Lukas Eichelberger

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Eichelberger
First name
Lukas
Email
lukas.eichelberger@student.unisg.ch
Phone
071 224 2763

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    Item type:Publication,
    FoodCoach: Fully Automated Diet Counseling
    (2025-02-11)
    Jing Wu  
    ;
    Simon Mayer  
    ;
    Simeon Pilz
    ;
    Yasmine S. Antille
    ;
    Jan L. Albert
    Unhealthy dietary habits are a major preventable risk factor for widespread non-communicable diseases (NCD). Diet counseling is effective in managing diet-related NCDs, but constrained by its manual nature and limited (clinical) resources. To address these challenges, we propose a fully automated diet counseling system FoodCoach. It monitors people's food purchases using digital receipts from loyalty cards and provides structured dietary recommendations. We introduce the FoodCoach system's recommender algorithm and architecture, along with evaluation results from a two-arm randomized controlled trial involving 61 participants. The trial results demonstrate the technical feasibility and potential for scalable, fully automated diet counseling, despite not showing a significant change in participants' food purchase healthiness. We further show how others can deploy and extend the FoodCoach system in their own context and provide all relevant component implementations. Our core research contributions are: 1) a novel dietary recommendation algorithm designed and implemented with clinical experts, and 2) a scalable system architecture that employs a knowledge graph for enhanced interoperability and applicability to diverse domains and data sources. From a practical perspective, FoodCoach can augment traditional diet counseling through automatic diet monitoring and evaluation modules. Additionally, it streamlines the counseling process, conserving clinical resources, and ultimately contributing to the reduction of NCD prevalence.
    Type:journal article
    Journal:IEEE Journal of Biomedical and Health Informatics
    DOI:10.1109/JBHI.2025.3540899
    URI:https://www.alexandria.unisg.ch/handle/20.500.14171/122042
    Scopus© Citations 10
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    Electromyography-based Kinesthetic Teaching of Industrial Collaborative Robots
    (ACM, 2024-03-11)
    Felix Wohlgemuth
    ;
    Iori Mizutani  
    ;
    Lukas Eichelberger  
    ;
    Simon Mayer  
    Current methods for robot teaching still lack intuitiveness and efficiency or require instrumentation of the environment (e.g., with cameras). This poses problems, especially for companies that cannot afford dedicated robot programmers. Classical online teaching with a teach pendant (TP) can be tedious and confusing, and kinesthetic teaching (KT) is often perceived as inefficient since toggling gravity compensation mode forces users to switch between interfaces or constrains them physically. We propose a novel robot teaching method that allows users to activate gravity compensation mode, confirm positions along trajectories, and manipulate the end effector. In our system, this is done via hand gestures that occur naturally while handling the robot and that we detect by equipping an operator with a wearable electromyography (EMG) armband. To evaluate our system, we compared it to a commercially available KT system in a user study that yielded statistical evidence that our approach is significantly faster while no difference regarding the perceived usability of the systems was found. Additionally, expert interviews confirm that the baseline system is state of the art and confirmed the market potential of EMG-based KT. Finally, we confirmed that the gesture classifier does not need to be re-trained for each user, which makes the proposed system a highly interesting option in practice that is cost-effective, efficient, and provides high teaching ergonomics.
    Type:conference paper
    Journal:Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction
    DOI:10.1145/3610978.3640615
    URI:https://www.alexandria.unisg.ch/handle/20.500.14171/122128
    Scopus© Citations 3
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    Item type:Publication,
    Supernatural Superserious: Reactive Executable Markers
    (Association of Computing Machinery, 2025-10-12)
    Lukas Eichelberger  
    ;
    Simon Mayer  
    ;
    Ganesh Ramanathan  
    Machine-readable markers, such as QR codes, are used in a wide range of applications. These markers usually feature a single static payload that directs users to further application-relevant information, e.g., on a website or in local data resources. Recent research has introduced advanced versions of such markers: reactive QR codes that carry multiple payloads that are revealed depending on the marker’s context and executable QR codes that carry executable code as payload. Building on these works, we introduce Reactive Executable Markers (REM). Based on exemplary application scenarios, we illustrate how REMs enable the offloading of the interpretation of environmental information to the REM designer without relying on network connectivity. Finally, we present a REM demonstrator and discuss the potential future evolution of REMs.
    Type:conference contribution
    DOI:10.1145/3714394.3754409
    URI:https://www.alexandria.unisg.ch/handle/20.500.14171/123539