3 citations · 21 across the 26 of their papers we have counts for
8 papers · 1 filter
A Case Study on Energy-Efficient Edge AI Crack Segmentation
Matthias Tschope, Mohamed Moursi, Vladimir Rybalkin +3
Crack segmentation on edge devices can support continuous infrastructure monitoring and maintenance and thereby help to preserve public safety. Furthermore, autonomous infrastructu…
Assessing the Impact of Sampling Irregularity in Time Series Data: Human Activity Recognition As A Case Study
Mengxi Liu, Daniel Geißler, Sizhen Bian +2
Human activity recognition (HAR) ideally relies on data from wearable or environment-instrumented sensors sampled at regular intervals, enabling standard neural network models opti…
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…
iMove: Exploring Bio-impedance Sensing for Fitness Activity Recognition
Mengxi Liu, Vitor Fortes Rey, Yu Zhang +3
Automatic and precise fitness activity recognition can be beneficial in aspects from promoting a healthy lifestyle to personalized preventative healthcare. While IMUs are currently…
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…
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…