3 citations · 13 across the 23 of their papers we have counts for
10 papers · 1 filter
ActivityNarrated: An Open-Ended Narrative Paradigm for Wearable Human Activity Understanding
Lala Shakti Swarup Ray, Mengxi Liu, Alcina Pinto +4
Wearable human activity recognition (HAR) has made steady progress, yet much of this progress remains grounded in fixed-window, closed-set classification benchmarks. This formulati…
Human in the Latent Loop (HILL): Interactively Guiding Model Training Through Human Intuition
Daniel Geissler, Lars Krupp, Vishal Banwari +4
Latent space representations are critical for understanding and improving the behavior of machine learning models, yet they often remain obscure and intricate. Understanding and ex…
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…
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…
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…
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…
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…
CaptAinGlove: Capacitive and Inertial Fusion-Based Glove for Real-Time on Edge Hand Gesture Recognition for Drone Control
Hymalai Bello, Sungho Suh, Daniel Geißler +3
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…