35 citations · 127 across the 42 of their papers we have counts for
43 papers
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…
PIM: Physics-Informed Multi-task Pre-training for Improving Inertial Sensor-Based Human Activity Recognition
Dominique Nshimyimana, Vitor Fortes Rey, Sungho Suh +2
Human activity recognition (HAR) with deep learning models relies on large amounts of labeled data, often challenging to obtain due to associated cost, time, and labor. Self-superv…
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…
Initial Findings on Sensor based Open Vocabulary Activity Recognition via Text Embedding Inversion
Lala Shakti Swarup Ray, Bo Zhou, Sungho Suh +1
Conventional human activity recognition (HAR) relies on classifiers trained to predict discrete activity classes, inherently limiting recognition to activities explicitly present i…
OV-HHIR: Open Vocabulary Human Interaction Recognition Using Cross-modal Integration of Large Language Models
Lala Shakti Swarup Ray, Bo Zhou, Sungho Suh +1
Understanding human-to-human interactions, especially in contexts like public security surveillance, is critical for monitoring and maintaining safety. Traditional activity recogni…
Beyond Confusion: A Fine-grained Dialectical Examination of Human Activity Recognition Benchmark Datasets
Daniel Geissler, Dominique Nshimyimana, Vitor Fortes Rey +3
The research of machine learning (ML) algorithms for human activity recognition (HAR) has made significant progress with publicly available datasets. However, most research priorit…
Spend More to Save More (SM2): An Energy-Aware Implementation of Successive Halving for Sustainable Hyperparameter Optimization
Daniel Geissler, Bo Zhou, Sungho Suh +1
A fundamental step in the development of machine learning models commonly involves the tuning of hyperparameters, often leading to multiple model training runs to work out the best…
Enhancing Interpretability Through Loss-Defined Classification Objective in Structured Latent Spaces
Daniel Geissler, Bo Zhou, Mengxi Liu +1
Supervised machine learning often operates on the data-driven paradigm, wherein internal model parameters are autonomously optimized to converge predicted outputs with the ground t…
Strategies and Challenges of Efficient White-Box Training for Human Activity Recognition
Daniel Geissler, Bo Zhou, Paul Lukowicz
Human Activity Recognition using time-series data from wearable sensors poses unique challenges due to complex temporal dependencies, sensor noise, placement variability, and diver…
Towards certifiable AI in aviation: landscape, challenges, and opportunities
Hymalai Bello, Daniel Geißler, Lala Ray +6
Artificial Intelligence (AI) methods are powerful tools for various domains, including critical fields such as avionics, where certification is required to achieve and maintain an…
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…
TSAK: Two-Stage Semantic-Aware Knowledge Distillation for Efficient Wearable Modality and Model Optimization in Manufacturing Lines
Hymalai Bello, Daniel Geißler, Sungho Suh +2
Smaller machine learning models, with less complex architectures and sensor inputs, can benefit wearable sensor-based human activity recognition (HAR) systems in many ways, from co…
ALS-HAR: Harnessing Wearable Ambient Light Sensors to Enhance IMU-based Human Activity Recogntion
Lala Shakti Swarup Ray, Daniel Geißler, Mengxi Liu +3
Despite the widespread integration of ambient light sensors (ALS) in smart devices commonly used for screen brightness adaptation, their application in human activity recognition (…
Misinforming LLMs: vulnerabilities, challenges and opportunities
Bo Zhou, Daniel Geißler, Paul Lukowicz
Large Language Models (LLMs) have made significant advances in natural language processing, but their underlying mechanisms are often misunderstood. Despite exhibiting coherent ans…
Initial Investigation of Kolmogorov-Arnold Networks (KANs) as Feature Extractors for IMU Based Human Activity Recognition
Mengxi Liu, Daniel Geißler, Dominique Nshimyimana +3
In this work, we explore the use of a novel neural network architecture, the Kolmogorov-Arnold Networks (KANs) as feature extractors for sensor-based (specifically IMU) Human Activ…
iKAN: Global Incremental Learning with KAN for Human Activity Recognition Across Heterogeneous Datasets
Mengxi Liu, Sizhen Bian, Bo Zhou +1
This work proposes an incremental learning (IL) framework for wearable sensor human activity recognition (HAR) that tackles two challenges simultaneously: catastrophic forgetting a…
BeSound: Bluetooth-Based Position Estimation Enhancing with Cross-Modality Distillation
Hymalai Bello, Sungho Suh, Bo Zhou +1
Smart factories leverage advanced technologies to optimize manufacturing processes and enhance efficiency. Implementing worker tracking systems, primarily through camera-based meth…
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…
Text me the data: Generating Ground Pressure Sequence from Textual Descriptions for HAR
Lala Shakti Swarup Ray, Bo Zhou, Sungho Suh +3
In human activity recognition (HAR), the availability of substantial ground truth is necessary for training efficient models. However, acquiring ground pressure data through physic…
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…
The Power of Training: How Different Neural Network Setups Influence the Energy Demand
Daniel Geißler, Bo Zhou, Mengxi Liu +2
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…
Remaining useful life prediction of Lithium-ion batteries using spatio-temporal multimodal attention networks
Sungho Suh, Dhruv Aditya Mittal, Hymalai Bello +3
Lithium-ion batteries are widely used in various applications, including electric vehicles and renewable energy storage. The prediction of the remaining useful life (RUL) of batter…