Human-machine interface for wheelchair control with EMG and its evaluation

Han, Jong-Seob; Bien, Zeungnam; Kim, Dae‐Jin; Lee, Hyong-Euk; Kim, Jong Sung

doi:10.1109/iembs.2003.1279672

Cited by 35 publications

(5 citation statements)

References 8 publications

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“…It is reported that BCI is only preferred when the use of MuCIs is not feasible [2]. There have been numerous studies on the potential of MuCI systems, including multifunction prosthesis [3][4][5][6], power exoskeleton control [7], wheelchairs [8][9], robotic control [10] and grasping control [11].…”

Section: Introductionmentioning

confidence: 99%

Robust Facial Expression Recognition for MuCI: A Comprehensive Neuromuscular Signal Analysis

Hamedi

Salleh

Ting

et al. 2018

IEEE Trans. Affective Comput.

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This paper presents a comprehensive study on the analysis of neuromuscular signal activities to recognize eleven facial expressions for Muscle Computer Interfacing applications. A robust denoising protocol comprised of Wavelet transform and Kalman filtering is proposed to enhance the electromyogram (EMG) signal-to-noise ratio and improve classification performance. The effectiveness of eight different time-domain facial EMG features on system performance is examined and compared in order to identify the most discriminative one. Fourteen pattern recognition-based algorithms are employed to classify the extracted features. These classifiers are evaluated in terms of classification accuracy and processing time. Finally, the best methods that obtain almost identical system performance are compared through the Normalized Mutual Information (NMI) criterion and a repeated measure analysis of variance (ANOVA) for a statistical significant test.To clarify the impact of signal denoising, all considered EMG features and classifiers are assessed with and without this stage. Results show that: (1) the proposed denosing step significantly improves the system performance; (2) Root Mean Square is the most discriminative facial EMG feature; (3) discriminant analysis when the parameters are estimated by the Maximum Likelihood algorithm achieves the highest classification accuracy and NMI; however, ANOVA reveals no significant difference among the best methods with almost similar performance.

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Section: Introductionmentioning

confidence: 99%

Robust Facial Expression Recognition for MuCI: A Comprehensive Neuromuscular Signal Analysis

Hamedi

Salleh

Ting

et al. 2018

IEEE Trans. Affective Comput.

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“…Neuro-Fuzzy approach has also been used for EMG classification especially in machine control fields (Kiguchi et al, 2003;Ahsan et al, 2010). Then, FCNN, simplified fuzzy ARTMAP and FMMNN were introduced by and Han et al (2004) respectively. GMM, N-GMM, HMM and LLGMN are known as Probabilistic classifiers which were presented and applied in myoelectric classification (Huang et al, 2005;Fukuda et al, 2003).…”

Section: Introductionmentioning

confidence: 99%

Surface Electromyography-Based Facial Expression Recognition in Bi-Polar Configuration

Hamedi

Salleh²,

Swee³

et al. 2011

Journal of Computer Science

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Problem statement: Facial expression recognition has been improved recently and it has become a significant issue in diagnostic and medical fields, particularly in the areas of assistive technology and rehabilitation. Apart from their usefulness, there are some problems in their applications like peripheral conditions, lightening, contrast and quality of video and images. Approach: Facial Action Coding System (FACS) and some other methods based on images or videos were applied. This study proposed two methods for recognizing 8 different facial expressions such as natural (rest), happiness in three conditions, anger, rage, gesturing 'a' like in apple word and gesturing no by pulling up the eyebrows based on Three-channels in Bi-polar configuration by SEMG. Raw signals were processed in three main steps (filtration, feature extraction and active features selection) sequentially. Processed data was fed into Support Vector Machine and Fuzzy C-Means classifiers for being classified into 8 facial expression groups. Results: 91.8 and 80.4% recognition ratio had been achieved for FCM and SVM respectively. Conclusion: The confirmed enough accuracy and power in this field of study and FCM showed its better ability and performance in comparison with SVM. It's expected that in near future, new approaches in the frequency bandwidth of each facial gesture will provide better results.

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“…The virtual cursor control task is often used to evaluate the performance of human machine interfaces [67][68][69][70][71][72] . Traditionally, surface electromyography has been used to decode motor intent and drive the cursor during a virtual target achievement control task [73][74][75][76][77] . Various algorithms have been used to generate the control signal, including pattern recognition, linear regression, etc, and the derived signal can be used to control either the velocity or position of the cursor.…”

Section: Characterizing Movement Quality In Control Tasksmentioning

confidence: 99%

Using Principles of Motor Control to Analyze Performance of Human Machine Interfaces

Patwardhan

Gladhill

Joiner

et al. 2023

Preprint

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There have been significant advances in biosignal extraction techniques to drive external biomechatronic devices or to use as inputs to sophisticated human machine interfaces. The control signals are typically derived from biological signals such as myoelectric measurements made either from the surface of the skin or subcutaneously. Other biosignal sensing modalities are emerging. With improvements in sensing modalities and control algorithms, it is becoming possible to robustly control the target position of a end effector. It remains largely unknown to what extent these improvements can lead to naturalistic human-like movement. In this paper, we sought to answer this question. We utilized a sensing paradigm called sonomyography based on continuous ultrasound imaging of forearm muscles. Unlike myoelectric control strategies which measure electrical activation and use the extracted signals to determine the velocity of an end-effector; sonomyography measures muscle deformation directly with ultrasound and uses the extracted signals to proportionally control the position of an end-effector. Previously, we showed that users were able to accurately and precisely perform a virtual target acquisition task using sonomyography. In this work, we investigate the time course of the control trajectories derived from sonomyography. We show that the time course of the sonomyography-derived trajectories that users take to reach virtual targets reflect the trajectories shown to be typical for kinematic characteristics observed in biological limbs. Specifically, during a target acquisition task, the velocity profiles followed a minimum jerk trajectory shown for point-to-point arm reaching movements, with similar time to target. In addition, the trajectories based on ultrasound imaging result in a systematic delay and scaling of peak movement velocity as the movement distance increased. We believe this is the first evaluation of similarities in control policies in coordinated movements in jointed limbs, and those based on position control signals extracted at the individual muscle level. These results have strong implications for the future development of control paradigms for assistive technologies.

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Human-machine interface for wheelchair control with EMG and its evaluation

Cited by 35 publications

References 8 publications

Robust Facial Expression Recognition for MuCI: A Comprehensive Neuromuscular Signal Analysis

Robust Facial Expression Recognition for MuCI: A Comprehensive Neuromuscular Signal Analysis

Surface Electromyography-Based Facial Expression Recognition in Bi-Polar Configuration

Using Principles of Motor Control to Analyze Performance of Human Machine Interfaces

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