Separation method of bending and torsion in shape sensing based on FBG sensors array.

This paper presents a theoretical method for separating bending and torsion of shape sensing sensor to improve sensing accuracy during its deformation. We design a kind of shape sensing sensor by encapsulating three fibers on the surface of a flexible rod and forming a triangular FBG sensors array. According to the configuration of FBG sensors array, we derive the relationship between bending curvature and bending strain, and set up a function about the packaging angle of FBG sensor and strain induced by torsion under different twist angles. Combined with the influence of bending and torsion on strain, we establish a nonlinear matrix equation resolving three unknown parameters including maximum strain, bending direction and wavelength shift induced by torsion and temperature. The three parameters are sufficient to separate bending and torsion, and acquire two scalar functions including curvature and torsion, which could describe 3D shape of rod according to Frenet-Serret formulas. Experimental results show that the relative average error of measurement about maximum strain, bending direction is respectively 2.65% and 0.86% when shape-sensing sensor is bent into an arc with a radius of 260 mm. The separating method also applied to 2D shape and 3D shape of reconstruction, and the absolute spatial position maximum error is respectively 3.79mm and 11.10mm when shape-sensing sensor with length 500mm is bent into arc shape with a radius 260mm and helical curve. The experiment results verify the feasibility of separating method, which would provide effective parameters for precise 3D reconstruction model of shape sensing sensor.

Advanced Hand Gesture Prediction Robust to Electrode Shift with an Arbitrary Angle.

Recent advances in myoelectric controlled techniques have made the surface electromyogram (sEMG)-based sensing armband a promising candidate for acquiring bioelectric signals in a simple and convenient way. However, inevitable electrode shift as a non-negligible defect commonly causes a trained classifier requiring continuous recalibrations. In this study, a novel hand gesture prediction is firstly proposed; it is robust to electrode shift with arbitrary angle. Unlike real-time recognition which outputs target gestures only after the termination of hand motions, our proposed advanced prediction can provide the same results, even before the completion of signal collection. Moreover, by combining interpolated peak location and preset synchronous gesture, the developed simplified rapid electrode shift detection and correction at random rather than previous fixed angles are realized. Experimental results demonstrate that it is possible to achieve both electrode shift detection with high precision and gesture prediction with high accuracy. This study provides a new insight into electrode shift robustness which brings gesture prediction a step closer to practical applications.

Real-Time Surface EMG Pattern Recognition for Hand Gestures Based on an Artificial Neural Network.

In recent years, surface electromyography (sEMG) signals have been increasingly used in pattern recognition and rehabilitation. In this paper, a real-time hand gesture recognition model using sEMG is proposed. We use an armband to acquire sEMG signals and apply a sliding window approach to segment the data in extracting features. A feedforward artificial neural network (ANN) is founded and trained by the training dataset. A test method is used in which the gesture will be recognized when recognized label times reach the threshold of activation times by the ANN classifier. In the experiment, we collected real sEMG data from twelve subjects and used a set of five gestures from each subject to evaluate our model, with an average recognition rate of 98.7% and an average response time of 227.76 ms, which is only one-third of the gesture time. Therefore, the pattern recognition system might be able to recognize a gesture before the gesture is completed.

Age- and Parkinson's disease-related evaluation of gait by General Tau Theory.

The degeneration of postural control in the elderly and patients with Parkinson's disease (PD) can be debilitating and may lead to increased fall risk. This study evaluated the changes in postural control during gait affected by PD and aging using a new method based on the General Tau Theory. Fifteen patients with PD, 11 healthy old adults (HOs), and 15 healthy young adults (HYs) were recruited. Foot trajectories of each participant were monitored during walking by a three-camera Optotrak Certus(®) motion capture system. The anteroposterior direction of foot movement during stepping was analyzed by tau-G and tau-J guidance strategies. Two linear regression analyses suggested that the tau of the step-gap was strongly coupled onto the tau-J guidance during walking. The regression slope K could estimate the coupling ratio in the tau-coupling equation which reflects the performance of postural control during gait. The mean K value for the PD group, which was highest among the three groups, was approximately 0.5. Therefore, participants in the PD group walked with the poorest postural control and exhibited a relatively hard contact with the endpoint during stepping when compared with those in the HO and HY groups. The HY and HO groups obtained mean K values significantly lower than 0.5, which indicated that the gait was well controlled and ended at low speed with low deceleration. However, the HO group showed a decreased tendency for postural control, in which the mean K value was significantly higher than that of the HY group. The K value was moderately positively correlated with the double support time and negatively correlated with the stride length and walking speed. The tau-J coupling ratio can provide additional insight into gait disturbances and may serve as a reliable, objective, and quantitative tool to evaluate dynamic postural control during walking.

Computer-aided diagnosis expert system for cerebrovascular diseases.

OBJECTIVE: To establish an expert diagnosis system for cerebrovascular diseases (CVDs) and assess accuracy of the diagnosis system. METHODS: An expert diagnosis system for CVDs was established and evaluated using actual clinical cases. RESULTS: An expert diagnosis system for CVDs was established and tested in 319 clinical patients. Diagnosis accordance was obtained in 307 patients (the diagnosis accordance rate was 96.2%). Involved were 223, 7, 23, 54 and 12 patients with cerebral thrombosis, cerebral embolism, transient ischemic attack, cerebral hemorrhage and subarachnoid hemorrhage, respectively; and diagnosis accordance was obtained in 219 (98.2%), 6 (85.7%), 23 (100%), 48 (88.9%) and 11 (91.7%), respectively. CONCLUSION: Overall, the case analysis results support and demonstrate the diagnostic reasoning accuracy of the expert diagnosis system for CVDs. With the expert diagnosis system, medical experts' diagnosis of CVDs can be effectively mimicked and auxiliary diagnosis of CVDs has been preliminarily realized, laying a foundation for increasing the diagnostic accuracy of clinical diagnoses as it pertains to CVDs.

[Research of contour analysis on endoscopy navigation].

In this paper, a new method of contour analysis is presented to find path in endoscopy navigation. At first, edge detection is used to endoscope image. Then we do skeleton extraction and connect the edge skeleton to contours by grouping criteria. At last we can get navigation information by analyzing the contours. Experiments show that this algorithm has good precision and stability.