Mechanomyography Based Closed-Loop Functional Electrical Stimulation Cycling System

Woods, Billy; Subramanian, Mahendran; Shafti, Ali; Faisal, A. Aldo

doi:10.1109/biorob.2018.8487941

Cited by 5 publications

(4 citation statements)

References 18 publications

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“…Nevertheless, the RL controller successfully learns to interpret and exploit the information without any pre-defined rule. This means it may be possible to integrate feedback signals that can provide information on muscle fatigue such as electromyography (EMG) or mechanomyography (MMG) or both [10], [11] into a FES cycling system [12], controlled by an RL system. On the conventional side, there are some advanced methods such as HOSM [13] that can maintain the cadence against the advancing fatigue in real-world.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Neuromechanics-based Deep Reinforcement Learning of Neurostimulation Control in FES cycling

Wannawas

Subramanian

Faisal

2021

2021 10th International IEEE/EMBS Conference on Neural Engineering (NER)

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Functional Electrical Stimulation (FES) can restore motion to a paralysed's person muscles. Yet, control stimulating many muscles to restore the practical function of entire limbs is an unsolved problem. Current neurostimulation engineering still relies on 20th Century control approaches and correspondingly shows only modest results that require daily tinkering to operate at all. Here, we present our state-of-the-art Deep Reinforcement Learning developed for real-time adaptive neurostimulation of paralysed legs for FES cycling. Core to our approach is the integration of a personalised neuromechanical component into our reinforcement learning (RL) framework that allows us to train the model efficiently-without demanding extended training sessions with the patient and working outof-the-box. Our neuromechanical component includes merges musculoskeletal models of muscle/tendon function and a multistate model of muscle fatigue, to render the neurostimulation responsive to a paraplegic's cyclist instantaneous muscle capacity. Our RL approach outperforms PID and Fuzzy Logic controllers in accuracy and performance. Crucially, our system learned to stimulate a cyclist's legs from ramping up speed at the start to maintaining a high cadence in steady-state racing as the muscles fatigue. A part of our RL neurostimulation system has been successfully deployed at the Cybathlon 2020 bionic Olympics in the FES discipline with our paraplegic cyclist winning the Silver medal among 9 competing teams.

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Section: Conclusion and Discussionmentioning

confidence: 99%

Neuromechanics-based Deep Reinforcement Learning of Neurostimulation Control in FES cycling

Wannawas

Subramanian

Faisal

2021

2021 10th International IEEE/EMBS Conference on Neural Engineering (NER)

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“…Inertial measurement units can also be placed on the pilot's legs to directly measure the knee angle (38,44) or thigh inclination, indicating the knee flexion or extension phase (45,46), as input in the FES control system. Mechanomyography sensors have been used to monitor muscle activity in real time during stimulation and supervise muscle fatigue (47,48). Several teams have incorporated force sensors into the tricycle pedals to improve their stimulation algorithm, aiming to enhance performance by measuring muscle fatigue (48,49).…”

Section: Devices and Methodsmentioning

confidence: 99%

“…Mechanomyography sensors have been used to monitor muscle activity in real time during stimulation and supervise muscle fatigue (47,48). Several teams have incorporated force sensors into the tricycle pedals to improve their stimulation algorithm, aiming to enhance performance by measuring muscle fatigue (48,49). The strategy for modulating the FES stimulation diversified considerably from CYBATHLON 2016 to CYBATHLON 2020, ranging from manual on/off controllers for stimulator or interval pedaling, to simple proportional-integral-derivative controllers for cadence control, to complex reinforcement learning-based autotuning controllers with fatigue as the control variable (37,39,44,(50)(51)(52)(53).…”

Section: Devices and Methodsmentioning

confidence: 99%

How the CYBATHLON Competition Has Advanced Assistive Technologies

Jaeger

Baptista

Basla

et al. 2023

Annu. Rev. Control Robot. Auton. Syst.

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Approximately 1.1. billion people worldwide live with some form of disability, and assistive technology has the potential to increase their overall quality of life. However, the end users’ perspective and needs are often not sufficiently considered during the development of this technology, leading to frustration and nonuse of existing devices. Since its first competition in 2016, CYBATHLON has aimed to drive innovation in the field of assistive technology by motivating teams to involve end users more actively in the development process and to tailor novel devices to their actual daily-life needs. Competition tasks therefore represent unsolved daily-life challenges for people with disabilities and serve the purpose of benchmarking the latest developments from research laboratories and companies from around the world. This review describes each of the competition disciplines, their contributions to assistive technology, and remaining challenges in the user-centered development of this technology.

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“…Throughout an episode, the fatigue level changes as a function of applied stimulation intensity and the fatigue rate. As a comparison baseline, we also implement a PID controller on all our tasks, due to it being the conventional method used in many FES control applications such as cycling [24], [25] and arm motion [26]. In tasks requiring control of multiple muscles, such as cycling, PID control is used together with a pre-defined active muscle pattern that determines to which muscles the stimulation is applied.…”

Section: Controller Setup and Learningmentioning

confidence: 99%

I am Robot: Neuromuscular Reinforcement Learning to Actuate Human Limbs through Functional Electrical Stimulation

Wannawas,

Shafti,

Faisal

2021

Preprint

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Human movement disorders or paralysis lead to the loss of control of muscle activation and thus motor control. Functional Electrical Stimulation (FES) is an established and safe technique for contracting muscles by stimulating the skin above a muscle to induce its contraction. However, an open challenge remains on how to restore motor abilities to human limbs through FES, as the problem of controlling the stimulation is unclear. We are taking a robotics perspective on this problem, by developing robot learning algorithms that control the ultimate humanoid robot, the human body, through electrical muscle stimulation. Human muscles are not trivial to control as actuators due to their force production being non-stationary as a result of fatigue and other internal state changes, in contrast to robot actuators which are wellunderstood and stationary over broad operation ranges. We present our Deep Reinforcement Learning approach to the control of human muscles with FES, using a recurrent neural network for dynamic state representation, to overcome the unobserved elements of the behaviour of human muscles under external stimulation. We demonstrate our technique both in neuromuscular simulations but also experimentally on a human. Our results show that our controller can learn to manipulate human muscles, applying appropriate levels of stimulation to achieve the given tasks while compensating for advancing muscle fatigue which arises throughout the tasks. Additionally, our technique can learn quickly enough to be implemented in real-world human-in-the-loop settings.

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Mechanomyography Based Closed-Loop Functional Electrical Stimulation Cycling System

Cited by 5 publications

References 18 publications

Neuromechanics-based Deep Reinforcement Learning of Neurostimulation Control in FES cycling

Neuromechanics-based Deep Reinforcement Learning of Neurostimulation Control in FES cycling

How the CYBATHLON Competition Has Advanced Assistive Technologies

I am Robot: Neuromuscular Reinforcement Learning to Actuate Human Limbs through Functional Electrical Stimulation

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