"Wink to grasp" - comparing eye, voice & EMG gesture control of grasp with soft-robotic gloves.

Noronha, Bernardo; Dziemian, Sabine; Zito, Giuseppe Angelo; Konnaris, Charalambos; Faisal, A Aldo (2017). "Wink to grasp" - comparing eye, voice & EMG gesture control of grasp with soft-robotic gloves. IEEE International Conference on Rehabilitation Robotics (ICORR), 2017, pp. 1043-1048. IEEE 10.1109/ICORR.2017.8009387

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The ability of robotic rehabilitation devices to support paralysed end-users is ultimately limited by the degree to which human-machine-interaction is designed to be effective and efficient in translating user intention into robotic action. Specifically, we evaluate the novel possibility of binocular eye-tracking technology to detect voluntary winks from involuntary blink commands, to establish winks as a novel low-latency control signal to trigger robotic action. By wearing binocular eye-tracking glasses we enable users to directly observe their environment or the actuator and trigger movement actions, without having to interact with a visual display unit or user interface. We compare our novel approach to two conventional approaches for controlling robotic devices based on electromyo-graphy (EMG) and speech-based human-computer interaction technology. We present an integrated software framework based on ROS that allows transparent integration of these multiple modalities with a robotic system. We use a soft-robotic SEM glove (Bioservo Technologies AB, Sweden) to evaluate how the 3 modalities support the performance and subjective experience of the end-user when movement assisted. All 3 modalities are evaluated in streaming, closed-loop control operation for grasping physical objects. We find that wink control shows the lowest error rate mean with lowest standard deviation of (0.23 ± 0.07, mean ± SEM) followed by speech control (0.35 ± 0. 13) and EMG gesture control (using the Myo armband by Thalamic Labs), with the highest mean and standard deviation (0.46 ± 0.16). We conclude that with our novel own developed eye-tracking based approach to control assistive technologies is a well suited alternative to conventional approaches, especially when combined with 3D eye-tracking based robotic end-point control.

Item Type:

Journal Article (Original Article)

Division/Institute:

04 Faculty of Medicine > Department of Head Organs and Neurology (DKNS) > Clinic of Neurology

UniBE Contributor:

Zito, Giuseppe Angelo

Subjects:

600 Technology > 610 Medicine & health

ISSN:

1945-7901

Publisher:

IEEE

Language:

English

Submitter:

Stefanie Hetzenecker

Date Deposited:

01 Mar 2018 09:17

Last Modified:

25 Oct 2019 14:03

Publisher DOI:

10.1109/ICORR.2017.8009387

PubMed ID:

28813959

BORIS DOI:

10.7892/boris.108817

URI:

https://boris.unibe.ch/id/eprint/108817

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