Multi-directional Dynamic Mechanical Impedance of the Human Ankle; A Key to Anthropomorphism in Lower Extremity Assistive Robots

Rastgaar, Mo; Lee, Hyunglae; Ficanha, Evandro M.; Ho, Patrick; Krebs, Hermano Igo; Hogan, Neville

doi:10.1007/978-94-017-8932-5_6

Cited by 12 publications

(15 citation statements)

References 41 publications

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“…The supporting frame (1) and straps were attached to the knee brace to hold the weight of the leg and Anklebot. These straps suspended the knee at 90 to keep the thigh parallel to the ground (Lee et al 2014a;Rastgaar et al 2014). Next, the ends of the Anklebot actuators were attached to the shoe bracket (8) Rastgaar et al 2014).…”

Section: Methodsmentioning

confidence: 99%

“…These straps suspended the knee at 90 to keep the thigh parallel to the ground (Lee et al 2014a;Rastgaar et al 2014). Next, the ends of the Anklebot actuators were attached to the shoe bracket (8) Rastgaar et al 2014). The ankle angle was also placed at 90 in the sagittal plane, where the Anklebot actuators were calibrated and set as the home position.…”

Section: Methodsmentioning

confidence: 99%

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Estimating the multivariable human ankle impedance in dorsi-plantarflexion and inversion-eversion directions using EMG signals and artificial neural networks

Dallali

Knop

Castelino

et al. 2017

Int J Intell Robot Appl

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The use of a suitably designed ankle-foot prosthesis is essential for transtibial amputees to regain lost mobility. A desired ankle-foot prosthesis must be able to replicate the function of a healthy human ankle by transferring the ground reaction forces to the body, absorbing shock during contact, and providing propulsion. During the swing phase of walking, the human ankle is soft and relaxed; however, it hardens as it bears the body weight and provides force for push-off. The stiffness is one of the components of the mechanical impedance, and it varies with muscle activation (Stochastic estimation of human ankle mechanical impedance in medial-lateral direction, 2014, Stochastic estimation of the multivariable mechanical impedance of the human ankle with active muscles, 2010). This study defines the relationship between ankle impedance and the lower extremity muscle activations using artificial neural networks (ANN). We used the Anklebot, a highly backdrivable, safe, and therapeutic robot to apply stochastic position perturbations to the human ankle in the sagittal and frontal planes. A previously proposed system identification method was used to estimate the target ankle impedance to train the ANN. The ankle impedance was estimated with relaxed muscles and with lower leg muscle activations at 10 and 20% of the maximum voluntary contraction (MVC) of each individual subject. Given the root mean squared (rms) of the electromyography (EMG) signals, the proposed ANN effectively predicted the ankle impedance with mean accuracy of 89.8 ± 6.1% in DP and mean accuracy of 88.3 ± 5.7% in IE, averaged across three muscle activation levels and all subjects.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Estimating the multivariable human ankle impedance in dorsi-plantarflexion and inversion-eversion directions using EMG signals and artificial neural networks

Dallali

Knop

Castelino

et al. 2017

Int J Intell Robot Appl

Self Cite

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“…The standard error is calculated using SE ¼ s= ffiffi ffi n p where s is the standard deviation computed over five repetitions of each test and n is the number of samples. As expected the magnitude of the ankle impedance is increasing with muscle activation, as was reported for DP and IE directions in Lee et al (2014). The corresponding coherence function was computed based on spectral analysis using Eq.…”

Section: Ankle Impedance Estimation Using System Identificationmentioning

confidence: 52%

“…The knee angle was measured using a goniometer and was fixed at 90°with respect to the shin. This brace was attached to a supporting Using lower extremity muscle activity to obtain human ankle impedance in the external-… 31 frame using straps to suspend the leg, hold the weight of the Anklebot system, and to prevent motion in other anatomical planes, as described in Lee et al (2014) and Rastgaar et al (2014). A shin brace (6) was also attached to prevent motion in the sagittal plane.…”

Section: Methodsmentioning

confidence: 99%

“…In one study, the Anklebot, consisting of two back-drivable linear actuators, was used to apply pseudo-random position perturbations to the ankle and measure the resulting position and torque response. Initial studies determined the multivariable mechanical impedance of the ankle in DP and IE during relaxed and active muscle co-contraction (Lee et al 2014). The experiments were performed when the lower extremity muscle activations were relaxed and at 10% of the subject's maximum voluntary contraction (MVC) and a dynamic model to represent the coupling between the DP and IE DOF was developed.…”

Section: Introductionmentioning

confidence: 99%

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Using lower extremity muscle activity to obtain human ankle impedance in the external–internal direction

Dallali

Knop

Castelino

et al. 2017

Int J Intell Robot Appl

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The human ankle has a critical role in locomotion and estimating its impedance is essential for human gait rehabilitation. The ankle is the first major joint that regulates the contact forces between the human body and the environment, absorbing shocks during the stance, and providing propulsion during walking. Its impedance varies with the level of the muscle activation. Hence, characterizing the complex relation between the ankle impedance and the lower leg's muscle activation levels may improve our understanding of the neuromuscular characteristics of the ankle. Most ankle-foot prostheses do not have a degree of freedom in the transverse plane, which can cause high amounts of shear stress to be applied to the socket and can lead to secondary injuries. Quantifying the ankle impedance in the transverse plane can guide the design for a variable impedance ankle-foot prosthesis that can significantly reduce the shear stress on the socket. This paper presents the results of applying artificial neural networks (ANN) to learn and estimate the relation between the ankle impedance in the transverse plane under non-load bearing condition using electromyography signals (EMG) from the lower leg muscles. The Anklebot was used to apply pseudorandom perturbations to the human ankle in the transverse plane while the other degrees of freedom (DOF) in the sagittal and frontal planes were constrained. The mechanical impedance of the ankle was estimated using a previously proposed stochastic identification method that describes the ankle impedance as a function of the applied disturbances torques and the ankle motion output. The ankle impedance with relaxed muscles and with the lower leg's muscle activations at 10 and 20% of the maximum voluntary contraction were estimated. The proposed ANN effectively predicts the ankle impedance within 85% accuracy (±5 Nm/rad absolute) for nine out of ten subjects given the root-mean-squared (rms) of the EMG signals. The main contribution of this paper is to quantify the relationship between lower leg muscle EMG signals and the ankle impedance in the transverse plane to pave the way towards designing and controlling this degree of freedom in a future ankle-foot prosthesis.

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Estimating the Relationship Between Multivariable Standing Ankle Impedance and Lower Extremity Muscle Activation

Knop

Ribeiro

Ficanha

et al. 2018

2018 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob)

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Multi-directional Dynamic Mechanical Impedance of the Human Ankle; A Key to Anthropomorphism in Lower Extremity Assistive Robots

Cited by 12 publications

References 41 publications

Estimating the multivariable human ankle impedance in dorsi-plantarflexion and inversion-eversion directions using EMG signals and artificial neural networks

Estimating the multivariable human ankle impedance in dorsi-plantarflexion and inversion-eversion directions using EMG signals and artificial neural networks

Using lower extremity muscle activity to obtain human ankle impedance in the external–internal direction

Estimating the Relationship Between Multivariable Standing Ankle Impedance and Lower Extremity Muscle Activation

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