Torque Estimation of Elbow Joint Using a Mechanomyogram Signal Based Biomechanical Model

Kosaki, Takahiro; Tochiki, Anna; Li, Shigang; Kanazawa, Rihito

doi:10.1109/mecatronics.2018.8495874

Cited by 6 publications

(4 citation statements)

References 15 publications

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“…Hence, we aimed to develop the detector separately from the classifier. In addition, we proposed an elbowjoint torque estimator based on a simplified musculotendinous model, together with MMG signals of the biceps and triceps brachii of a human upper arm [7]. We verified the accuracy of our estimator in comparison to an estimator using EMG signals.…”

Section: Introductionmentioning

confidence: 92%

“…We verified the accuracy of our estimator in comparison to an estimator using EMG signals. However, because the estimator employs a biomechanical approach, it requires computational effort, and its estimation performance is not yet good enough for classification [7]. One of our next steps will be the development of a high-accuracy motion classifier based on MMG signals.…”

Section: Introductionmentioning

confidence: 99%

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An Elbow Motion Classification Approach Based on the MT System Using Mechanomyogram Signals

Tochiki

Kosaki

2020

SICE Journal of Control, Measurement, and System Integration

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The objective of this paper is to propose a mechanomyogram (MMG)-based motion classification system comprised of a muscle-activity onset detector and a motion classifier. The detector identifies muscle-activity onset time using sampled time-series of MMG signals of biceps and triceps brachii of a human upper arm based on the Mahalanobis-Taguchi method. The classifier is based on the Recognition-Taguchi method and an AdaBoost ensemble learning technique, and distinguishes the flexion and extension of an elbow directly from time-series data of MMG signals of biceps and triceps brachii. We conducted an experimental comparison of the proposed classification system with our previous one based on discriminant analysis techniques to evaluate performance with research participants. Results verified the feasibility of the system and showed that the proposed system achieved higher classification performance than the previous one.

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Section: Introductionmentioning

confidence: 92%

Section: Introductionmentioning

confidence: 99%

An Elbow Motion Classification Approach Based on the MT System Using Mechanomyogram Signals

Tochiki

Kosaki

2020

SICE Journal of Control, Measurement, and System Integration

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“…Kosaki et al 14 proposed a simplified elbow torque estimator based on minute oscillation signals of bicep and tricep muscles. These signals are known as mechanomyogram (MMG) signals and unlike surface electromyograms (sEMG) there is no need for direct contact with skin.…”

Section: Introductionmentioning

confidence: 99%

Quantitative analysis and control of the torque profile of the upper limb using a kinetic model and motion measurements

Abdul-Ameer

2022

Int J Artif Organs

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This paper investigates a new approach to the rapid control of an upper limb exoskeleton actuator. We used a mathematical model and motion measurements of a human arm to estimate joint torque as a means to control the exoskeleton’s actuator. The proposed arm model is based on a two-pendulum configuration and is used to obtain instantaneous joint torques which are then passed into control law to regulate the actuator torque. Nine subjects volunteered to take part in the experimental protocol, in which inertial measurement units (IMUs) and a digital goniometer were used to measure and estimate the torque profiles. To validate the control law, a Simscape model was developed to simulate the arm model and control law in which measurement data from IMUs and a goniometer were fed into the suggested Simscape model. The arm torque profiles are key to the control approach and should be traced by torques produced by the exoskeleton actuators to provide comfort and flexibility for the subjects. A DC motor was used as an actuator for the exoskeleton, and its model was used in the physical Simscape model. To reduce the error in the driving torque compared with the reference arm torque, a PID controller was implemented. The results show the potential of our methodology for tracking and controlling the actuator’s torque, in which the mean square error was reduced to less than 0.2 - a significantly low value.

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“…Human torque estimation using MMG has also been investigated. A biomechanical model was used to demonstrate comparable performance of EMG and MMG in [18] for elbow joint torque estimation using two signals during isometric flexion and extension. Experimental results demonstrated comparable EMG and MMG performance for flexion/extension (90%-94% for both), however the work relied on biomechanics models which are not applicable to people with limited muscle function or abnormal gait.…”

Section: Introductionmentioning

confidence: 99%

Human Joint Torque Modelling With MMG and EMG During Lower Limb Human-Exoskeleton Interaction

Caulcrick

Huo

Hoult

et al. 2021

IEEE Robot. Autom. Lett.

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Human-robot cooperation is vital for optimising powered assist of lower limb exoskeletons (LLEs). Robotic capacity to intelligently adapt to human force, however, demands a fusion of data from exoskeleton and user state for smooth human-robot synergy. Muscle activity, mapped through electromyography (EMG) or mechanomyography (MMG) is widely acknowledged as usable sensor input that precedes the onset of human joint torque. However, competing and complementary information between such physiological feedback is yet to be exploited, or even assessed, for predictive LLE control. We investigate complementary and competing benefits of EMG and MMG sensing modalities as a means of calculating human torque input for assist-as-needed (AAN) LLE control. Three biomechanically agnostic machine learning approaches, linear regression, polynomial regression, and neural networks, are implemented for joint torque prediction during human-exoskeleton interaction experiments. Results demonstrate MMG predicts human joint torque with slightly lower accuracy than EMG for isometric humanexoskeleton interaction. Performance is comparable for dynamic exercise. Neural network models achieve the best performance for both MMG and EMG (94.8±0.7% with MMG and 97.6±0.8% with EMG (Mean±SD)) at the expense of training time and implementation complexity. This investigation represents the first MMG human joint torque models for LLEs and their first comparison with EMG. We provide our implementations for future investigations (https://github.com/cic12/ieee appx).

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Torque Estimation of Elbow Joint Using a Mechanomyogram Signal Based Biomechanical Model

Cited by 6 publications

References 15 publications

An Elbow Motion Classification Approach Based on the MT System Using Mechanomyogram Signals

An Elbow Motion Classification Approach Based on the MT System Using Mechanomyogram Signals

Quantitative analysis and control of the torque profile of the upper limb using a kinetic model and motion measurements

Human Joint Torque Modelling With MMG and EMG During Lower Limb Human-Exoskeleton Interaction

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