3 citations · 13 across the 23 of their papers we have counts for
12 papers · 1 filter
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…
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…
Leveraging Hybrid Intelligence Towards Sustainable and Energy-Efficient Machine Learning
Daniel Geissler, Paul Lukowicz
Hybrid intelligence aims to enhance decision-making, problem-solving, and overall system performance by combining the strengths of both, human cognitive abilities and artificial in…
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…
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…