An analytic approach to decipher usable gestures for quadriplegic users

Jiang, Hairong; Duerstock, Bradley S.; Wachs, Juan P.

doi:10.1109/smc.2014.6974542

Cited by 7 publications

(3 citation statements)

References 23 publications

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“…By assigning certain user gestures to respective keyboard press such as directional arrows, it is possible to deliver a meticulous control of certain precision. Video-based system [7][8][9][10][11] captures every movement of the user, which can mistakenly translate several unintended, accidental, or reflex movements. These systems also limit the usage of the HID to certain environments, such as places with quiet environment or places with well-lit lighting.…”

Section: Discussionmentioning

confidence: 99%

“…Several methods have been developed as solutions to this problem, for example, a voice-controlled system, 3,4 an electroencephalographic (EEG)-driven device, 5,6 mouth sticks, and several image recognition systems applied to head, eye, or facial movements. [7][8][9][10][11] Advancement in the voice recognition and artificial intelligence technology has made a voice-controlled system a good choice for an AT, but it still has some weaknesses in certain daily life scenarios. In some events that need detailed commands such as drawing an image using a mouse, voice-controlled system will be too arduous to be used.…”

Section: Introductionmentioning

confidence: 99%

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Electromyography-based gesture recognition for quadriplegic users using hidden Markov model with improved particle swarm optimization

Sigalingging

Budiarsa

Leu

et al. 2019

International Journal of Distributed Sensor Networks

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People with quadriplegia cannot move their body and limbs freely, making them unable to interact normally with their environment. This article aims to improve the life quality of quadriplegia patients through a development of a system to help them interact with their surroundings. A novel algorithm to classify human gestures is proposed in this article. The algorithm is developed as the core of an assistive technology system in the form of a human interface device, which utilizes electromyograph as its sensor. The system utilizes a wearable electromyograph with a custom software as the signal capturing and processing tool. The electrodes of the electromyograph are placed on certain positions on the face, corresponding to the locations of the major muscles that govern certain facial gestures. The signals are then processed using a novel algorithm that employs hidden Markov model and improved particle swarm optimization to classify the gesture. Based on the gestures, a custom command can be assigned for different conditions. The accuracy of the system is 96.25% for five gestures classification.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Electromyography-based gesture recognition for quadriplegic users using hidden Markov model with improved particle swarm optimization

Sigalingging

Budiarsa

Leu

et al. 2019

International Journal of Distributed Sensor Networks

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“…Imitating the movement of biological organisms has been a topic in animation [33] and robotics [4,27,22,29,2,18], which is often dependent on the parameterization of the creature's movement. In [40,27,9,19,20,14] motion capture data is used to seed artificial representations. In several of these cases, continuous, trajectory-based measurements for success have been posed.…”

Section: Introductionmentioning

confidence: 99%

An Information Theoretic Measure for Robot Expressivity

LaViers

2017

Preprint

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This paper presents a principled way to think about articulated movement for artificial agents and a measurement of platforms that produce such movement. In particular, in human-facing scenarios, the shape evolution of robotic platforms will become essential in creating systems that integrate -and communicate -with human counterparts. This paper provides a tool to measure the expressive capacity or expressivity of articulated platforms. To do this, it points to the synergistic relationship between computation and mechanization. Importantly, this way of thinking gives an information theoretic basis for measuring and comparing robots of increasing complexity and capability. The paper will provide concrete examples of this measure in application to current robotic platforms. It will also provide a comparison between the computational and mechanical capabilities of robotic platforms and analyze order-of-magnitude trends over the last 15 years. Implications for future work made by the paper are to provide a method by which to quantify movement imitation, outline a way of thinking about designing expressive robotic systems, and contextualize the capabilities of current robotic systems.

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