Hybrid Control Interface of a Semi-soft Assistive Glove for People with Spinal Cord Injuries

Kaneishi, Daisuke; Matthew, Robert Peter; Leu, Jessica; O'Donnell, Julia; Zhang, Bike; Tomizuka, Masayoshi; Stuart, Hannah

doi:10.1109/icorr.2019.8779427

Cited by 9 publications

(2 citation statements)

References 12 publications

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“…Prior work suggests that constraining wrist position during reach-to-grasp results in excessive compensatory movements [10]- [12]. We hypothesize that the kinematic constraint due to coupled hand and wrist function in tenodesis grasping likewise induces atypical and exaggerated upper body motions.…”

Section: Introductionmentioning

confidence: 81%

Tenodesis Grasp Emulator: Kinematic Assessment of Wrist-Driven Orthotic Control

Chang¹,

Raghid²,

McPherson³

et al. 2021

Preprint

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Wrist-driven orthotics have been designed to assist people with C6-7 spinal cord injury, however, the kinematic constraint imposed by such a control strategy can impede mobility and lead to abnormal body motion. This study characterizes body compensation using the novel Tenodesis Grasp Emulator, an adaptor orthotic that allows for the investigation of tenodesis grasping in subjects with unimpaired hand function. Subjects perform a series of grasp-and-release tasks in order to compare normal (test control) and constrained wrist-driven modes, showing significant compensation as a result of the constraint. A motor-augmented mode is also compared against traditional wrist-driven operation, to explore the potential role of hybrid human-robot control. We find that both the passive wrist-driven and motor-augmented modes fulfill different roles throughout various tasks tested. Thus, we conclude that a flexible control scheme that can alter intervention based on the task at hand holds the potential to reduce compensation in future work.

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Section: Introductionmentioning

confidence: 81%

Tenodesis Grasp Emulator: Kinematic Assessment of Wrist-Driven Orthotic Control

Chang¹,

Raghid²,

McPherson³

et al. 2021

Preprint

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“…Human intents, as reference inputs, have been integrated to guide the operation of exoskeletons [9,15]. Biological sensors, such as surface electromyography (sEMG) [9,15,20,43,59,61,62] and electroencephalography (EEG); motion sensors, such as inertial measurement units (IMUs) [10,14], electromagnetic tracking [9], and infrared distance sensors [31]; internal pressure and tactile force sensors; or a combination of these sensors [15] have been used. Though these approaches provide intuitive gross hand and finger motion control, the aforementioned algorithms are mainly for a pure sensor-based position or grip force control, which lacks the required hybrid force-position-based dexterous manipulation to perform fine motor skills.…”

Section: Introductionmentioning

confidence: 99%

Trajectory Control in Discrete-Time Nonlinear Coupling Dynamics of a Soft Exo-Digit and a Human Finger Using Input–Output Feedback Linearization

Alam,

Shedd,

Haghshenas-Jaryani

2023

Automation

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This paper presents a quasi-static model-based control algorithm for controlling the motion of a soft robotic exo-digit with three independent actuation joints physically interacting with the human finger. A quasi-static analytical model of physical interaction between the soft exo-digit and a human finger model was developed. Then, the model was presented as a nonlinear discrete-time multiple-input multiple-output (MIMO) state-space representation for the control system design. Input–output feedback linearization was utilized and a control input was designed to linearize the input–output, where the input is the actuation pressure of an individual soft actuator, and the output is the pose of the human fingertip. The asymptotic stability of the nonlinear discrete-time system for trajectory tracking control is discussed. A soft robotic exoskeleton digit (exo-digit) and a 3D-printed human-finger model integrated with IMU sensors were used for the experimental test setup. An Arduino-based electro-pneumatic control hardware was developed to control the actuation pressure of the soft exo-digit. The effectiveness of the controller was examined through simulation studies and experimental testing for following different pose trajectories corresponding to the human finger pose during the activities of daily living. The model-based controller was able to follow the desired trajectories with a very low average root-mean-square error of 2.27 mm in the x-direction, 2.75 mm in the y-direction, and 3.90 degrees in the orientation of the human finger distal link about the z-axis.

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