Reflex Control of Robotic Gait Using Human Walking Data

Macleod, Catherine; Meng, Lin; Conway, B.A.; Porr, Bernd

doi:10.1371/journal.pone.0109959

Cited by 11 publications

(25 citation statements)

References 81 publications

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“…Biarticular muscles: these muscles are connected to two links separated by a third link and two joints and can drive these two joints simultaneously, and have the capability to transfer energy mainly produced by monarticular muscles to joints where they can effectively contribute to the desired aim of movement. The mono and biarticular muscles that correspond to rectus femoris (RF), biceps femoris (BF) gastrocnemius (GAS) and tibialis anterior (TA) in human legs play significant roles in the stability of human walking [10,11].The muscles (RF) is a bifunctional muscle responsible for hip flexion in the swing phase and knee extension in the late swing and stance phase, the (BF) muscle responds to the hip extension in the stance phase and the knee flexion in the swing phase the (GAS) muscle is primarily responsible for ankle plantarflexion but also takes a minor role in knee extension, the TA muscle has two distinct roles during human walking: (1) to dorsiflex the ankle during the swing phase for foot clearance and placement; (2) to contract during ankle plantarflexion at the initial foot contact with the ground [1,15]. Thus, using springs like mono-biarticular muscles in the leg mechanism of bipedal robots are able to achieve the stability of human-like leg walking compared with those that assume the bipedal robots like rigid body structures.…”

Section: Introductionmentioning

confidence: 99%

Toward a dynamic analysis of bipedal robots inspired by human leg muscles

Fernini¹,

Temmar²,

Noor³

2018

J. MECH. ENG. SCI.

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A walking bipedal robot by adding passive springs like mono and biarticular muscles which correspond to rectus femoris (RF), biceps femoris (BF), gastrocnemius (GAS) and tibialis anterior (TA) in human legs has been modeled in this paper. The stability of human-like leg walking can be achieved by adding these passive springs in the leg mechanism of bipedal robot. On the other hand, using these springs may inflict a fatigue during walking due to the additional work that can provide to the joints. The main objective of this paper is to analyze the total work of the robot during walking by proposing four cases of the preload of the springs at the equilibrium position. It's found from this study that the case that has the most energy saving and ensure the comfortable walking to the robot is when the two muscles (GAS) and (TA) are not preloaded to support the total weight at the equilibrium position.

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Section: Introductionmentioning

confidence: 99%

Toward a dynamic analysis of bipedal robots inspired by human leg muscles

Fernini¹,

Temmar²,

Noor³

2018

J. MECH. ENG. SCI.

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“…2 , the muscle activity can be identified as following the ipsilateral HS for the hip extension and ipsilateral TO for the knee flexion (Macleod et al. 2014 ).…”

Section: Human Walking Studymentioning

confidence: 99%

“…In the previous study the muscle transfer functions were optimised using a curve fitting process to remove spurious artefacts and resampled at a specific sampling frequency to fit the mechanical system of the RunBot II (Macleod et al. 2014 ). Conversely, in this paper, a second-order low-pass Bessel filter; see Eq.…”

Section: Human Walking Studymentioning

confidence: 99%

“…The calculation of transfer functions relating sensory information and muscle EMG has been discussed in detail in our previous study (Macleod et al. 2014 ) where the transfer functions were applied to a prior generation of the RunBot (RunBot II) as a proof of concept. The stable gait cycle generated indicated that the approach had potential for use in robotic control.…”

Section: Introductionmentioning

confidence: 99%

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Bipedal robotic walking control derived from analysis of human locomotion

et al. 2018

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This paper proposes the design of a bipedal robotic controller where the function between the sensory input and motor output is treated as a black box derived from human data. In order to achieve this, we investigated the causal relationship between ground contact information from the feet and leg muscle activity n human walking and calculated filter functions which transform sensory signals to motor actions. A minimal, nonlinear, and robust control system was created and subsequently analysed by applying it to our bipedal robot RunBot III without any central pattern generators or precise trajectory control. The results demonstrate that our controller can generate stable robotic walking. This indicates that complex locomotion patterns can result from a simple model based on reflexes and supports the premise that human-derived control strategies have potential applications in robotics or assistive devices.Electronic supplementary materialThe online version of this article (10.1007/s00422-018-0750-5) contains supplementary material, which is available to authorized users.

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“…Meng et al extended this approach to include four muscles, the quadriceps, hamstrings, tibialis anterior and gastrocnemius muscles [16]. In a previous study [17], the EMG muscle activity of ten healthy subjects during gait was recorded in relation to five gait events. This recorded EMG activity was converted to a stimulation intensity pattern and played back at the corresponding gait events of each individual participant.…”

Section: Introductionmentioning

confidence: 99%

Adaptive multichannel FES neuroprosthesis with learning control and automatic gait assessment

Müller

del-Ama

Moreno

et al. 2020

J NeuroEngineering Rehabil

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Background: FES (Functional Electrical Stimulation) neuroprostheses have long been a permanent feature in the rehabilitation and gait support of people who had a stroke or have a Spinal Cord Injury (SCI). Over time the well-known foot switch triggered drop foot neuroprosthesis, was extended to a multichannel full-leg support neuroprosthesis enabling improved support and rehabilitation. However, these neuroprostheses had to be manually tuned and could not adapt to the persons' individual needs. In recent research, a learning controller was added to the drop foot neuroprosthesis, so that the full stimulation pattern during the swing phase could be adapted by measuring the joint angles of previous steps. Methods: The aim of this research is to begin developing a learning full-leg supporting neuroprosthesis, which controls the antagonistic muscle pairs for knee flexion and extension, as well as for ankle joint dorsi-and plantarflexion during all gait phases. A method was established that allows a continuous assessment of knee and foot joint angles with every step. This method can warp the physiological joint angles of healthy subjects to match the individual pathological gait of the subject and thus allows a direct comparison of the two. A new kind of Iterative Learning Controller (ILC) is proposed which works independent of the step duration of the individual and uses physiological joint angle reference bands. Results: In a first test with four people with an incomplete SCI, the results showed that the proposed neuroprosthesis was able to generate individually fitted stimulation patterns for three of the participants. The other participant was more severely affected and had to be excluded due to the resulting false triggering of the gait phase detection. For two of the three remaining participants, a slight improvement in the average foot angles could be observed, for one participant slight improvements in the averaged knee angles. These improvements where in the range of 4°at the times of peak dorsiflexion, peak plantarflexion, or peak knee flexion. Conclusions: Direct adaptation to the current gait of the participants could be achieved with the proposed method. The preliminary first test with people with a SCI showed that the neuroprosthesis can generate individual stimulation patterns. The sensitivity to the knee angle reset, timing problems in participants with significant gait fluctuations, and the automatic ILC gain tuning are remaining issues that need be addressed. Subsequently, future studies should compare the improved, long-term rehabilitation effects of the here presented neuroprosthesis, with conventional multichannel FES neuroprostheses.

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Reflex Control of Robotic Gait Using Human Walking Data

Cited by 11 publications

References 81 publications

Toward a dynamic analysis of bipedal robots inspired by human leg muscles

Toward a dynamic analysis of bipedal robots inspired by human leg muscles

Bipedal robotic walking control derived from analysis of human locomotion

Adaptive multichannel FES neuroprosthesis with learning control and automatic gait assessment

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