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

Bandara, H. M. Ravindu T.; Priyanayana, K. S.; Jayasekara, A. G. Buddhika P.; Chandima, D. P.; Gopura, R. A. R. C.

doi:10.1155/2020/9160528

Cited by 8 publications

(4 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are various types of intelligent wheelchairs developed in the literature based on the input method [25]. There are two major types of input methods available in the literature.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Navigation Decision Control Mechanism for Intelligent Wheel-Chair

Bandara,

Priyanayana,

Chandima

et al. 2023

IEEE Access

Self Cite

View full text Add to dashboard Cite

The continuous rising of the elderly/disabled population has created a requirement for assistive robotics devices to counter the lack of trustworthy servants. Intelligent Wheelchairs are developed for that particular purpose. Intelligent Wheelchairs differ depending on the interactive modality and most commonly found modalities are speech-controlled. Since these are assistive devices that need to act as human companions, it is necessary to have a dialogue between the device and the user. Even though the wheelchair is fully automated, the user should have control over it at some point. However, this exchange of control should be intelligent and transitions need to be executed in order to safeguard the user. Therefore the purpose of this paper is to propose an intelligent system that would navigate an intelligent voice-controlled wheelchair facilitating the intelligent exchange of control between the user and the wheelchair. This control is not simultaneous and one can override the other only when navigation could lead to collisions. In the proposed method, users can control the wheelchair using fixed vocal commands, and execution of those commands will be performed using the spatial and control parameters. Control of the wheelchair will be exchanged between the user and the wheelchair itself considering specific parameters such as obstacle distance, collision time, the velocity of the wheelchair among others. User control mode has 5 definite vocal commands with classifiers to identify any navigation command into the command model and considers uncertain terms such as 'little' and 'hard' for 'Turn' commands. Command classification had produced a Cohen's Kappa value of 0.9462 and the classifier for the uncertain terms had produced a Cohen's Kappa value of 0.7325. Both were acceptable values for those particular classifications. As per the experiment results, the proposed system reduced the vocal command frequency and risk of collisions through proper control of the velocity levels and intelligent exchange of control at given locations.

show abstract

“…There are various types of intelligent wheelchairs developed in the literature based on the input method [25]. There are two major types of input methods available in the literature.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Navigation Decision Control Mechanism for Intelligent Wheel-Chair

Bandara,

Priyanayana,

Chandima

et al. 2023

IEEE Access

Self Cite

View full text Add to dashboard Cite

show abstract

“…Furthermore, distraction can play a role because a user who encounters an obstacle is more concerned with safely avoiding the hazard than reporting it to others on the app. To remedy this challenge, emerging voice-based assistive technologies can provide alternative hands-free reporting mechanisms to alleviate the burden of reporting obstacles with hands [44][45][46].…”

Section: Vulnerable Road User Mobility Assistance Platformmentioning

confidence: 99%

Adaptive personalized routing for vulnerable road users

Darko

Folsom

Pugh

et al. 2022

IET Intelligent Trans Sys

View full text Add to dashboard Cite

This research presents an adaptive and personalized routing model that enables individuals with mobility impairments to save their route preferences to a mobility assistant platform. The proactive approach based on anticipated user need accommodates vulnerable road users' personalized optimum dynamic routing rather than a reactive approach passively awaiting input. Most currently available trip planners target the general public's use of simpler route options prioritized based on static road characteristics. These static normative approaches are only satisfactory when conditions of intermediate intersections in the network are consistent, a constant rate of change occurs per each change of the segment condition, and the same fixed routes are valid every day regardless of the user preference. In this study, the vulnerable road user mobility problem is modeled by accommodating personalized preferences changing by time, sidewalk segment traversability, and the interaction between sidewalk factors and weather conditions for each segment contributing to a path choice. The developed reinforcement learning solution presents a lower average cost of personalized, accessible, and optimal path choices in various trip scenarios and superior to traditional shortest path algorithms (e.g., Dijkstra) with static and dynamic extensions.

show abstract

“…The important assets of vision-based techniques are large flexibility for users, low hardware requirements, and no health issues. These methods have the potential to develop any natural interface for remote humanmachine interaction, this can ease the living of physically challenged or elderly people with impaired mobility [2,9,15].…”

Section: Components Of Dhgrmentioning

confidence: 99%

Cognitive Visual Tracking of Hand Gestures in Real-Time RGB Videos

Golash¹,

Jain²

2022

Artificial Intelligence

View full text Add to dashboard Cite

Real-time visual hand tracking is quite different from commonly tracked objects in RGB videos. Because the hand is a biological object and hence suffers from both physical and behavioral variations during its movement. Furthermore, the hand acquires a very small area in the image frame, and due to its erratic pattern of movement, the quality of images in the video is affected considerably, if recorded from a simple RGB camera. In this chapter, we propose a hybrid framework to track the hand movement in RGB video sequences. The framework integrates the unique features of the Faster Region-based Convolutional Neural Network (Faster R-CNN) built on Residual Network and Scale-Invariant Feature Transform (SIFT) algorithm. This combination is enriched with the discriminative learning power of deep neural networks and the fast detection capability of hand-crafted features SIFT. Thus, our method online adapts the variations occurring in real-time hand movement and exhibits high efficiency in cognitive recognition of hand trajectory. The empirical results shown in the chapter demonstrate that the approach can withstand the intrinsic as well as extrinsic challenges associated with visual tracking of hand gestures in RGB videos.

show abstract

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

Cited by 8 publications

References 27 publications

Hybrid Navigation Decision Control Mechanism for Intelligent Wheel-Chair

Hybrid Navigation Decision Control Mechanism for Intelligent Wheel-Chair

Adaptive personalized routing for vulnerable road users

Cognitive Visual Tracking of Hand Gestures in Real-Time RGB Videos

Contact Info

Product

Resources

About