Intelligent wheelchair control strategies for older adults with cognitive impairment: user attitudes, needs, and preferences

Viswanathan, Pooja; Zambalde, Ellen Pereira; Foley, Geneviève; Graham, Julianne L.; Wang, Rosalie H.; Adhikari, Bikram; Mackworth, Alan K.; Mihailidis, Alex; Miller, William C.; Mitchell, Ian M.

doi:10.1007/s10514-016-9568-y

Cited by 35 publications

(27 citation statements)

References 35 publications

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“…PerMMA [6] combines manipulation and mobility assistance in support of complete independence for its users. A wizard of oz experiment (in which a human teleoperator simulates the SWC) was used in [7] to explore SWC control strategies for older adults with cognitive impairment, including user attitudes, needs, and preferences. Seating pressure sensors are used to monitor the user in [8].…”

Section: A Smart Powered Wheelchairsmentioning

confidence: 99%

Learning Motion Predictors for Smart Wheelchair Using Autoregressive Sparse Gaussian Process

Fan

Meng

Chen

et al. 2018

2018 IEEE International Conference on Robotics and Automation (ICRA)

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Constructing a smart wheelchair on a commercially available powered wheelchair (PWC) platform avoids a host of seating, mechanical design and reliability issues but requires methods of predicting and controlling the motion of a device never intended for robotics. Analog joystick inputs are subject to black-box transformations which may produce intuitive and adaptable motion control for human operators, but complicate robotic control approaches; furthermore, installation of standard axle mounted odometers on a commercial PWC is difficult. In this work, we present an integrated hardware and software system for predicting the motion of a commercial PWC platform that does not require any physical or electronic modification of the chair beyond plugging into an industry standard auxiliary input port. This system uses an RGB-D camera and an Arduino interface board to capture motion data, including visual odometry and joystick signals, via ROS communication. Future motion is predicted using an autoregressive sparse Gaussian process model. We evaluate the proposed system on real-world short-term path prediction experiments. Experimental results demonstrate the system's efficacy when compared to a baseline neural network model.

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Section: A Smart Powered Wheelchairsmentioning

confidence: 99%

Learning Motion Predictors for Smart Wheelchair Using Autoregressive Sparse Gaussian Process

Fan

Meng

Chen

et al. 2018

2018 IEEE International Conference on Robotics and Automation (ICRA)

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“…However there remains no clear consensus within the literature on which is the right way to accomplish shared control, and there is reason to believe that what is "right" may differ for each person. The work of Viswanathan et al (2016) contributed to this Special Issue gathers user feedback about various control modes, with a focus on different levels of cognitive impairment in the human operator. In order to first solicit user-feedback before complete system building, they opt for a wizard-of-oz paradigm that has a human experimenter provide the assistive control commands.…”

Section: Assistive Robots For Manipulation and Mobilitymentioning

confidence: 99%

Special Issue on Assistive and Rehabilitation Robotics

Mohammed

Park²,

Park

et al. 2017

Auton Robot

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“…Furthermore, vocal and gestural expressions can be integrated to create navigation commands [9]. As an example, a person in a wheelchair might say "go there" and the person can integrate hand gesture to the sentence by showing which direction that he wants to move [10]. Vocal command may contain uncertain terms such as distance and direction expressing terms like "near," "far," "middle," "left," and "right" or place expressing terms like "here" and "there" [11].…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Gesture Classification Model for Domestic Wheelchair Navigation with Gesture Variance Compensation

Bandara

Priyanayana

Jayasekara

et al. 2020

Applied Bionics and Biomechanics

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Elderly and disabled population is rapidly increasing. It is important to uplift their living standards by improving the confidence towards daily activities. Navigation is an important task, most elderly and disabled people need assistance with. Replacing human assistance with an intelligent system which is capable of assisting human navigation via wheelchair systems is an effective solution. Hand gestures are often used in navigation systems. However, those systems do not possess the capability to accurately identify gesture variances. Therefore, this paper proposes a method to create an intelligent gesture classification system with a gesture model which was built based on human studies for every essential motion in domestic navigation with hand gesture variance compensation capability. Experiments have been carried out to evaluate user remembering and recalling capability and adaptability towards the gesture model. Dynamic Gesture Identification Module (DGIM), Static Gesture Identification Module (SGIM), and Gesture Clarifier (GC) have been introduced in order to identify gesture commands. The proposed system was analyzed for system accuracy and precision using results of the experiments conducted with human users. Accuracy of the intelligent system was determined with the use of confusion matrix. Further, those results were analyzed using Cohen’s kappa analysis in which overall accuracy, misclassification rate, precision, and Cohen’s kappa values were calculated.

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Intelligent wheelchair control strategies for older adults with cognitive impairment: user attitudes, needs, and preferences

Cited by 35 publications

References 35 publications

Learning Motion Predictors for Smart Wheelchair Using Autoregressive Sparse Gaussian Process

Learning Motion Predictors for Smart Wheelchair Using Autoregressive Sparse Gaussian Process

Special Issue on Assistive and Rehabilitation Robotics

An Intelligent Gesture Classification Model for Domestic Wheelchair Navigation with Gesture Variance Compensation

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