Evaluation and Training of Human Finger Tapping Movements

Non

Measurement and Evaluation of Finger Tapping Movements Using Log-linearized Gaussian Mixture Networks

This paper proposes a method to quantitatively measure and evaluate finger tapping movements for the assessment of motor function using log-linearized Gaussian mixture networks (LLGMNs). First, finger tapping movements are measured using magnetic sensors, and eleven indices are computed for evaluation. After standardizing these indices based on those of normal subjects, they are input to LLGMNs to assess motor function. Then, motor ability is probabilistically discriminated to determine whether it is normal or not using a classifier combined with the output of multiple LLGMNs based on bagging and entropy. This paper reports on evaluation and discrimination experiments performed on finger tapping movements in 33 Parkinson’s disease (PD) patients and 32 normal elderly subjects. The results showed that the patients could be classified correctly in terms of their impairment status with a high degree of accuracy (average rate: 93.1 ± 3.69%) using 12 LLGMNs, which was about 5% higher than the results obtained using a single LLGMN

Micromanipulation System Capable of Simultaneously Presenting High-Resolution and Large Field-of-View Images in Real-Time

Microinjection technology is widely used in biotechnological processes such as gene manipulation and microinsemination. Generally, microinjection is performed under an optical microscope while viewing the video of the targets in real-time. In the microinjection process, multiple oocytes must be placed in the same droplet, for which the injection must be performed multiple times. Furthermore, the target must be observed at different magnifications during injection and the oocyte transfer operation. Hence, the operators are required to change the magnification and light intensity repeatedly. These tasks are complicated and are a burden for the operators. Thus, the accuracy, reproducibility, and productivity of microinjection depend on the operator’s skill level. In this study, to reduce the burden on the operator and simplify the operation of microinjection, we propose a micromanipulation system that enables both wide-range and high-resolution video presentation with free viewpoint selection. The proposed micromanipulation system is based on a view-expansion microscope system with simultaneous multi-view imaging using a galvanometer mirror and a high-speed vision system. We verify the effectiveness of the proposed system by evaluating the resolution of images presented to the operator and through the experiments involving moving microbeads performed by inexperienced subjects. The experimental results indicate that our micromanipulation system streamlines the task of micromanipulation by eliminating the need to change the objective lens

Step phase regulation.

<p>Linear regression analyses between the relative step phase of each step (<i>φ_i</i>) and the phase change from each step to the following step () in a PD−FOG patient (left) and a FOG−P patient (right) during the ‘Go’ (upper) and ‘Back’ (lower) portions of the walking task. The analyses were performed separately for the left-to-right phase changes (blue) and for the right-to-left phase changes (red). The slope of the relation was smaller and the noise in the step phase regulation was larger in FOG−P than in PD−FOG.</p