IMU-based assistance modulation in upper limb soft wearable exosuits

Little, Kieran; Antuvan, Chris Wilson; Xiloyannis, Michele; A.P.S., de Noronha Bernardo; Kim, Yongtae G.; Masia, Lorenzo; Accoto, Dino

doi:10.1109/icorr.2019.8779362

Cited by 28 publications

(17 citation statements)

References 22 publications

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“…The elbow exosuit used in this study, previously described in [10,13,33], supports the performance of elbow movements (Fig. 1a).…”

Section: A Elbow Exosuitsupporting

confidence: 73%

“…In the last decade, research endeavours have shifted the focus towards softer devices. Typically created from deformable materials that improve their biomimetic properties, these wearable robots are generally more ergonomic and safer [10][11][12][13][14]. The soft properties of these devices are intrinsically related to the type of actuation, being based mostly either in fluidic or cable transmissions.…”

Section: Introductionmentioning

confidence: 99%

“…In this study, we use our previously introduced soft exosuit meant for elbow assistance [13] and a soft-rigid hybrid exoskeleton supporting hand extension. We explore the feasibility of using this integrated wearable system as an assistive tool for performing ADLs on a sample of chronic stroke patients.…”

Section: Introductionmentioning

confidence: 99%

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Soft, Lightweight Wearable Robots to Support the Upper Limb in Activities of Daily Living: A Feasibility Study on Chronic Stroke Patients

Noronha

Little

et al. 2022

IEEE Trans. Neural Syst. Rehabil. Eng.

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Stroke can be a devastating condition that impairs the upper limb and reduces mobility. Wearable robots can aid impaired users by supporting performance of Activities of Daily Living (ADLs). In the past decade, soft devices have become popular due to their inherent malleable and low-weight properties that makes them generally safer and more ergonomic. In this study, we present an improved version of our previously developed gravity-compensating upper limb exosuit and introduce a novel hand exoskeleton. The latter uses 3D-printed structures that are attached to the back of the fingers which prevent undesired hyperextension of joints. We explored the feasibility of using this integrated system in a sample of 10 chronic stroke patients who performed 10 ADLs. We observed a significant reduction of 30.3 ± 3.5% (mean ± standard error), 31.2 ± 3.2% and 14.0 ± 5.1% in the mean muscular activity of the Biceps Brachii (BB), Anterior Deltoid (AD) and Extensor Digitorum Communis muscles, respectively. Additionally, we observed a reduction of 14.0 ± 11.5%, 14.7 ± 6.9% and 12.8 ± 4.4% in the coactivation of the pairs of muscles BB and Triceps Brachii (TB), BB and AD, and TB and Pectoralis Major (PM), respectively, typically associated to pathological muscular synergies, without significant degradation of healthy muscular coactivation. There was also a significant increase of elbow flexion angle (12.1±1.5°). These results further cement the potential of using lightweight wearable devices to assist impaired users.

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“…The elbow exosuit used in this study, previously described in [10,13,33], supports the performance of elbow movements (Fig. 1a).…”

Section: A Elbow Exosuitsupporting

confidence: 73%

Section: Introductionmentioning

confidence: 99%

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Soft, Lightweight Wearable Robots to Support the Upper Limb in Activities of Daily Living: A Feasibility Study on Chronic Stroke Patients

Noronha

Little

et al. 2022

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…An IMU was used to acquire information regarding arm movement of the exoskeleton's user and to use this information as feedback signals in the control loop to improve the response of the exoskeleton. 8,[24][25][26] In Atia and Salah, 24 two IMU sensors were used in a control loop to track arm motion, while in Little et al 25 three IMU sensors were used to measure the elbow flexion and shoulder joint elevation. The measured data were used in their previously developed controller, which was based on gravity compensation.…”

Section: Introductionmentioning

confidence: 99%

Quantitative analysis and control of the torque profile of the upper limb using a kinetic model and motion measurements

Abdul-Ameer

2022

Int J Artif Organs

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This paper investigates a new approach to the rapid control of an upper limb exoskeleton actuator. We used a mathematical model and motion measurements of a human arm to estimate joint torque as a means to control the exoskeleton’s actuator. The proposed arm model is based on a two-pendulum configuration and is used to obtain instantaneous joint torques which are then passed into control law to regulate the actuator torque. Nine subjects volunteered to take part in the experimental protocol, in which inertial measurement units (IMUs) and a digital goniometer were used to measure and estimate the torque profiles. To validate the control law, a Simscape model was developed to simulate the arm model and control law in which measurement data from IMUs and a goniometer were fed into the suggested Simscape model. The arm torque profiles are key to the control approach and should be traced by torques produced by the exoskeleton actuators to provide comfort and flexibility for the subjects. A DC motor was used as an actuator for the exoskeleton, and its model was used in the physical Simscape model. To reduce the error in the driving torque compared with the reference arm torque, a PID controller was implemented. The results show the potential of our methodology for tracking and controlling the actuator’s torque, in which the mean square error was reduced to less than 0.2 - a significantly low value.

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“…Human-machine interfaces (HMIs) are widely used in a range of applications: control of assistive robots, such as robotic prostheses [ 1 , 2 ] and orthoses [ 3 , 4 , 5 , 6 ], or teleoperation [ 7 , 8 ]. Motion intention detection (MID) is fundamental for implementing HMIs, as it allows for an efficient, hands-free interaction between the user and the device [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Elbow Motion Trajectory Prediction Using a Multi-Modal Wearable System: A Comparative Analysis of Machine Learning Techniques

Little

Pappachan

Yang

et al. 2021

Sensors

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Motion intention detection is fundamental in the implementation of human-machine interfaces applied to assistive robots. In this paper, multiple machine learning techniques have been explored for creating upper limb motion prediction models, which generally depend on three factors: the signals collected from the user (such as kinematic or physiological), the extracted features and the selected algorithm. We explore the use of different features extracted from various signals when used to train multiple algorithms for the prediction of elbow flexion angle trajectories. The accuracy of the prediction was evaluated based on the mean velocity and peak amplitude of the trajectory, which are sufficient to fully define it. Results show that prediction accuracy when using solely physiological signals is low, however, when kinematic signals are included, it is largely improved. This suggests kinematic signals provide a reliable source of information for predicting elbow trajectories. Different models were trained using 10 algorithms. Regularization algorithms performed well in all conditions, whereas neural networks performed better when the most important features are selected. The extensive analysis provided in this study can be consulted to aid in the development of accurate upper limb motion intention detection models.

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IMU-based assistance modulation in upper limb soft wearable exosuits

Cited by 28 publications

References 22 publications

Soft, Lightweight Wearable Robots to Support the Upper Limb in Activities of Daily Living: A Feasibility Study on Chronic Stroke Patients

Soft, Lightweight Wearable Robots to Support the Upper Limb in Activities of Daily Living: A Feasibility Study on Chronic Stroke Patients

Quantitative analysis and control of the torque profile of the upper limb using a kinetic model and motion measurements

Elbow Motion Trajectory Prediction Using a Multi-Modal Wearable System: A Comparative Analysis of Machine Learning Techniques

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