muscle synergy theory to enable evaluation of rhythmic motions with non-stationary characteristics such as human finger tapping movements. The model consists of multiple CPGs to approximate the complex rhythmic movement of humans, and has the potential to allow evaluation of abnormal movements in patients with motor function impairments such as Parkinson's disease (PD).To verify the validity of the proposed model, comparison experiments were conducted using model parameters (i.e., synergies, weight coefficients and time-shift parameters) extracted from finger tapping movements performed by individuals in a healthy subject group and a PD patient group. The results showed that the number of synergies, the second moment of synergy shapes and the coefficient of variation of maximum weight coefficients show significant differences for each subject group, and indicated that the model could be used to evaluate irregular rhythmic movements as well as regular ones.
Abstract-This paper proposes a method of estimating fingertip forces in finger tapping movements based on human fingerpad characteristics. Since the human fingerpad exhibits elasticity, the proposed technique recreates the relationship between the fingertip force and the displacement generated between the thumb and index fingerpads as a fingerpadstiffness model. Then, using this model, the force between the two fingertips (the fingertip force) can be estimated from the measured fingerpad deformation only. As the method does not require any sensors to be attached to the finger contact surface to measure fingertip force, it can be used to evaluate the tendency of force in natural and unconstrained finger tapping movements conducted by the subject.In the experiments conducted, fingertip forces and the displacement of the two fingerpads generated when the subjects pinched and pushed a force sensor with the thumb and index finger were measured to approximate the relationships between fingerpad force and deformation. The results indicated that human fingerpad characteristics can be expressed using a fingerpad-stiffness function (including an exponential function), and that fingerpad forces can be estimated using the proposed model. Furthermore, comparison between a Parkinson's disease (PD) patient and a healthy subject confirmed differences in the finger tapping forces for each. This implies the possibility of assessing motor function in PD patients using the finger tapping force evaluation method proposed in this paper.
We tested the repeatability of a finger tapping device with magnetic sensors to determine its reliability. This device, which was developed to assist in the diagnosis of movement disorders such as Parkinson's disease (PD) and strokes, was used to measure the distance between the thumb and index fingers during finger tapping movements (repeatedly opening and closing the gap between the thumb and forefinger). We evaluated three types of repeatability based on the interclass correlation coefficient (ICC) and analysis of variance (ANOVA), i.e., repeatability when movements were measured at different times, that when movements were measured with different devices, and that when movements were measured by different measurers. We analyzed these three types for three finger tapping tasks on both hands for 21 characteristics calculated from finger tapping waveforms. As a result, repeatability when using different devices was high regardless of the task or which hand was used. Repeatability when movements were measured at different times and when different measurers were used was high in some tasks, but not all. One of the finger tapping tasks (finger tapping movement with the largest amplitude and highest velocity), which is used in a conventional method of diagnosing PD (UPDRS), did not have sufficient repeatability, while the other tasks exhibited high repeatability. These results clearly demonstrate that this device is extremely reliable.
We tested the repeatability of a finger tapping device with magnetic sensors to determine its reliability. This device, which was developed to assist in the diagnosis of movement disorders such as Parkinson's disease (PD) and strokes, measures the distance between the first and index fingers during finger tapping movements (opening and closing the fingers repeatedly). We evaluated three types of repeatability based on ICC (interclass correlation coefficient) and Welch's test (test for equal means in a oneway layout): repeatability when measured at different times, when using different devices, and when using different measurers. We calculated these three types for three finger tapping tasks on both hands for 21 characteristics calculated from finger tapping waveforms. Results demonstrated that the repeatability when using different devices is high regardless of the task or hand. The repeatability when measuring at different times and when using different measurers is high at some tasks, but not all. One of the finger tapping tasks (finger tapping movement with the largest amplitude and highest velocity), which is used in a conventional PD diagnosis method (UPDRS), does not have enough repeatability, while other tasks show high repeatability. Results also showed that five characteristics have the highest repeatability (ICC ≥ 0.5 or significance probability of Welch's test ≥ 5% in all tasks): "total moving distance," "average of local minimum acceleration in opening motion," "average of local minimum acceleration in closing motion," "average of local maximum distance" and "average of local minimum velocity". These results clearly demonstrate the strong repeatability of this device and lead to more precise diagnosis of movement disorders.
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