3 citations · 10 across the 9 of their papers we have counts for
6 papers · 1 filter
Scaling Human Activity Recognition: A Comparative Evaluation of Synthetic Data Generation and Augmentation Techniques
Zikang Leng, Archith Iyer, Thomas Plötz
Human activity recognition (HAR) is often limited by the scarcity of labeled datasets due to the high cost and complexity of real-world data collection. To mitigate this, recent wo…
AgentSense: Virtual Sensor Data Generation Using LLM Agents in Simulated Home Environments
Zikang Leng, Megha Thukral, Yaqi Liu +4
A major challenge in developing robust and generalizable Human Activity Recognition (HAR) systems for smart homes is the lack of large and diverse labeled datasets. Variations in h…
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…
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,…
Generating Virtual On-body Accelerometer Data from Virtual Textual Descriptions for Human Activity Recognition
Zikang Leng, Hyeokhyen Kwon, Thomas Plötz
The development of robust, generalized models in human activity recognition (HAR) has been hindered by the scarcity of large-scale, labeled data sets. Recent work has shown that vi…
Fine-grained Human Activity Recognition Using Virtual On-body Acceleration Data
Zikang Leng, Yash Jain, Hyeokhyen Kwon +1
Previous work has demonstrated that virtual accelerometry data, extracted from videos using cross-modality transfer approaches like IMUTube, is beneficial for training complex and…