35 citations · 102 across the 51 of their papers we have counts for
7 papers · 1 filter
Past, Present, and Future of Sensor-Based Human Activity Recognition Using Wearables: A Surveying Tutorial on a Still Challenging Task
Harish Haresamudram, Chi Ian Tang, Sungho Suh +2
In the many years since the inception of wearable sensor-based Human Activity Recognition (HAR), a wide variety of methods have been introduced and evaluated for their ability to r…
A Wearable Multi-Modal Edge-Computing System for Real-Time Kitchen Activity Recognition
Mengxi Liu, Sungho Suh, Juan Felipe Vargas +3
In the human activity recognition research area, prior studies predominantly concentrate on leveraging advanced algorithms on public datasets to enhance recognition performance, li…
Sensor Data Augmentation from Skeleton Pose Sequences for Improving Human Activity Recognition
Parham Zolfaghari, Vitor Fortes Rey, Lala Ray +3
The proliferation of deep learning has significantly advanced various fields, yet Human Activity Recognition (HAR) has not fully capitalized on these developments, primarily due to…
CoSS: Co-optimizing Sensor and Sampling Rate for Data-Efficient AI in Human Activity Recognition
Mengxi Liu, Zimin Zhao, Daniel Geißler +3
Recent advancements in Artificial Neural Networks have significantly improved human activity recognition using multiple time-series sensors. While employing numerous sensors with h…
Unsupervised Statistical Feature-Guided Diffusion Model for Sensor-based Human Activity Recognition
Si Zuo, Vitor Fortes Rey, Sungho Suh +2
Human activity recognition (HAR) from on-body sensors is a core functionality in many AI applications: from personal health, through sports and wellness to Industry 4.0. A key prob…
FieldHAR: A Fully Integrated End-to-end RTL Framework for Human Activity Recognition with Neural Networks from Heterogeneous Sensors
Mengxi Liu, Bo Zhou, Zimin Zhao +5
In this work, we propose an open-source scalable end-to-end RTL framework FieldHAR, for complex human activity recognition (HAR) from heterogeneous sensors using artificial neural…
Adversarial Deep Feature Extraction Network for User Independent Human Activity Recognition
Sungho Suh, Vitor Fortes Rey, Paul Lukowicz
User dependence remains one of the most difficult general problems in Human Activity Recognition (HAR), in particular when using wearable sensors. This is due to the huge variabili…