Wrist angle estimation based on musculoskeletal systems with EMG

Sawaguchi, Eita; Sadahiro, Teruyoshi; Iwase, Masami

doi:10.1109/idaacs.2011.6072755

Cited by 5 publications

(1 citation statement)

References 6 publications

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“…Fakuda et al [2003] developed a manipulator controlled by arm movements, using myoelectric signals to determine which joint should be controlled. Sawaguchi et al [2011] developed estimation models of the wrist angle from the corresponding myoelectric potential. One of them is a one-link model considering the moment arm.…”

Section: Introductionmentioning

confidence: 99%

Continuous Estimation Prediction of Knee Joint Angles Using Fusion of Electromyographic and Inertial Sensors for Active Transfemoral Leg Prostheses

Delis

Miosso

Carvalho

et al. 2018

Adv. Data Sci. Adapt. Data Anal.

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Information extracted from the surface electromyographic (sEMG) signals can allow for the detection of movement intention in transfemoral prostheses. The sEMG can help estimate the angle between the femur and the tibia in the sagittal plane. However, algorithms based exclusively on sEMG information can lead to inaccurate results. Data captured by inertial-sensors can improve this estimate. We propose three myoelectric algorithms that extract data from sEMG and inertial sensors using Kalman-filters. The proposed fusion-based algorithms showed improved performance compared to methods based exclusively on sEMG data, generating improvements in the accuracy of knee joint angle estimation and reducing estimation artifacts.

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Section: Introductionmentioning

confidence: 99%

Continuous Estimation Prediction of Knee Joint Angles Using Fusion of Electromyographic and Inertial Sensors for Active Transfemoral Leg Prostheses

Delis

Miosso

Carvalho

et al. 2018

Adv. Data Sci. Adapt. Data Anal.

View full text Add to dashboard Cite

show abstract

From on-body sensors to in-body data for health monitoring and medical robotics: A survey

Hernández

Raison

Alexandre

et al. 2014

2014 Global Information Infrastructure and Networking Symposium (GIIS)

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The estimation of musculoskeletal data such as muscle forces and joint torques could have a significant impact on patient care monitoring and medical robotics as well as on reducing healthcare and industrial costs by improving the treatment in the field of rehabilitation. Direct measurement of these data is now non-invasive, as they are computed from dedicated wireless on-body sensors, which can synchronously measure segment positions, muscle activation, external forces and allow to estimate muscle force and joint torques using musculoskeletal models. This paper presents a state-of-the-art survey reviewing both the most commonly used on-body sensors, over the last thirty years, to compute in-body data and the most popular optokinetic cameras. The results are presented and classified into tables which show the evolution of on-body sensors since the 1980's, but also the challenges that lie ahead, as very accurate sensors only accentuate the faults of an inaccurate musculoskeletal model. The survey results show that there is a lack of studies validating the different musculoskeletal models. In addition, current interfaces between hardware and software could be improved.

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Continuous Wrist Joint Control Using Muscle Deformation Measured on Forearm Skin

Kato

Hirabayashi

Matsurnoto

et al. 2018

2018 IEEE International Conference on Robotics and Automation (ICRA)

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Wrist angle estimation based on musculoskeletal systems with EMG

Cited by 5 publications

References 6 publications

Continuous Estimation Prediction of Knee Joint Angles Using Fusion of Electromyographic and Inertial Sensors for Active Transfemoral Leg Prostheses

Continuous Estimation Prediction of Knee Joint Angles Using Fusion of Electromyographic and Inertial Sensors for Active Transfemoral Leg Prostheses

From on-body sensors to in-body data for health monitoring and medical robotics: A survey

Continuous Wrist Joint Control Using Muscle Deformation Measured on Forearm Skin

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