Abstract
Neurorobotic augmentation (e.g., robotic assist) is now in regular use to support individuals suffering from impaired motor functions. A major unresolved challenge, however, is the excessive cognitive load necessary for the human–machine interface (HMI). Grasp control remains one of the most challenging HMI tasks, demanding simultaneous, agile, and precise control of multiple degrees-of-freedom (DoFs) while following a specific timing pattern in the joint and human–robot task spaces. Most commercially available systems use either an indirect mode-switching configuration or a limited sequential control strategy, limiting activation to one DoF at a time. To address this challenge, we introduce a shared autonomy framework centred around a low-cost multi-modal sensor suite fusing: (a) mechanomyography (MMG) to estimate the intended muscle activation, (b) camera-based visual information for integrated autonomous object recognition, and (c) inertial measurement to enhance intention prediction based on the grasping trajectory. The complete system predicts user intent for grasp based on measured dynamical features during natural motions. A total of 84 motion features were extracted from the sensor suite, and tests were conducted on 10 able-bodied and 1 amputee participants for grasping common household objects with a robotic hand. Real-time grasp classification accuracy using visual and motion features obtained 100%, 82.5%, and 88.9% across all participants for detecting and executing grasping actions for a bottle, lid, and box, respectively. The proposed multimodal sensor suite is a novel approach for predicting different grasp strategies and automating task performance using a commercial upper-limb prosthetic device. The system also shows potential to improve the usability of modern neurorobotic systems due to the intuitive control design.
Highlights
The absence of the means to interact with the environment represents a steep barrier in quality of life for people with acquired or congenital upper limb deficiency
Field operation of myoelectric prostheses relies on button activations, manual switching functions, and electromyography (EMG) input to iterate through different grasp modes
Remark—limitations and future perspectives: In this study, we proposed the feasibility of use of a multi-modal sensing unit using a common classification algorithm (i.e., K-nearest neighbour (KNN) classifier) to detect user intent and classify grasp patterns to interact with different objects
Summary
The absence of the means to interact with the environment represents a steep barrier in quality of life for people with acquired or congenital upper limb deficiency. The vital role of the hand in basic activities of daily living (ADLs) is well-acknowledged [1]. Sensors 2020, 20, 6097 and complexity of the hand, which involves more than twenty degrees-of-freedom (DoFs), is still exceptionally challenging to capture artificially [2]. The development of advanced myoelectric prostheses in the last decades has led to innovative designs of multi-DoF control systems (e.g., [3,4]). A highly functional, robust, intuitive, and natural human–robot interface is yet to be developed. Field operation of myoelectric prostheses relies on button activations, manual switching functions, and electromyography (EMG) input to iterate through different grasp modes
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