arXiv subjects

Sapna S Mishra

Publications and source records attributed to Sapna S Mishra.

2 recordsLinked to original sources

arXiv2025

Development of Immersive Virtual and Augmented Reality-Based Joint Attention Training Platform for Children with Autism

Joint Attention (JA), a crucial social skill for developing shared focus, is often impaired in children with Autism Spectrum Disorder (ASD), affecting social communication and highlighting the need for early intervention. Addressing gaps in prior research, such as limited use of immersive technology and reliance on distracting peripherals, we developed a novel JA training platform using Augmented Reality (AR) and Virtual Reality (VR) devices. The platform integrates eye gaze-based interactions to ensure participants undivided attention. To validate the platform, we conducted experiments on ASD (N=19) and Neurotypical (NT) (N=13) participants under a trained pediatric neurologist's supervision. For quantitative analysis, we employed key measures such as the number of correct responses, the duration of establishing eye contact (s), and the duration of registering a response (s), along with correlations to CARS scores and age. Results from AR-based experiments showed NT participants registered responses significantly faster (<0.00001) than ASD participants. A correlation (Spearman coefficient=0.57, p=0.03) was found between ASD participants response time and CARS scores. A similar trend was observed in VR-based experiments. When comparing response accuracy in ASD participants across platforms, AR yielded a higher correctness rate (92.30%) than VR (69.49%), indicating AR's greater effectiveness. These findings suggest that immersive technology can aid JA training in ASD. Future studies should explore long-term benefits and real-world applicability.

cs.HC↗

arXiv2022

Assistive Completion of Agrammatic Aphasic Sentences: A Transfer Learning Approach using Neurolinguistics-based Synthetic Dataset

Damage to the inferior frontal gyrus (Broca's area) can cause agrammatic aphasia wherein patients, although able to comprehend, lack the ability to form complete sentences. This inability leads to communication gaps which cause difficulties in their daily lives. The usage of assistive devices can help in mitigating these issues and enable the patients to communicate effectively. However, due to lack of large scale studies of linguistic deficits in aphasia, research on such assistive technology is relatively limited. In this work, we present two contributions that aim to re-initiate research and development in this field. Firstly, we propose a model that uses linguistic features from small scale studies on aphasia patients and generates large scale datasets of synthetic aphasic utterances from grammatically correct datasets. We show that the mean length of utterance, the noun/verb ratio, and the simple/complex sentence ratio of our synthetic datasets correspond to the reported features of aphasic speech. Further, we demonstrate how the synthetic datasets may be utilized to develop assistive devices for aphasia patients. The pre-trained T5 transformer is fine-tuned using the generated dataset to suggest 5 corrected sentences given an aphasic utterance as input. We evaluate the efficacy of the T5 model using the BLEU and cosine semantic similarity scores. Affirming results with BLEU score of 0.827/1.00 and semantic similarity of 0.904/1.00 were obtained. These results provide a strong foundation for the concept that a synthetic dataset based on small scale studies on aphasia can be used to develop effective assistive technology.

q-bio.QM↗