most citedWorker Activity Recognition in Manufacturing Line Using Near-body Electric Field

3 citations · 5 across the 6 of their papers we have counts for

collaborators

6 papers

cs.HC20241 cited

iFace: Hand-Over-Face Gesture Recognition Leveraging Impedance Sensing

Mengxi Liu, Hymalai Bello, Bo Zhou +2

Hand-over-face gestures can provide important implicit interactions during conversations, such as frustration or excitement. However, in situations where interlocutors are not visi…

eess.SP20241 cited

Body-Area Capacitive or Electric Field Sensing for Human Activity Recognition and Human-Computer Interaction: A Comprehensive Survey

Sizhen Bian, Mengxi Liu, Bo Zhou +2

Due to the fact that roughly sixty percent of the human body is essentially composed of water, the human body is inherently a conductive object, being able to, firstly, form an inh…

cs.CV2023

A Novel Local-Global Feature Fusion Framework for Body-weight Exercise Recognition with Pressure Mapping Sensors

Davinder Pal Singh, Lala Shakti Swarup Ray, Bo Zhou +2

We present a novel local-global feature fusion framework for body-weight exercise recognition with floor-based dynamic pressure maps. One step further from the existing studies usi…

cs.LG20233 cited

Worker Activity Recognition in Manufacturing Line Using Near-body Electric Field

Sungho Suh, Vitor Fortes Rey, Sizhen Bian +5

Manufacturing industries strive to improve production efficiency and product quality by deploying advanced sensing and control systems. Wearable sensors are emerging as a promising…

cs.CV2023

PressureTransferNet: Human Attribute Guided Dynamic Ground Pressure Profile Transfer using 3D simulated Pressure Maps

Lala Shakti Swarup Ray, Vitor Fortes Rey, Bo Zhou +2

We propose PressureTransferNet, a novel method for Human Activity Recognition (HAR) using ground pressure information. Our approach generates body-specific dynamic ground pressure…

cs.CV2023

Selecting the motion ground truth for loose-fitting wearables: benchmarking optical MoCap methods

Lala Shakti Swarup Ray, Bo Zhou, Sungho Suh +1

To help smart wearable researchers choose the optimal ground truth methods for motion capturing (MoCap) for all types of loose garments, we present a benchmark, DrapeMoCapBench (DM…