no code implementations • 24 Apr 2024 • Hymalai Bello, Sungho Suh, Bo Zhou, Paul Lukowicz
Once the student model is trained, the model only takes as inputs the BLE-RSSI data for inference, retaining the advantages of ubiquity and low cost of BLE RSSI.
no code implementations • 22 Feb 2024 • Lala Shakti Swarup Ray, Bo Zhou, Sungho Suh, Lars Krupp, Vitor Fortes Rey, Paul Lukowicz
We show that the combination of vector quantization of sensor data along with simple text conditioned auto regressive strategy allows us to obtain high-quality generated pressure sequences from textual descriptions with the help of discrete latent correlation between text and pressure maps.
1 code implementation • 18 Jan 2024 • Hyungmin Kim, Donghun Kim, Pyunghwan Ahn, Sungho Suh, Hansang Cho, Junmo Kim
With the minimal additional computation cost of image resizing, ContextMix enhances performance compared to existing augmentation techniques.
no code implementations • 3 Jan 2024 • Mengxi Liu, Zimin Zhao, Daniel Geißler, Bo Zhou, Sungho Suh, Paul Lukowicz
Recent advancements in Artificial Neural Networks have significantly improved human activity recognition using multiple time-series sensors.
no code implementations • 3 Jan 2024 • Daniel Geißler, Bo Zhou, Mengxi Liu, Sungho Suh, Paul Lukowicz
This work offers a heuristic evaluation of the effects of variations in machine learning training regimes and learning paradigms on the energy consumption of computing, especially HPC hardware with a life-cycle aware perspective.
no code implementations • 29 Oct 2023 • Sungho Suh, Dhruv Aditya Mittal, Hymalai Bello, Bo Zhou, Mayank Shekhar Jha, Paul Lukowicz
The proposed ST-MAN is to capture the complex spatio-temporal dependencies in the battery data, including the features that are often neglected in existing works.
no code implementations • 14 Sep 2023 • Davinder Pal Singh, Lala Shakti Swarup Ray, Bo Zhou, Sungho Suh, Paul Lukowicz
We present a novel local-global feature fusion framework for body-weight exercise recognition with floor-based dynamic pressure maps.
no code implementations • 16 Aug 2023 • Tae Yeob Kang, Haebom Lee, Sungho Suh
In the field of optoelectronics, indium tin oxide (ITO) electrodes play a crucial role in various applications, such as displays, sensors, and solar cells.
no code implementations • 7 Aug 2023 • Sungho Suh, Vitor Fortes Rey, Sizhen Bian, Yu-Chi Huang, Jože M. Rožanec, Hooman Tavakoli Ghinani, Bo Zhou, Paul Lukowicz
This paper presents a novel wearable sensing prototype that combines IMU and body capacitance sensing modules to recognize worker activities in the manufacturing line.
no code implementations • 7 Aug 2023 • Dhruv Mittal, Hymalai Bello, Bo Zhou, Mayank Shekhar Jha, Sungho Suh, Paul Lukowicz
Early prediction of remaining useful life (RUL) is crucial for effective battery management across various industries, ranging from household appliances to large-scale applications.
no code implementations • 1 Aug 2023 • Lala Shakti Swarup Ray, Vitor Fortes Rey, Bo Zhou, Sungho Suh, Paul Lukowicz
We propose PressureTransferNet, a novel method for Human Activity Recognition (HAR) using ground pressure information.
1 code implementation • 21 Jul 2023 • Lala Shakti Swarup Ray, Bo Zhou, Sungho Suh, Paul Lukowicz
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 (DMCB), specifically designed to evaluate the performance of optical marker-based and marker-less MoCap.
1 code implementation • ICCV 2023 • Hyungmin Kim, Sungho Suh, Daehwan Kim, Daun Jeong, Hansang Cho, Junmo Kim
Existing methods for novel category discovery are limited by their reliance on labeled datasets and prior knowledge about the number of novel categories and the proportion of novel samples in the batch.
no code implementations • 3 Jul 2023 • Lala Shakti Swarup Ray, Bo Zhou, Lars Krupp, Sungho Suh, Paul Lukowicz
Accurate camera calibration is crucial for various computer vision applications.
no code implementations • 24 Jun 2023 • Yunmin Cho, Lala Shakti Swarup Ray, Kundan Sai Prabhu Thota, Sungho Suh, Paul Lukowicz
The proposed method utilizes a U-Net-based network architecture that incorporates cloth and human attributes to guide the realistic virtual try-on synthesis.
no code implementations • 19 Jun 2023 • Hymalai Bello, Sungho Suh, Bo Zhou, Paul Lukowicz
The increasing prevalence of stress-related eating behaviors and their impact on overall health highlights the importance of effective and ubiquitous monitoring systems.
no code implementations • 7 Jun 2023 • Hymalai Bello, Sungho Suh, Daniel Geißler, Lala Ray, Bo Zhou, Paul Lukowicz
We present CaptAinGlove, a textile-based, low-power (1. 15Watts), privacy-conscious, real-time on-the-edge (RTE) glove-based solution with a tiny memory footprint (2MB), designed to recognize hand gestures used for drone control.
no code implementations • 2 Jun 2023 • Chonghyo Joo, Jeongdong Kim, Hyungtae Cho, Jaewon Lee, Sungho Suh, Junghwan Kim
In this paper, we propose a neural network framework that utilizes chemical property information to improve the performance of naphtha composition prediction.
no code implementations • 30 May 2023 • Si Zuo, Vitor Fortes Rey, Sungho Suh, Stephan Sigg, Paul Lukowicz
The proposed method aims to generate synthetic time-series sensor data without relying on labeled data, addressing the scarcity and annotation difficulties associated with real-world sensor data.
Ranked #1 on Human Activity Recognition on MM-Fit
Generative Adversarial Network Human Activity Recognition +1
no code implementations • 22 May 2023 • Mengxi Liu, Bo Zhou, Zimin Zhao, Hyeonseok Hong, Hyun Kim, Sungho Suh, Vitor Fortes Rey, Paul Lukowicz
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 networks (ANN) optimized for FPGA or ASIC integration.
no code implementations • 20 Apr 2023 • Tae Yeob Kang, Haebom Lee, Sungho Suh
Fault detection and diagnosis of the interconnects are crucial for prognostics and health management (PHM) of electronics.
no code implementations • 24 Feb 2023 • Jwalin Bhatt, Yaroslav Zharov, Sungho Suh, Tilo Baumbach, Vincent Heuveline, Paul Lukowicz
Morphological atlases are an important tool in organismal studies, and modern high-throughput Computed Tomography (CT) facilities can produce hundreds of full-body high-resolution volumetric images of organisms.
no code implementations • 8 Feb 2023 • Hymalai Bello, Luis Alfredo Sanchez Marin, Sungho Suh, Bo Zhou, Paul Lukowicz
The sensors were placed unobtrusively in a sports cap to monitor facial muscle activities related to facial expressions.
no code implementations • 1 Feb 2023 • Lala Shakti Swarup Ray, Bo Zhou, Sungho Suh, Paul Lukowicz
Ground pressure exerted by the human body is a valuable source of information for human activity recognition (HAR) in unobtrusive pervasive sensing.
no code implementations • 20 Nov 2022 • Hyungmin Kim, Sungho Suh, SungHyun Baek, Daehwan Kim, Daun Jeong, Hansang Cho, Junmo Kim
Our model not only distills the deterministic and progressive knowledge which are from the pre-trained and previous epoch predictive probabilities but also transfers the knowledge of the deterministic predictive distributions using adversarial learning.
no code implementations • 4 Oct 2022 • Vitor Fortes Rey, Sungho Suh, Paul Lukowicz
To mitigate this problem we propose a method that facilitates the use of information from sensors that are only present during the training process and are unavailable during the later use of the system.
no code implementations • 3 Oct 2022 • Mengxi Liu, Sungho Suh, Bo Zhou, Agnes Gruenerbl, Paul Lukowicz
Meanwhile, we evaluate the impact of the infrared array sensor on the recognition accuracy of these activities.
no code implementations • 14 Sep 2022 • Sungho Suh, Vitor Fortes Rey, Paul Lukowicz
In this work, we propose a novel Transformer-based Adversarial learning framework for human activity recognition using wearable sensors via Self-KnowledgE Distillation (TASKED), that accounts for individual sensor orientations and spatial and temporal features.
no code implementations • 28 May 2022 • Kundan Sai Prabhu Thota, Sungho Suh, Bo Zhou, Paul Lukowicz
The estimation of 3D human body shape and clothing measurements is crucial for virtual try-on and size recommendation problems in the fashion industry but has always been a challenging problem due to several conditions, such as lack of publicly available realistic datasets, ambiguity in multiple camera resolutions, and the undefinable human shape space.
no code implementations • 21 Mar 2022 • Jože M. Rožanec, Inna Novalija, Patrik Zajec, Klemen Kenda, Hooman Tavakoli, Sungho Suh, Entso Veliou, Dimitrios Papamartzivanos, Thanassis Giannetsos, Sofia Anna Menesidou, Ruben Alonso, Nino Cauli, Antonello Meloni, Diego Reforgiato Recupero, Dimosthenis Kyriazis, Georgios Sofianidis, Spyros Theodoropoulos, Blaž Fortuna, Dunja Mladenić, John Soldatos
Human-centricity is the core value behind the evolution of manufacturing towards Industry 5. 0.
no code implementations • 23 Oct 2021 • Sungho Suh, Vitor Fortes Rey, Paul Lukowicz
The proposed network is based on the adversarial encoder-decoder structure with the MMD realign the data distribution over multiple subjects.
1 code implementation • 26 Sep 2021 • Sungho Suh, Paul Lukowicz, Yong Oh Lee
The experimental results show that the proposed feature extraction method can effectively predict the RUL and outperforms the conventional RUL prediction approaches based on deep neural networks.
no code implementations • 1 Jul 2021 • Sungho Suh, Sojeong Cheon, Wonseo Choi, Yeon Woong Chung, Won-Kyung Cho, Ji-Sun Paik, Sung Eun Kim, Dong-Jin Chang, Yong Oh Lee
Deep neural networks (DNNs) have been widely used for medical image analysis.
no code implementations • 20 Nov 2020 • Joel Jang, Yoonjeon Kim, Kyoungho Choi, Sungho Suh
Classification tasks require a balanced distribution of data to ensure the learner to be trained to generalize over all classes.
1 code implementation • 24 Oct 2020 • Sungho Suh, Paul Lukowicz, Yong Oh Lee
In this paper, we propose a novel supervised discriminative feature generation (DFG) method for a minority class dataset.
1 code implementation • 20 Oct 2020 • Sungho Suh, Jihun Kim, Paul Lukowicz, Yong Oh Lee
Document image enhancement and binarization methods are often used to improve the accuracy and efficiency of document image analysis tasks such as text recognition.
Ranked #1 on Binarization on LRDE DBD
no code implementations • 26 Aug 2020 • Sungho Suh, Paul Lukowicz, Yong Oh Lee
These results are expected to improve the shipping address recognition and verification system by applying different image preprocessing steps based on the classified conditions.