arXiv subjects

Paul Lukowicz

Publications and source records attributed to Paul Lukowicz.

At least 37 records · Page 2Linked to original sources

arXiv2025

Promoting Sustainable Web Agents: Benchmarking and Estimating Energy Consumption through Empirical and Theoretical Analysis

Web agents, like OpenAI's Operator and Google's Project Mariner, are powerful agentic systems pushing the boundaries of Large Language Models (LLM). They can autonomously interact with the internet at the user's behest, such as navigating websites, filling search masks, and comparing price lists. Though web agent research is thriving, induced sustainability issues remain largely unexplored. To highlight the urgency of this issue, we provide an initial exploration of the energy and $CO_2$ cost associated with web agents from both a theoretical -via estimation- and an empirical perspective -by benchmarking. Our results show how different philosophies in web agent creation can severely impact the associated expended energy, and that more energy consumed does not necessarily equate to better results. We highlight a lack of transparency regarding disclosing model parameters and processes used for some web agents as a limiting factor when estimating energy consumption. Our work contributes towards a change in thinking of how we evaluate web agents, advocating for dedicated metrics measuring energy consumption in benchmarks.

cs.AI↗

arXiv2025

Bridging Generalization and Personalization in Human Activity Recognition via On-Device Few-Shot Learning

Human Activity Recognition (HAR) with different sensing modalities requires both strong generalization across diverse users and efficient personalization for individuals. However, conventional HAR models often fail to generalize when faced with user-specific variations, leading to degraded performance. To address this challenge, we propose a novel on-device few-shot learning framework that bridges generalization and personalization in HAR. Our method first trains a generalizable representation across users and then rapidly adapts to new users with only a few labeled samples, updating lightweight classifier layers directly on resource-constrained devices. This approach achieves robust on-device learning with minimal computation and memory cost, making it practical for real-world deployment. We implement our framework on the energy-efficient RISC-V GAP9 microcontroller and evaluate it on three benchmark datasets (RecGym, QVAR-Gesture, Ultrasound-Gesture). Across these scenarios, post-deployment adaptation improves accuracy by 3.73\%, 17.38\%, and 3.70\%, respectively. These results demonstrate that few-shot on-device learning enables scalable, user-aware, and energy-efficient wearable human activity recognition by seamlessly uniting generalization and personalization. The related framework is open sourced for further research\footnote{https://github.com/kangpx/onlineTiny2023}.

cs.LG↗

arXiv2025

ChairPose: Pressure-based Chair Morphology Grounded Sitting Pose Estimation through Simulation-Assisted Training

Prolonged seated activity is increasingly common in modern environments, raising concerns around musculoskeletal health, ergonomics, and the design of responsive interactive systems. Existing posture sensing methods such as vision-based or wearable approaches face limitations including occlusion, privacy concerns, user discomfort, and restricted deployment flexibility. We introduce ChairPose, the first full body, wearable free seated pose estimation system that relies solely on pressure sensing and operates independently of chair geometry. ChairPose employs a two stage generative model trained on pressure maps captured from a thin, chair agnostic sensing mattress. Unlike prior approaches, our method explicitly incorporates chair morphology into the inference process, enabling accurate, occlusion free, and privacy preserving pose estimation. To support generalization across diverse users and chairs, we introduce a physics driven data augmentation pipeline that simulates realistic variations in posture and seating conditions. Evaluated across eight users and four distinct chairs, ChairPose achieves a mean per joint position error of 89.4 mm when both the user and the chair are unseen, demonstrating robust generalization to novel real world generalizability. ChairPose expands the design space for posture aware interactive systems, with potential applications in ergonomics, healthcare, and adaptive user interfaces.

cs.HC↗

arXiv2025

No Scratch Quantum Computing by Reducing Qubit Overhead for Efficient Arithmetics

Quantum arithmetic computation requires a substantial number of scratch qubits to stay reversible. These operations necessitate qubit and gate resources equivalent to those needed for the larger of the input or output registers due to state encoding. Quantum Hamiltonian Computing (QHC) introduces a novel approach by encoding input for logic operations within a single rotating quantum gate. This innovation reduces the required qubit register $ N $ to the size of the output states $ O $, where $ N = \log_2 O $. Leveraging QHC principles, we present reversible half-adder and full-adder circuits that compress the standard Toffoli + CNOT layout [Vedral et al., PRA, 54, 11, (1996)] from three-qubit and four-qubit formats for the Quantum half-adder circuit and five sequential Fredkin gates using five qubits [Moutinho et al., PRX Energy 2, 033002 (2023)] for full-adder circuit; into a two-qubit, 4$\times $4 Hilbert space. This scheme, presented here, is optimized for classical logic evaluated on quantum hardware, which due to unitary evolution can bypass classical CMOS energy limitations to certain degree. Although we avoid superposition of input and output states in this manuscript, this remains feasible in principle. We see the best application for QHC in finding the minimal qubit and gate resources needed to evaluate any truth table, advancing FPGA capabilities using integrated quantum circuits or photonics.

quant-ph↗

arXiv2025

Passive Body-Area Electrostatic Field (Human Body Capacitance) for Ubiquitous Computing

Passive body-area electrostatic field sensing, also referred to as human body capacitance (HBC), is an energy-efficient and non-intrusive sensing modality that exploits the human body's inherent electrostatic properties to perceive human behaviors. This paper presents a focused overview of passive HBC sensing, including its underlying principles, historical evolution, hardware architectures, and applications across research domains. Key challenges, such as susceptibility to environmental variation, are discussed to trigger mitigation techniques. Future research opportunities in sensor fusion and hardware enhancement are highlighted. To support continued innovation, this work provides open-source resources and aims to empower researchers and developers to leverage passive electrostatic sensing for next-generation wearable and ambient intelligence systems.

eess.SP↗

arXiv2025

TinierHAR: Towards Ultra-Lightweight Deep Learning Models for Efficient Human Activity Recognition on Edge Devices

Human Activity Recognition (HAR) on resource-constrained wearable devices demands inference models that harmonize accuracy with computational efficiency. This paper introduces TinierHAR, an ultra-lightweight deep learning architecture that synergizes residual depthwise separable convolutions, gated recurrent units (GRUs), and temporal aggregation to achieve SOTA efficiency without compromising performance. Evaluated across 14 public HAR datasets, TinierHAR reduces Parameters by 2.7x (vs. TinyHAR) and 43.3x (vs. DeepConvLSTM), and MACs by 6.4x and 58.6x, respectively, while maintaining the averaged F1-scores. Beyond quantitative gains, this work provides the first systematic ablation study dissecting the contributions of spatial-temporal components across proposed TinierHAR, prior SOTA TinyHAR, and the classical DeepConvLSTM, offering actionable insights for designing efficient HAR systems. We finally discussed the findings and suggested principled design guidelines for future efficient HAR. To catalyze edge-HAR research, we open-source all materials in this work for future benchmarking\footnote{https://github.com/zhaxidele/TinierHAR}

cs.CV↗

arXiv2025

SImpHAR: Advancing impedance-based human activity recognition using 3D simulation and text-to-motion models

Human Activity Recognition (HAR) with wearable sensors is essential for applications in healthcare, fitness, and human-computer interaction. Bio-impedance sensing offers unique advantages for fine-grained motion capture but remains underutilized due to the scarcity of labeled data. We introduce SImpHAR, a novel framework addressing this limitation through two core contributions. First, we propose a simulation pipeline that generates realistic bio-impedance signals from 3D human meshes using shortest-path estimation, soft-body physics, and text-to-motion generation serving as a digital twin for data augmentation. Second, we design a two-stage training strategy with decoupled approach that enables broader activity coverage without requiring label-aligned synthetic data. We evaluate SImpHAR on our collected ImpAct dataset and two public benchmarks, showing consistent improvements over state-of-the-art methods, with gains of up to 22.3% and 21.8%, in terms of accuracy and macro F1 score, respectively. Our results highlight the promise of simulation-driven augmentation and modular training for impedance-based HAR.

cs.CV↗

arXiv2025

W2W: A Simulated Exploration of IMU Placement Across the Human Body for Designing Smarter Wearable

Inertial measurement units (IMUs) are central to wearable systems for activity recognition and pose estimation, but sensor placement remains largely guided by heuristics and convention. In this work, we introduce Where to Wear (W2W), a simulation-based framework for systematic exploration of IMU placement utility across the body. Using labeled motion capture data, W2W generates realistic synthetic IMU signals at 512 anatomically distributed surface patches, enabling high-resolution, task-specific evaluation of sensor performance. We validate reliability of W2W by comparing spatial performance rankings from synthetic data with real IMU recordings in two multimodal datasets, confirming strong agreement in activity-wise trends. Further analysis reveals consistent spatial trends across activity types and uncovers overlooked high-utility regions that are rarely used in commercial systems. These findings challenge long-standing placement norms and highlight opportunities for more efficient, task-adaptive sensor configurations. Overall, our results demonstrate that simulation with W2W can serve as a powerful design tool for optimizing sensor placement, enabling scalable, data-driven strategies that are impractical to obtain through physical experimentation alone.

cs.HC↗

arXiv2025

iBreath: Usage Of Breathing Gestures as Means of Interactions

Breathing is a spontaneous but controllable body function that can be used for hands-free interaction. Our work introduces "iBreath", a novel system to detect breathing gestures similar to clicks using bio-impedance. We evaluated iBreath's accuracy and user experience using two lab studies (n=34). Our results show high detection accuracy (F1-scores > 95.2%). Furthermore, the users found the gestures easy to use and comfortable. Thus, we developed eight practical guidelines for the future development of breathing gestures. For example, designers can train users on new gestures within just 50 seconds (five trials), and achieve robust performance with both user-dependent and user-independent models trained on data from 21 participants, each yielding accuracies above 90%. Users preferred single clicks and disliked triple clicks. The median gesture duration is 3.5-5.3 seconds. Our work provides solid ground for researchers to experiment with creating breathing gestures and interactions.

cs.HC↗

arXiv2025

Boosting Classification with Quantum-Inspired Augmentations

Understanding the impact of small quantum gate perturbations, which are common in quantum digital devices but absent in classical computers, is crucial for identifying potential advantages in quantum machine learning. While these perturbations are typically seen as detrimental to quantum computation, they can actually enhance performance by serving as a natural source of data augmentation. Additionally, they can often be efficiently simulated on classical hardware, enabling quantum-inspired approaches to improve classical machine learning methods. In this paper, we investigate random Bloch sphere rotations, which are fundamental SU(2) transformations, as a simple yet effective quantum-inspired data augmentation technique. Unlike conventional augmentations such as flipping, rotating, or cropping, quantum transformations lack intuitive spatial interpretations, making their application to tasks like image classification less straightforward. While common quantum augmentation methods rely on applying quantum models or trainable quanvolutional layers to classical datasets, we focus on the direct application of small-angle Bloch rotations and their effect on classical data. Using the large-scale ImageNet dataset, we demonstrate that our quantum-inspired augmentation method improves image classification performance, increasing Top-1 accuracy by 3%, Top-5 accuracy by 2.5%, and the F$_1$ score from 8% to 12% compared to standard classical augmentation methods. Finally, we examine the use of stronger unitary augmentations. Although these transformations preserve information in principle, they result in visually unrecognizable images with potential applications for privacy computations. However, we show that our augmentation approach and simple SU(2) transformations do not enhance differential privacy and discuss the implications of this limitation.

cs.CV↗

arXiv2025

QuKAN: A Quantum Circuit Born Machine approach to Quantum Kolmogorov Arnold Networks

Kolmogorov Arnold Networks (KANs), built upon the Kolmogorov Arnold representation theorem (KAR), have demonstrated promising capabilities in expressing complex functions with fewer neurons. This is achieved by implementing learnable parameters on the edges instead of on the nodes, unlike traditional networks such as Multi-Layer Perceptrons (MLPs). However, KANs potential in quantum machine learning has not yet been well explored. In this work, we present an implementation of these KAN architectures in both hybrid and fully quantum forms using a Quantum Circuit Born Machine (QCBM). We adapt the KAN transfer using pre-trained residual functions, thereby exploiting the representational power of parametrized quantum circuits. In the hybrid model we combine classical KAN components with quantum subroutines, while the fully quantum version the entire architecture of the residual function is translated to a quantum model. We demonstrate the feasibility, interpretability and performance of the proposed Quantum KAN (QuKAN) architecture.

quant-ph↗

arXiv2025

Human in the Latent Loop (HILL): Interactively Guiding Model Training Through Human Intuition

Latent space representations are critical for understanding and improving the behavior of machine learning models, yet they often remain obscure and intricate. Understanding and exploring the latent space has the potential to contribute valuable human intuition and expertise about respective domains. In this work, we present HILL, an interactive framework allowing users to incorporate human intuition into the model training by interactively reshaping latent space representations. The modifications are infused into the model training loop via a novel approach inspired by knowledge distillation, treating the user's modifications as a teacher to guide the model in reshaping its intrinsic latent representation. The process allows the model to converge more effectively and overcome inefficiencies, as well as provide beneficial insights to the user. We evaluated HILL in a user study tasking participants to train an optimal model, closely observing the employed strategies. The results demonstrated that human-guided latent space modifications enhance model performance while maintaining generalization, yet also revealing the risks of including user biases. Our work introduces a novel human-AI interaction paradigm that infuses human intuition into model training and critically examines the impact of human intervention on training strategies and potential biases.

cs.LG↗

arXiv2025

TxP: Reciprocal Generation of Ground Pressure Dynamics and Activity Descriptions for Improving Human Activity Recognition

Sensor-based human activity recognition (HAR) has predominantly focused on Inertial Measurement Units and vision data, often overlooking the capabilities unique to pressure sensors, which capture subtle body dynamics and shifts in the center of mass. Despite their potential for postural and balance-based activities, pressure sensors remain underutilized in the HAR domain due to limited datasets. To bridge this gap, we propose to exploit generative foundation models with pressure-specific HAR techniques. Specifically, we present a bidirectional Text$\times$Pressure model that uses generative foundation models to interpret pressure data as natural language. TxP accomplishes two tasks: (1) Text2Pressure, converting activity text descriptions into pressure sequences, and (2) Pressure2Text, generating activity descriptions and classifications from dynamic pressure maps. Leveraging pre-trained models like CLIP and LLaMA 2 13B Chat, TxP is trained on our synthetic PressLang dataset, containing over 81,100 text-pressure pairs. Validated on real-world data for activities such as yoga and daily tasks, TxP provides novel approaches to data augmentation and classification grounded in atomic actions. This consequently improved HAR performance by up to 12.4\% in macro F1 score compared to the state-of-the-art, advancing pressure-based HAR with broader applications and deeper insights into human movement.

cs.AI↗

arXiv2025

Talk2X -- An Open-Source Toolkit Facilitating Deployment of LLM-Powered Chatbots on the Web

Integrated into websites, LLM-powered chatbots offer alternative means of navigation and information retrieval, leading to a shift in how users access information on the web. Yet, predominantly closed-sourced solutions limit proliferation among web hosts and suffer from a lack of transparency with regard to implementation details and energy efficiency. In this work, we propose our openly available agent Talk2X leveraging an adapted retrieval-augmented generation approach (RAG) combined with an automatically generated vector database, benefiting energy efficiency. Talk2X's architecture is generalizable to arbitrary websites offering developers a ready to use tool for integration. Using a mixed-methods approach, we evaluated Talk2X's usability by tasking users to acquire specific assets from an open science repository. Talk2X significantly improved task completion time, correctness, and user experience supporting users in quickly pinpointing specific information as compared to standard user-website interaction. Our findings contribute technical advancements to an ongoing paradigm shift of how we access information on the web.

cs.AI↗

arXiv2025

PIM: Physics-Informed Multi-task Pre-training for Improving Inertial Sensor-Based Human Activity Recognition

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-supervised learning (SSL) has emerged as an effective approach to leverage unlabeled data through pretext tasks, such as masked reconstruction and multitask learning with signal processing-based data augmentations, to pre-train encoder models. However, such methods are often derived from computer vision approaches that disregard physical mechanisms and constraints that govern wearable sensor data and the phenomena they reflect. In this paper, we propose a physics-informed multi-task pre-training (PIM) framework for IMU-based HAR. PIM generates pre-text tasks based on the understanding of basic physical aspects of human motion: including movement speed, angles of movement, and symmetry between sensor placements. Given a sensor signal, we calculate corresponding features using physics-based equations and use them as pretext tasks for SSL. This enables the model to capture fundamental physical characteristics of human activities, which is especially relevant for multi-sensor systems. Experimental evaluations on four HAR benchmark datasets demonstrate that the proposed method outperforms existing state-of-the-art methods, including data augmentation and masked reconstruction, in terms of accuracy and F1 score. We have observed gains of almost 10\% in macro f1 score and accuracy with only 2 to 8 labeled examples per class and up to 3% when there is no reduction in the amount of training data.

cs.CV↗

arXiv2025

Optimization of An Induced Magnetic Field-Based Positioning System

Using oscillating magnetic fields for indoor positioning is a robust way to resist dynamic environments. This work presents the hard- and software-related optimizations of an induced magnetic field positioning system. We describe a new coil architecture for both the transmitter and receiver, reducing inter-axes cross-talk. A new analog circuit design on the receiver side attains an acceptable noise level and increases the detection range from 4m to 8m (the covered area is increased from $50m^2$ to $200m^2$). The median positioning error is reduced from 0.56~m to 0.25m in the near field with fingerprinting methods. Experiments in office and factory areas (including robotic and industrial equipment) demonstrate the system's robustness in large areas. This work aims to enlighten researchers working on the same topic with constructive optimization directions on their own induced magnetic field-based systems.

eess.SP↗

arXiv2025

Hybrid CNN-Dilated Self-attention Model Using Inertial and Body-Area Electrostatic Sensing for Gym Workout Recognition, Counting, and User Authentification

While human body capacitance ($HBC$) has been explored as a novel wearable motion sensing modality, its competence has never been quantitatively demonstrated compared to that of the dominant inertial measurement unit ($IMU$) in practical scenarios. This work is thus motivated to evaluate the contribution of $HBC$ in wearable motion sensing. A real-life case study, gym workout tracking, is described to assess the effectiveness of $HBC$ as a complement to $IMU$ in activity recognition. Fifty gym sessions from ten volunteers were collected, bringing a fifty-hour annotated $IMU$ and $HBC$ dataset. With a hybrid CNN-Dilated neural network model empowered with the self-attention mechanism, $HBC$ slightly improves accuracy to the $IMU$ for workout recognition and has substantial advantages over $IMU$ for repetition counting. This work helps to enhance the understanding of $HBC$, a novel wearable motion-sensing modality based on the body-area electrostatic field. All materials presented in this work are open-sourced to promote further study \footnote{https://github.com/zhaxidele/Toolkit-for-HBC-sensing}.

eess.SP↗

arXiv2025

Towards Sustainable Web Agents: A Plea for Transparency and Dedicated Metrics for Energy Consumption

Improvements in the area of large language models have shifted towards the construction of models capable of using external tools and interpreting their outputs. These so-called web agents have the ability to interact autonomously with the internet. This allows them to become powerful daily assistants handling time-consuming, repetitive tasks while supporting users in their daily activities. While web agent research is thriving, the sustainability aspect of this research direction remains largely unexplored. We provide an initial exploration of the energy and CO2 cost associated with web agents. Our results show how different philosophies in web agent creation can severely impact the associated expended energy. We highlight lacking transparency regarding the disclosure of model parameters and processes used for some web agents as a limiting factor when estimating energy consumption. As such, our work advocates a change in thinking when evaluating web agents, warranting dedicated metrics for energy consumption and sustainability.

cs.AI↗