Towards the development of a voice-controlled exoskeleton system for restoring hand function

Wang, Xuefeng; Tran, Phillip; Callahan, Sarah M.; Wolf, Steven L.; Desai, Jaydev P.

doi:10.1109/ismr.2019.8710195

Cited by 11 publications

(3 citation statements)

References 23 publications

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“…Xuefeng Wang, et al [21] proposed using Apple's Siri to control their exoskeleton glove, with the program running on an iPhone. According to Apple, Siri has a relatively low (5%) word error rate and is able to run without noticeable latency on an iPhone.…”

Section: Towards the Development Of A Voice-controlled Exoskeleton Sy...mentioning

confidence: 99%

Personalized voice activated grasping system for a robotic exoskeleton glove

Guo

Pradhan

et al. 2022

Mechatronics

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Controlling an exoskeleton glove with a highly efficient human-machine interface (HMI), while accurately applying force to each joint remains a hot topic. This paper proposes a fast, secure, accurate, and portable solution to control an exoskeleton glove. This state of the art solution includes both hardware and software components. The exoskeleton glove uses a modified serial elastic actuator (SEA) to achieve accurate force sensing. A portable electronic system is designed based on the SEA to allow force measurement, force application, slip detection, cloud computing, and a power supply to provide over 2 hours of continuous usage. A voice-control-based HMI referred to as the integrated trigger-word configurable voice activation and speaker verification system (CVASV), is integrated into a robotic exoskeleton glove to perform high-level control. The CVASV HMI is designed for embedded systems with limited computing power to perform voice-activation and voice-verification simultaneously. The system uses MobileNet as the feature extractor to reduce computational cost. The HMI is tuned to allow better performance in grasping daily objects. This study focuses on applying the CVASV HMI to the exoskeleton glove to perform a stable grasp with force-control and slip-detection using SEA based exoskeleton glove. This research found that using MobileNet as the speaker verification neural network can increase the speed of processing while maintaining similar verification accuracy. I would also like to gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU.

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Section: Towards the Development Of A Voice-controlled Exoskeleton Sy...mentioning

confidence: 99%

Personalized voice activated grasping system for a robotic exoskeleton glove

Guo

Pradhan

et al. 2022

Mechatronics

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“…The inability to engage in the voluntary movement has likely had several detrimental effects on people's ability to improve their lifestyles in the past and still does so today. For instance, there is a huge limitation to the human's self-dependence, sensorimotor ability, neuroplasticity, and nature of life (Gassert and Dietz, 2018 ; Wagner et al, 2018 ; Wang et al, 2019 ; Bourbeau et al, 2020 ; Marquez-Chin and Popovic, 2020 ). In the United States (US) alone, nearly 610,000 people have a stroke for the first time every year, and 185,000 are periodically affected (Salmela et al, 2017 ).…”

Section: Introductionmentioning

confidence: 99%

Lower limb exoskeleton robot and its cooperative control: A review, trends, and challenges for future research

et al. 2023

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Effective control of an exoskeleton robot (ER) using a human-robot interface is crucial for assessing the robot's movements and the force they produce to generate efficient control signals. Interestingly, certain surveys were done to show off cutting-edge exoskeleton robots. The review papers that were previously published have not thoroughly examined the control strategy, which is a crucial component of automating exoskeleton systems. As a result, this review focuses on examining the most recent developments and problems associated with exoskeleton control systems, particularly during the last few years (2017–2022). In addition, the trends and challenges of cooperative control, particularly multi-information fusion, are discussed.

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“…Visionbased HMI requires minimal user action, but is low in precision and lacks initial grasp force planning ability (Kim et al, 2019;Pham et al, 2015;Ko et al, 2023;Calandra et al, 2018;Yamaguchi and Atkeson, 2017;Takamuku and Gomi, 2019). Voice-based HMI is known for its outstanding high accuracy, but lacks force planning ability (Guo et al, 2020;Wang et al, 2019;Kim et al, 2020). Force planning is critical for exoskeleton HMIs and can only be provided by the user through EMGbased HMIs.…”

Section: Introductionmentioning

confidence: 99%

Vision-Based Human-Machine Interface for an Assistive Robotic Exoskeleton Glove

Guo,

Xu,

Tzvi

2023

Preprint

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This paper presents a vision-based Human-Machine Interface (HMI) for an assistive exoskeleton glove, designed to incorporate force planning capabilities. While Electroencephalogram (EEG) and Electromyography (EMG)-based HMIs allow direct grasp force planning via user signals, voice and vision-based HMIs face limitations. In particular, two primary force planning methods encounter issues in these HMIs. First, traditional force optimization struggles with unfamiliar objects due to lack of object information. Second, the slip-grasp method faces a high failure rate due to inadequate initial grasp force. To address these challenges, this paper introduces a vision-based HMI to estimate the initial grasp forces of the target object. The initial grasp force estimation is performed based on the size and surface material of the target object. The experimental results demonstrate a grasp success rate of 87. 5%, marking significant improvements over the slip-grasp method (71.9%).

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Towards the development of a voice-controlled exoskeleton system for restoring hand function

Cited by 11 publications

References 23 publications

Personalized voice activated grasping system for a robotic exoskeleton glove

Personalized voice activated grasping system for a robotic exoskeleton glove

Lower limb exoskeleton robot and its cooperative control: A review, trends, and challenges for future research

Vision-Based Human-Machine Interface for an Assistive Robotic Exoskeleton Glove

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