Adaptive Model-Based Myoelectric Control for a Soft Wearable Arm Exosuit: A New Generation of Wearable Robot Control

Lotti, Nicola; Xiloyannis, Michele; Durandau, Guillaume; Galofaro, Elisa; Sanguineti, Vittorio; Masia, Lorenzo; Sartori, Massimo

doi:10.1109/mra.2019.2955669

Cited by 120 publications

(104 citation statements)

References 20 publications

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“…On the other hand, the ability of using non-invasive wearable sensor data (e.g., electromyography surface electrodes, thin-film low-profile ultrasonography probes) to drive forward subject-specific neuromuscular models will lead to a framework for deriving high-fidelity estimates of human neuromuscular function [ 141 , 142 ]. This is expected to provide new avenues to inform wearable device controllers of the user’s current physiological state or determine the optimal body postures to prevent musculoskeletal injuries on the long term [ 7 , 8 , 143 ].…”

Section: Discussionmentioning

confidence: 99%

“…SOPHIA aims at developing a new generation of human–robot collaboration (HRC) technologies (namely, human augmentation technologies such as wearbots and cobots) able to improve, among others, human ergonomics during the execution of occupational manual handling activities. Wearbots, such as exoskeletons, are defined as wearable under-actuated devices with advanced interaction and sensing capabilities, designed to offload workers from internal loadings and to keep them in ergonomic and comfortable working conditions [ 7 , 8 ]. Cobots are defined as reconfigurable collaborative robots, capable of responding to task variations and to the worker’s intentions in a timely manner, while simultaneously offloading him/her from external loadings (task-related payloads) and keeping him/her in task-optimum and ergonomic working conditions.…”

Section: Introductionmentioning

confidence: 99%

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The Sensor-Based Biomechanical Risk Assessment at the Base of the Need for Revising of Standards for Human Ergonomics

Ranavolo

Ajoudani

Cherubini

et al. 2020

Sensors

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Due to the epochal changes introduced by “Industry 4.0”, it is getting harder to apply the varying approaches for biomechanical risk assessment of manual handling tasks used to prevent work-related musculoskeletal disorders (WMDs) considered within the International Standards for ergonomics. In fact, the innovative human–robot collaboration (HRC) systems are widening the number of work motor tasks that cannot be assessed. On the other hand, new sensor-based tools for biomechanical risk assessment could be used for both quantitative “direct instrumental evaluations” and “rating of standard methods”, allowing certain improvements over traditional methods. In this light, this Letter aims at detecting the need for revising the standards for human ergonomics and biomechanical risk assessment by analyzing the WMDs prevalence and incidence; additionally, the strengths and weaknesses of traditional methods listed within the International Standards for manual handling activities and the next challenges needed for their revision are considered. As a representative example, the discussion is referred to the lifting of heavy loads where the revision should include the use of sensor-based tools for biomechanical risk assessment during lifting performed with the use of exoskeletons, by more than one person (team lifting) and when the traditional methods cannot be applied. The wearability of sensing and feedback sensors in addition to human augmentation technologies allows for increasing workers’ awareness about possible risks and enhance the effectiveness and safety during the execution of in many manual handling activities.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Sensor-Based Biomechanical Risk Assessment at the Base of the Need for Revising of Standards for Human Ergonomics

Ranavolo

Ajoudani

Cherubini

et al. 2020

Sensors

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“…The resulting simulation estimates body states, such as individual muscle forces, that are difficult to directly measure with an experiment. This approach has been extensively used to analyze human locomotion [36,38], to estimate body state in real-time to control assistive devices [39,40], and to predict effects of exoskeleton assistance [41] and surgical interventions [42] on muscle coordination.…”

Section: Musculoskeletal Simulationsmentioning

confidence: 99%

Deep reinforcement learning for modeling human locomotion control in neuromechanical simulation

Song

Kidziński

Peng

et al. 2020

Preprint

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Modeling human motor control and predicting how humans will move in novel environments is a grand scientific challenge. Despite advances in neuroscience techniques, it is still difficult to measure and interpret the activity of the millions of neurons involved in motor control. Thus, researchers in the fields of biomechanics and motor control have proposed and evaluated motor control models via neuromechanical simulations, which produce physically correct motions of a musculoskeletal model. Typically, researchers have developed control models that encode physiologically plausible motor control hypotheses and compared the resulting simulation behaviors to measurable human motion data. While such plausible control models were able to simulate and explain many basic locomotion behaviors (e.g. walking, running, and climbing stairs), modeling higher layer controls (e.g. processing environment cues, planning long-term motion strategies, and coordinating basic motor skills to navigate in dynamic and complex environments) remains a challenge. Recent advances in deep reinforcement learning lay a foundation for modeling these complex control processes and controlling a diverse repertoire of human movement; however, reinforcement learning has been rarely applied in neuromechanical simulation to model human control. In this paper, we review the current state of neuromechanical simulations, along with the fundamentals of reinforcement learning, as it applies to human locomotion. We also present a scientific competition and accompanying software platform, which we have organized to accelerate the use of reinforcement learning in neuromechanical simulations. This “Learn to Move” competition, which we have run annually since 2017 at the NeurIPS conference, has attracted over 1300 teams from around the world. Top teams adapted state-of-art deep reinforcement learning techniques to produce complex motions, such as quick turning and walk-to-stand transitions, that have not been demonstrated before in neuromechanical simulations without utilizing reference motion data. We close with a discussion of future opportunities at the intersection of human movement simulation and reinforcement learning and our plans to extend the Learn to Move competition to further facilitate interdisciplinary collaboration in modeling human motor control for biomechanics and rehabilitation research.

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“…These advantages were previously demonstrated by our previous works where the HMI can estimate joint torques in real-time for a wide range of tasks such as walking, backward locomotion, calf rise, jumping, sidestepping and calf rising [79]. Furthermore, it offers further advantage than the ability to predict joint torques on a wide range of tasks, it also offers great flexibility as it can easily be adapted to other body parts and wearable robotic devices such as upper-limbs, which we demonstrated when the proposed HMI was used to control a hand prosthesis in an amputee [18] and assisted via a soft exosuit, elbow's dynamic movement [143]. This method was also be applied successfully to post-stroke and spinal cord injury patients to voluntary control their knee and ankle joint simultaneously [144].…”

Section: Discussionmentioning

confidence: 99%

“…Nevertheless, as presented in this section, the issues of wearable robotic devices (prostheses and exoskeletons, for upper and lower-limbs) are common between them. The proposed HMI was tested on an upper-limb prosthesis [18] and an upper-limb soft exosuit [19] presenting the same biomechanical benefits as shown in this dissertation.…”

Section: Section: Introductionmentioning

confidence: 99%

Towards the next Generation in Human-machine-interfacing : : Controlling Wearable Robots via Neuromusculoskeletal Modelling

Durandau¹

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Adaptive Model-Based Myoelectric Control for a Soft Wearable Arm Exosuit: A New Generation of Wearable Robot Control

Cited by 120 publications

References 20 publications

The Sensor-Based Biomechanical Risk Assessment at the Base of the Need for Revising of Standards for Human Ergonomics

The Sensor-Based Biomechanical Risk Assessment at the Base of the Need for Revising of Standards for Human Ergonomics

Deep reinforcement learning for modeling human locomotion control in neuromechanical simulation

Towards the next Generation in Human-machine-interfacing : : Controlling Wearable Robots via Neuromusculoskeletal Modelling

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