Imperial College London

DrHsien-YungHuang

Faculty of EngineeringDepartment of Bioengineering

Visiting Researcher
 
 
 
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Contact

 

h.huang14 Website

 
 
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Location

 

Building E - Sir Michael UrenWhite City Campus

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Summary

 

Publications

Publication Type
Year
to

8 results found

Teng Z, Xu G, Zhang X, Chen X, Zhang S, Huang H-Yet al., 2023, Concurrent and continuous estimation of multi-finger forces by synergy mapping and reconstruction: a pilot study., J Neural Eng, Vol: 20

Objective.The absence of intuitive control in present myoelectric interfaces makes it a challenge for users to communicate with assistive devices efficiently in real-world conditions. This study aims to tackle this difficulty by incorporating neurophysiological entities, namely muscle and force synergies, onto multi-finger force estimation to allow intuitive myoelectric control.Approach. Eleven healthy subjects performed six isometric grasping tasks at three muscle contraction levels. The exerted fingertip forces were collected concurrently with the surface electromyographic (sEMG) signals from six extrinsic and intrinsic muscles of hand. Muscle synergies were then extracted from recorded sEMG signals, while force synergies were identified from measured force data. Afterwards, a linear regressor was trained to associate the two types of synergies. This would allow us to predict multi-finger forces simply by multiplying the activation signals derived from muscle synergies with the weighting matrix of initially identified force synergies. To mitigate the false activation of unintended fingers, the force predictions were finally corrected by a finger state recognition procedure.Main results. We found that five muscle synergies and four force synergies are able to make a tradeoff between the computation load and the prediction accuracy for the proposed model; When trained and tested on all six grasping tasks, our method (SYN-II) achieved better performance (R2= 0.80 ± 0.04, NRMSE = 0.19 ± 0.01) than conventional sEMG amplitude-based method; Interestingly, SYN-II performed better than all other methods when tested on two unknown tasks outside the four training tasks (R2= 0.74 ± 0.03, NRMSE = 0.22 ± 0.02), which indicated better generalization ability.Significance. This study shows the first attempt to link between muscle and force synergies to allow concurrent and continuous estimation of multi-finger forces from sEMG. The proposed approach ma

Journal article

Jing S, Huang H-Y, Vaidyanathan R, Farina Det al., 2022, Accurate and Robust Locomotion Mode Recognition Using High-Density EMG Recordings from a Single Muscle Group., Annu Int Conf IEEE Eng Med Biol Soc, Vol: 2022, Pages: 686-689

Existing methods for human locomotion mode recognition often rely on using multiple bipolar electrode sensors on multiple muscle groups to accurately identify underlying motor activities. To avoid this complex setup and facilitate the translation of this technology, we introduce a single grid of high-density surface electromyography (HDsEMG) electrodes mounted on a single location (above the rectus femoris) to classify six locomotion modes in human walking. By employing a neural network, the trained model achieved average recognition accuracy of 97.7% with 160ms latency, significantly better than the model trained with one bipolar electrode pair placed on the same muscle (71.4% accuracy). To further exploit the spatial and temporal information of HDsEMG, we applied data augmentation to generate artificial data from simulated displaced electrodes, aiming to counteract the influence of electrode shifts. By employing a convolutional neural network with the enhanced dataset, the updated model was not strongly affected by electrode misplacement (93.9% accuracy) while models trained by bipolar electrode data were significantly disrupted by electrode shifts (29.4% accuracy). Findings suggest HDsEMG could be a valuable resource for mapping gait with fewer sensor locations and greater robustness. Results offer future promise for real-time control of assistive technology such as exoskeletons.

Journal article

Huang HY, Farkhatdinov I, Arami A, Bouri M, Burdet Eet al., 2021, Cable-driven robotic interface for lower limb neuromechanics identification, IEEE Transactions on Biomedical Engineering, Vol: 68, Pages: 461-469, ISSN: 0018-9294

This paper presents a versatile cable-driven robotic interface to investigate the single-joint joint neuromechanics of the hip, knee and ankle in the sagittal plane. This endpoint-based interface offers highly dynamic interaction and accurate position control (as is typically required for neuromechanics identification), and provides measurements of position, interaction force and EMG of leg muscles. It can be used with the subject upright, corresponding to a natural posture during walking or standing, and does not impose kinematic constraints on a joint, in contrast to existing interfaces. Mechanical evaluations demonstrated that the interface yields a rigidity above 500 N/m with low viscosity. Tests with a rigid dummy leg and linear springs show that it can identify the mechanical impedance of a limb accurately. A smooth perturbation is developed and tested with a human subject, which can be used to estimate the hip neuromechanics.

Journal article

Atashzar SF, Huang H-Y, Duca FD, Burdet E, Farina Det al., 2020, Energetic Passivity Decoding of Human Hip Joint for Physical Human-Robot Interaction, IEEE ROBOTICS AND AUTOMATION LETTERS, Vol: 5, Pages: 5953-5960, ISSN: 2377-3766

Journal article

Huang HY, Arami A, Farkhatdinov I, Formica D, Burdet Eet al., 2020, The Influence of Posture, Applied Force and Perturbation Direction on Hip Joint Viscoelasticity, IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, Vol: 28, Pages: 1138-1145, ISSN: 1534-4320

Journal article

van der Kooij H, van Asseldonk E, van Oort G, Sluiter V, Emmens A, Witteveen H, Tagliamonte NL, Tamburella F, Pisotta I, Masciullo M, Arquilla M, Molinari M, Wu A, Ijspeert A, Dzeladini FF, Thorsteinsson F, Arami A, Burdet E, Huang HY, Gregoor W, Meijneke Cet al., 2019, Symbitron: Symbiotic man-machine interactions in wearable exoskeletons to enhance mobility for paraplegics, Biosystems and Biorobotics, Pages: 361-364

The main goal of the Symbitron project was to develop a safe, bio-inspired, personalized wearable exoskeleton that enables SCI patients to walk without additional assistance, by complementing their remaining motor function. Here we give an overview of major achievements of the projects.

Book chapter

Huang H-Y, Farkhatdinov I, Arami A, Burdet Eet al., 2017, Modelling Neuromuscular Function of SCI Patients in Balancing, 3rd International Conference on NeuroRehabilitation (ICNR), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 355-359, ISSN: 2195-3562

Conference paper

Huang HY, Chen JS, Huang CE, 2014, Toward the gait analysis and control of a powered lower limb orthosis in ascending and descending stairs, Pages: 417-426

Aging is inevitable for every human. Meniscus will gradually wear out with continuous loading and aging, it will result in a sour knee and lose the interest to walk or even standup. There is evidence that positive relationship between muscle activity and Electromyography (EMG). It is also noted that muscle contraction can be divided into two types, concentric and eccentric and will transmit through muscle before muscle contraction. With the differences, the model of EMG and moment could be established. It is suggested that if one can provide assistance at the correct time with the correct amount of energy, through a powered lower limb orthosis, one would be able to alleviate the problem of cartilage deteriorating and enable the elders to ascend and descent from the ladder.

Conference paper

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