Cognitive Decision Making for Navigation Assistance Based on Intent Recognition

Pushp, Sumant; Bhardwaj, Basant; Hazarika, Shyamanta M.

doi:10.1007/978-3-319-71928-3_9

Cited by 4 publications

(2 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The human-machine interaction system can work towards achieving the goal only when it supports some varied capabilities that are required for a system to achieve human level intelligence [17,18]. Desiderata for completeness and evaluation of a cognitive agent for human machine communication, whether directly or through embedded process are: Adaption could be bolstered if one can properly categorize knowledge and can recognize and extract rational knowledge.…”

Section: Desideratamentioning

confidence: 99%

Declarative Memory-based Structure for the Representation of Text Data

Pushp,

Kashmira,

Hazarika

2020

Preprint

Self Cite

View full text Add to dashboard Cite

In the era of intelligent computing, computational progress in text processing is an essential consideration. Many systems have been developed to process text over different languages. Though, there is considerable development, they still lack in understanding of the text, i.e., instead of keeping text as knowledge, many treat text as a data. In this work we introduce a text representation scheme which is influenced by human memory infrastructure. Since texts are declarative in nature, a structural organization would foster efficient computation over text. We exploit long term episodic memory to keep text information observed over time. This not only keep fragments of text in an organized fashion but also reduces redundancy and stores the temporal relation among them.Wordnet has been used to imitate semantic memory, which works at word level to facilitate the understanding about individual words within text. Experimental results of various operation performed over episodic memory and growth of knowledge infrastructure over time is reported.

show abstract

Section: Desideratamentioning

confidence: 99%

Declarative Memory-based Structure for the Representation of Text Data

Pushp,

Kashmira,

Hazarika

2020

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Furthermore, misunderstanding of unintended hand movements can create critical situations. Therefore, safety precautions should be also considered [17].…”

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