most citedOn the Benefit of Generative Foundation Models for Human Activity Recognition

3 citations · 8 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CV2024

Classifying Simulated Gait Impairments using Privacy-preserving Explainable Artificial Intelligence and Mobile Phone Videos

Lauhitya Reddy, Ketan Anand, Shoibolina Kaushik +4

Accurate diagnosis of gait impairments is often hindered by subjective or costly assessment methods, with current solutions requiring either expensive multi-camera equipment or rel…

cs.AI20241 cited

Feasibility of assessing cognitive impairment via distributed camera network and privacy-preserving edge computing

Chaitra Hegde, Yashar Kiarashi, Allan I Levey +3

INTRODUCTION: Mild cognitive impairment (MCI) is characterized by a decline in cognitive functions beyond typical age and education-related expectations. Since, MCI has been linked…

cs.CV2024

Explainable Artificial Intelligence for Quantifying Interfering and High-Risk Behaviors in Autism Spectrum Disorder in a Real-World Classroom Environment Using Privacy-Preserving Video Analysis

Barun Das, Conor Anderson, Tania Villavicencio +6

Rapid identification and accurate documentation of interfering and high-risk behaviors in ASD, such as aggression, self-injury, disruption, and restricted repetitive behaviors, are…

cs.CV20241 cited

IMUGPT 2.0: Language-Based Cross Modality Transfer for Sensor-Based Human Activity Recognition

Zikang Leng, Amitrajit Bhattacharjee, Hrudhai Rajasekhar +4

One of the primary challenges in the field of human activity recognition (HAR) is the lack of large labeled datasets. This hinders the development of robust and generalizable model…

cs.CV20233 cited

On the Benefit of Generative Foundation Models for Human Activity Recognition

Zikang Leng, Hyeokhyen Kwon, Thomas Plötz

In human activity recognition (HAR), the limited availability of annotated data presents a significant challenge. Drawing inspiration from the latest advancements in generative AI,…

cs.HC20232 cited

Indoor Localization using Bluetooth and Inertial Motion Sensors in Distributed Edge and Cloud Computing Environment

Yashar Kiarashi, Chaitra Hedge, Venkata Siva Krishna Madala +5

Spatial navigation of indoor space usage patterns reveals important cues about the cognitive health of individuals. In this work, we present a low-cost, scalable, open-source edge…