15 citations · 18 across the 5 of their papers we have counts for
5 papers
The Potential and Limitations of Vision-Language Models for Human Motion Understanding: A Case Study in Data-Driven Stroke Rehabilitation
Victor Li, Naveenraj Kamalakannan, Avinash Parnandi +2
Vision-language models (VLMs) have demonstrated remarkable performance across a wide range of computer-vision tasks, sparking interest in their potential for digital health applica…
Quantifying Impairment and Disease Severity Using AI Models Trained on Healthy Subjects
Boyang Yu, Aakash Kaku, Kangning Liu +7
Automatic assessment of impairment and disease severity is a key challenge in data-driven medicine. We propose a novel framework to address this challenge, which leverages AI model…
PrimSeq: a deep learning-based pipeline to quantitate rehabilitation training
Avinash Parnandi, Aakash Kaku, Anita Venkatesan +7
Stroke rehabilitation seeks to increase neuroplasticity through the repeated practice of functional motions, but may have minimal impact on recovery because of insufficient repetit…
Sequence-to-Sequence Modeling for Action Identification at High Temporal Resolution
Aakash Kaku, Kangning Liu, Avinash Parnandi +7
Automatic action identification from video and kinematic data is an important machine learning problem with applications ranging from robotics to smart health. Most existing works…
Pragmatic classification of movement primitives for stroke rehabilitation
Avinash Parnandi, Jasim Uddin, Dawn M. Nilsen +1
Rehabilitation training is the primary intervention to improve motor recovery after stroke, but a tool to measure functional training does not currently exist. To bridge this gap,…