Qichuan Ding

A State-Space EMG Model for the Estimation of Continuous Joint Movements

A state-space electromyography (EMG) model is developed for continuous motion estimation of human limb in this paper. While the general Hill-based muscle model (HMM) estimates only joint torque from EMG signals in an “open-loop” form, we integrate the forward dynamics of human joint movement into the HMM, and such an extended HMM can be used to estimate the joint motion states directly. EMG features are developed to construct measurement equations for the extended HMM to form a state-space model. With the state-space HMM, a normal closed-loop prediction-correction approach such as the Kalman-type algorithm can be used to estimate the continuous joint movement from EMG signals, where the measurement equation is used to reject model uncertainties and external disturbances. Moreover, we propose a new normalization approach for EMG signals for the purpose of rejecting the dependence of the motion estimation on varying external loads. Comprehensive experiments are conducted on the human elbow joint, and the improvements of the proposed methods are verified by the comparison of the EMG-based estimation and the inertial measurement unit measurements.

Missing-Data Classification With the Extended Full-Dimensional Gaussian Mixture Model: Applications to EMG-Based Motion Recognition

Missing data are a common drawback that pattern recognition techniques need to handle when solving real-life classification tasks. This paper first discusses problems in handling high-dimensional samples with missing values by the Gaussian mixture model (GMM). Since fitting the GMM by directly using high-dimensional samples as inputs is difficult due to the convergence and stability issues, a novel method is proposed to build the high-dimensional GMM by extending a reduced-dimensional GMM to the full-dimensional space. Based on the extended full-dimensional GMM, two approaches, namely, marginalization and conditional-mean imputation, are proposed to classify samples with missing data in online phase. Then, the proposed methods were employed to recognize hand motions from surface electromyography (sEMG) signals, and more than 75% of classification accuracy of motions can be obtained even if 50% of sEMG signals were missing. Comparisons with normal mean and zero imputations also demonstrate the improvements of the proposed methods. Finally, a control scheme for a myoelectric hand was designed by involving the novel methods, and online experiments confirm the ability of the proposed methods to improve the safety and stability of practical systems.

Continuous Estimation of Human Multi-Joint Angles From sEMG Using a State-Space Model

Due to the couplings among joint-relative muscles, it is a challenge to accurately estimate continuous multi-joint movements from multi-channel sEMG signals. Traditional approaches always build a nonlinear regression model, such as artificial neural network, to predict the multi-joint movement variables using sEMG as inputs. However, the redundant sEMG-data are always not distinguished; the prediction errors cannot be evaluated and corrected online as well. In this work, a correlation-based redundancy-segmentation method is proposed to segment the sEMG-vector including redundancy into irredundant and redundant subvectors. Then, a general state-space framework is developed to build the motion model by regarding the irredundant subvector as input and the redundant one as measurement output. With the built state-space motion model, a closed-loop prediction-correction algorithm, i.e., the unscented Kalman filter (UKF), can be employed to estimate the multi-joint angles from sEMG, where the redundant sEMG-data are used to reject model uncertainties. After having fully employed the redundancy, the proposed method can provide accurate and smooth estimation results. Comprehensive experiments are conducted on the multi-joint movements of the upper limb. The maximum RMSE of the estimations obtained by the proposed method is 0.16±0.03, which is significantly less than 0.25±0.06 and 0.27±0.07 (p < 0.05) obtained by common neural networks.

A General Closed-Loop Framework for Multi-dimensional Sequence Processing

A novel closed-loop framework for multidimensional sequence processing is proposed in this paper. Traditional sequence-driven models are always forward, so no information is feedback to correct their outputs, which may deviate from the true values gradually due to the estimation error accumulating. To overcome the problem, the multidimensional vector in the input sequence is divided into two vectors based on its data attribute. One vector sequence generated from the original input sequence is considered as the new input sequence, and the other is considered as the measurement output sequence. The original output sequence is treated as the state sequence. Then, a closed-loop model in the state-space form is constructed, with which the states can be estimated online by filtering algorithms. The feasibility of the proposed framework has been verified by using the robot inverse kinematics.

Real-time myoelectric prosthetic-hand control to reject outlier motion interference using one-class classifier

Electromyography (EMG) has been popularly used as interface command to achieve a natural control for myoelectric prosthetic-hands. Traditional EMG-based recognition methods always only focus on the classification of target motion classes that were defined in the training phase, but have no ability to reject outlier motion interferences that did not present before. In this paper, a hybrid classifier that combines one one-class Gaussian classifiers and a multi-class LDA was constructed to achieve EMG-based motion classification, in which Gaussian classifiers were used to reject outlier interferences, while LDA was used to classify target motion samples. The robust hybrid classifier is easily built and has low run-time complexity. Extensive experiments were conducted to verify the performance of the proposed hybrid classifier, where 91.6% of target motion recognition accuracy and 96.5% of outlier motion rejection accuracy were respectively obtained. Finally, the hybrid classifier was involved to achieve a robust and real-time control of a myoelectric prosthetic-hand.

sEMG based quantitative assessment of acupuncture on Bell's palsy: an experimental study

Acupuncture is a traditional Chinese therapeutic method, recognized by western medicine as an important complementary therapy. However, the efficacy assessment of this traditional intervention has been mainly empirical rather than scientific. In this paper, we propose a surface electromyography (sEMG) based approach for quantitative assessment of acupuncture on Bell’s palsy. The assessment is made by comparing the muscle activities of healthy with diseased ones. A feature-vector of four individual sEMG features is introduced for more comprehensive representation of muscle activity. A clustering technique is then proposed to calculate quantitative differences between the muscle activities of the healthy and diseased sides. A regressive model is also proposed to predict the recovery trend of a patient based on the quantitative assessments during his/her clinical acupuncture history. As reported in the paper, a total of 20 selected Bell’s palsy patients have participated in the extensive experiments that were conducted to verify the performance of the proposed method.摘要针刺是一种传统的中医治疗手段, 已经在临床上得到广泛的应用. 但是, 针刺疗效目前缺乏客观精确的量化评估方法. 本文提出一种基于表面肌电信号的针刺治疗面瘫疗效量化评估方法, 主要包括以下几个部分: 首先, 采集患者健康侧与患侧的表面肌电信号, 并提取特征, 组建特征向量; 然后, 采用主元分析和 k均值方法, 对特征向量进行降维聚类, 以类别中心距离作为评估针刺疗效的指标; 最后, 利用类别中心距离构造自回归模型, 预测患者的康复趋势. 20 名面瘫患者参与到本实验中, 验证上述方法. 结果表明, 本文方法预测精度达到 92.8%以上.