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.
no code implementations • 31 Jan 2024 • Mengxi Liu, Vitor Fortes Rey, Yu Zhang, Lala Shakti Swarup Ray, Bo Zhou, Paul Lukowicz
While IMUs are currently the prominent fitness tracking modality, through iMove, we show bio-impedence can help improve IMU-based fitness tracking through sensor fusion and contrastive learning. To evaluate our methods, we conducted an experiment including six upper body fitness activities performed by ten subjects over five days to collect synchronized data from bio-impedance across two wrists and IMU on the left wrist. The contrastive learning framework uses the two modalities to train a better IMU-only classification model, where bio-impedance is only required at the training phase, by which the average Macro F1 score with the input of a single IMU was improved by 3. 22 \% reaching 84. 71 \% compared to the 81. 49 \% of the IMU baseline model.
no code implementations • 11 Jan 2024 • Sizhen Bian, Mengxi Liu, Bo Zhou, Paul Lukowicz, Michele Magno
To this end, we first sorted the explorations into three domains according to the involved body forms: body-part electric field, whole-body electric field, and body-to-body electric field, and enumerated the state-of-art works in the domains with a detailed survey of the backed sensing tricks and targeted applications.
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 • 21 Aug 2023 • Lars Krupp, Steffen Steinert, Maximilian Kiefer-Emmanouilidis, Karina E. Avila, Paul Lukowicz, Jochen Kuhn, Stefan Küchemann, Jakob Karolus
In a study, students with a background in physics were assigned to solve physics exercises, with one group having access to an internet search engine (N=12) and the other group being allowed to use ChatGPT (N=27).
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.
no code implementations • 3 Jul 2023 • Vitor Fortes Rey, Dominique Nshimyimana, Paul Lukowicz
Recently self-supervised learning has been proposed in the field of human activity recognition as a solution to the labelled data availability problem.
no code implementations • 3 Jul 2023 • Lala Shakti Swarup Ray, Daniel Geißler, Bo Zhou, Paul Lukowicz, Berit Greinke
It was observed through embedding areas of origami structures with conductive materials as single-end capacitive sensing patches, that the sensor signals change coherently with the motion of the structure.
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 • 23 Jun 2023 • Dino Pedreschi, Luca Pappalardo, Emanuele Ferragina, Ricardo Baeza-Yates, Albert-Laszlo Barabasi, Frank Dignum, Virginia Dignum, Tina Eliassi-Rad, Fosca Giannotti, Janos Kertesz, Alistair Knott, Yannis Ioannidis, Paul Lukowicz, Andrea Passarella, Alex Sandy Pentland, John Shawe-Taylor, Alessandro Vespignani
Human-AI coevolution, defined as a process in which humans and AI algorithms continuously influence each other, increasingly characterises our society, but is understudied in artificial intelligence and complexity science literature.
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 • 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 • 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 • 10 Nov 2022 • Mengxi Liu, Sizhen Bian, Paul Lukowicz
This work described a novel non-contact, wearable, real-time eye blink detection solution based on capacitive sensing technology.
1 code implementation • 26 Oct 2022 • Sizhen Bian, Vitor Fortes Rey, Siyu Yuan, Paul Lukowicz
In the second case, we tried to recognize actions related to manipulating objects and physical collaboration between users by using a wrist-worn HBC sensing unit.
no code implementations • 8 Oct 2022 • Mengxi Liu, Sizhen Bian, Bo Zhou, Agnes Grünerbl, Paul Lukowicz
We studied the frequency sensitivity of the electrochemical impedance spectrum regarding distinct beverages and the importance of features like amplitude, phase, and real and imaginary components for beverage classification.
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 • 18 Jul 2022 • Sizhen Bian, Kexuan Guo, Mengxi Liu, Bo Zhou, Paul Lukowicz
In more detail, the transmitters generate the oscillating magnetic fields with a registered sequence, the receiver senses the strength of the induced magnetic field by a customized three axes coil, which is configured as the LC oscillator with the same oscillating frequency so that an induced current shows up when the receiver is located in the field of the generated magnetic field.
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 • 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.
1 code implementation • 29 Nov 2020 • Anton Smerdov, Andrey Somov, Evgeny Burnaev, Bo Zhou, Paul Lukowicz
In this article, we propose the methods based on the sensor data analysis for predicting whether a player will win the future encounter.
BIG-bench Machine Learning Interpretable Machine Learning +8
no code implementations • 23 Nov 2020 • Vitor Fortes Rey, Kamalveer Kaur Garewal, Paul Lukowicz
Furthermore we show that by either including a small amount of real sensor data for model calibration or simply leveraging the fact that (in general) we can easily generate much more simulated data from video than we can collect in terms of real sensor data the advantage of real sensor data can be eventually equalized.
3 code implementations • 2 Nov 2020 • Anton Smerdov, Bo Zhou, Paul Lukowicz, Andrey Somov
An important feature of the dataset is simultaneous data collection from five players, which facilitates the analysis of sensor data on a team level.
Ranked #1 on Skills Evaluation on eSports Sensors Dataset
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.
no code implementations • LREC 2020 • Georg Rehm, Katrin Marheinecke, Stefanie Hegele, Stelios Piperidis, Kalina Bontcheva, Jan Hajič, Khalid Choukri, Andrejs Vasiļjevs, Gerhard Backfried, Christoph Prinz, José Manuel Gómez Pérez, Luc Meertens, Paul Lukowicz, Josef van Genabith, Andrea Lösch, Philipp Slusallek, Morten Irgens, Patrick Gatellier, Joachim köhler, Laure Le Bars, Dimitra Anastasiou, Albina Auksoriūtė, Núria Bel, António Branco, Gerhard Budin, Walter Daelemans, Koenraad De Smedt, Radovan Garabík, Maria Gavriilidou, Dagmar Gromann, Svetla Koeva, Simon Krek, Cvetana Krstev, Krister Lindén, Bernardo Magnini, Jan Odijk, Maciej Ogrodniczuk, Eiríkur Rögnvaldsson, Mike Rosner, Bolette Sandford Pedersen, Inguna Skadiņa, Marko Tadić, Dan Tufiş, Tamás Váradi, Kadri Vider, Andy Way, François Yvon
Multilingualism is a cultural cornerstone of Europe and firmly anchored in the European treaties including full language equality.
no code implementations • 16 May 2019 • Tom Hanika, Marek Herde, Jochen Kuhn, Jan Marco Leimeister, Paul Lukowicz, Sarah Oeste-Reiß, Albrecht Schmidt, Bernhard Sick, Gerd Stumme, Sven Tomforde, Katharina Anna Zweig
The field of collaborative interactive learning (CIL) aims at developing and investigating the technological foundations for a new generation of smart systems that support humans in their everyday life.
no code implementations • 30 Jan 2017 • David Bannach, Martin Jänicke, Vitor F. Rey, Sven Tomforde, Bernhard Sick, Paul Lukowicz
Traditional activity recognition systems work on the basis of training, taking a fixed set of sensors into account.
no code implementations • 4 Jan 2017 • Monit Shah Singh, Vinaychandran Pondenkandath, Bo Zhou, Paul Lukowicz, Marcus Liwicki
Convolutional Neural Networks (CNNs) have become the state-of-the-art in various computer vision tasks, but they are still premature for most sensor data, especially in pervasive and wearable computing.
no code implementations • 1 Apr 2015 • Adrian Calma, Tobias Reitmaier, Bernhard Sick, Paul Lukowicz, Mark Embrechts
Active learning (AL) is a learning paradigm where an active learner has to train a model (e. g., a classifier) which is in principal trained in a supervised way, but in AL it has to be done by means of a data set with initially unlabeled samples.