Studying user-defined body gestures for navigating interactive maps

Guerrero-García, Josefina; González, Claudia; Pinto, David

doi:10.1145/3123818.3123851

Cited by 4 publications

(2 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Anja and Gebhard [ 19 ] conducted a usability study employing head movements and head poses to aid disabled people who cannot use their upper limbs. Garcia et al [ 20 ] used body gestures to navigate interactive maps.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Target Selections by ”Hand Gestures + Facial Expression” for a Rehabilitation Robot

Han

Zhang

et al. 2022

Sensors

View full text Add to dashboard Cite

In this study we propose a “hand gesture + face expression” human machine interaction technique, and apply this technique to bedridden rehabilitation robot. “Hand gesture + Facial expression” interactive technology combines the input mode of gesture and facial expression perception. It involves seven basic facial expressions that can be used to determine a target selecting task, while hand gestures are used to control a cursor’s location. A controlled experiment was designed and conducted to evaluate the effectiveness of the proposed hybrid technology. A series of target selecting tasks with different target widths and layouts were designed to examine the recognition accuracy of hybrid control gestures. An interactive experiment applied to a rehabilitation robot is designed to verify the feasibility of this interactive technology applied to rehabilitation robots. The experimental results show that the “hand + facial expression” interactive gesture has strong robustness, which can provide a novel guideline for designing applications in VR interfaces, and it can be applied to the rehabilitation robots.

show abstract

Section: Introductionmentioning

confidence: 99%

Hybrid Target Selections by ”Hand Gestures + Facial Expression” for a Rehabilitation Robot

Han

Zhang

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…Gestures included hand gestures, facial expressions, or cues which are difficult to understand even for human beings. 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].…”

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

View full text Add to dashboard Cite

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.

show abstract

Elicitation of Interaction Techniques with 3D Data Visualizations in Immersive Environment using HMDs

Aktar

Maurer

2022

2022 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct)

View full text Add to dashboard Cite

Studying user-defined body gestures for navigating interactive maps

Cited by 4 publications

References 18 publications

Hybrid Target Selections by ”Hand Gestures + Facial Expression” for a Rehabilitation Robot

Hybrid Target Selections by ”Hand Gestures + Facial Expression” for a Rehabilitation Robot

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

Elicitation of Interaction Techniques with 3D Data Visualizations in Immersive Environment using HMDs

Contact Info

Product

Resources

About