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Felix Wortmann
Title
Prof. Dr.
Last Name
Wortmann
First name
Felix
Email
felix.wortmann@unisg.ch
Phone
+41 71 224 7325
Homepage
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1 - 10 of 136
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PublicationBlockchain for the IoT: Privacy-Preserving Protection of Sensor Data(Assoc. of Information Systems, )
;Chanson, Mathieu ;Bogner, AndreasA constantly growing pool of smart, connected Internet of Things (IoT) devices poses completely new challenges for business regarding security and privacy. In fact, the widespread adoption of smart products might depend on the ability of organizations to offer systems that ensure adequate sensor data integrity while guaranteeing sufficient user privacy. In light of these challenges, previous research indicates that blockchain technology may be a promising means to mitigate issues of data security arising in the IoT. Building upon the existing body of knowledge, we propose a design theory, including requirements, design principles, and features, for a blockchain-based sensor data protection system (SDPS) that leverages data certification. We then design and develop an instantiation of an SDPS (CertifiCar) in three iterative cycles that prevents the fraudulent manipulation of car mileage data. Furthermore, we provide an ex-post evaluation of our design theory considering CertifiCar and two additional use cases in the realm of pharmaceutical supply chains and energy microgrids. The evaluation results suggest that the proposed design ensures the tamper-resistant gathering, processing, and exchange of IoT sensor data in a privacy-preserving, scalable, and efficient manner.Type: journal articleJournal: Journal of the Association for Information SystemsVolume: Vol. 20, Issue 9Issue: Article 10DOI: 10.17705/1jais.00567Scopus© Citations 83 -
PublicationMachine learning for non-invasive sensing of hypoglycaemia while driving in people with diabetes(Wiley Online Library, 2023-02-15)
;Lehmann, Vera ;Zueger, Thomas ;Maritsch, Martin ;Kraus, Mathias ;Albrecht, Caroline ;Bérubé, Caterina ;Feuerriegel, Stefan ;Styger, Naïma ;Lagger, Sophie ;Laimer, MarkusStettler, ChristophType: journal articleJournal: Diabetes, Obesity and MetabolismVolume: 26 -
PublicationA Scalable Risk-Scoring System Based on Consumer-Grade Wearables for Inpatients With COVID-19: Statistical Analysis and Model Development(JMIR, 2022-05-25)
;Föll, Simon ;Lison, Adrian ;Maritsch, Martin ;Klingberg, Karsten ;Lehmann, Vera ;Züger, Thomas ;Srivastava, David ;Jegerlehner, Sabrina ;Feuerriegel, Stefan ;Exadaktylos, AristomenisType: journal articleVolume: 6Issue: 6 -
PublicationA KPI Set for Steering the IoT Business in Product Companies( 2022-02-17)
;Gebauer, HeikoType: journal articleJournal: Research Technology ManagementVolume: 65Issue: 2Scopus© Citations 3 -
PublicationTowards Non-intrusive Camera-Based Heart Rate Variability Estimation in the Car Under Naturalistic Condition(IEEE, 2022-07-15)
;Liu, Shu ;Zhou, Zimu ;Maritsch, Martin ;He, XiaoxiType: journal articleJournal: IEEE Internet of Things JournalVolume: 09Issue: 14Scopus© Citations 3 -
PublicationPatterns of business model innovation for advancing IoT platforms(Emerald, 2022-01-03)
;Markfort, Lino ;Arzt, Alexander ;Kögler, Philipp ;Gebauer, Heiko ;Haugk, Sebastian ;Leyh, ChristianPurpose – The emergence of Internet of Things (IoT) platforms in product companies opens up new data-driven business opportunities. This paper looks at the emergence of these IoT platforms from a business-model perspective. Design/methodology/approach – The study applies a mixed method with two research studies: Study I–a cluster analysis based on a quantitative survey, and Study II–case studies based on qualitative interviews. Findings – The findings reveal that there is no gradual shift in a company’s business model, but in fact three distinct and sequential patterns of business model innovations: (1) platform skimming, (2) platform revenue generation and (3) platform orchestration. Research limitations/implications – The results are subject to the typical limitations of both quantitative and qualitative studies. Practical implications – The results provide guidance to managers on how to modify the components of the business model (value proposition, value creation and/or delivery and profit equation) in order to enable platforms to advance. Social implications – As IoT platforms continue to advance, product companies achieve better performance in terms of productivity and profitability, and more easily secure competitive advantages and jobs. Originality/value – The paper makes three original contributions: (1) it is the first quantitative study on IoT platforms in product companies, (2) identifies three patterns of business model innovations and (3) offers a first process perspective for understanding the sequence of these patterns as IoT platforms advance.Type: journal articleJournal: Journal of Service ManagementVolume: 33Issue: 1Scopus© Citations 8 -
PublicationUnderstanding the Interactions Between Driving Behavior and Well-being in Daily Driving: Causal Analysis of a Field Study( 2022-08)
;Stephan, PaulType: journal articleJournal: J Med Internet ResVolume: 24Issue: 8 -
PublicationBidding on a peer-to-peer energy market - an expoloratory field study(INFORMS, 2022-09-09)
;Wörner, Anselma Meret ;Tiefenbeck, Verena ;Ableitner, LilianeAzevedo, InêsType: journal articleJournal: Information Systems ResearchVolume: 33/3Scopus© Citations 5 -
PublicationFLIRT: A Feature Generation Toolkit for Wearable Data( 2021-11-11)
;Föll, Simon ;Maritsch, Martin ;Spinola, Federica ;Mishra, Varun ;Barata, FilipeBackground and Objective: Researchers use wearable sensing data and machine learning (ML) models to predict various health and behavioral outcomes. However, sensor data from commercial wearables are prone to noise, missing, or artifacts. Even with the recent interest in deploying commercial wearables for long-term studies, there does not exist a standardized way to process the raw sensor data and researchers often use highly specific functions to preprocess, clean, normalize, and compute features. This leads to a lack of uniformity and reproducibility across different studies, making it difficult to compare results. To overcome these issues, we present FLIRT: A Feature Generation Toolkit for Wearable Data; it is an open-source Python package that focuses on processing physiological data specifically from commercial wearables with all its challenges from data cleaning to feature extraction. Methods: FLIRT leverages a variety of state-of-the-art algorithms (e.g., particle filters, ML-based artifact detection) to ensure a robust preprocessing of physiological data from wearables. In a subsequent step, FLIRT utilizes a sliding-window approach and calculates a feature vector of more than 100 dimensions – a basis for a wide variety of ML algorithms. Results: We evaluated FLIRT on the publicly available WESAD dataset, which focuses on stress detection with an Empatica E4 wearable. Preprocessing the data with FLIRT ensures that unintended noise and artifacts are appropriately filtered. In the classification task, FLIRT outperforms the preprocessing baseline of the original WESAD paper. Conclusion: FLIRT provides functionalities beyond existing packages that can address unmet needs in physiological data processing and feature generation: (a) integrated handling of common wearable file formats (e.g., Empatica E4 archives), (b) robust preprocessing, and (c) standardized feature generation that ensures reproducibility of results. Nevertheless, while FLIRT comes with a default configuration to accommodate most situations, it offers a highly configurable interface for all of its implemented algorithms to account for specific needs.Type: journal articleJournal: Computer Methods and Programs in BiomedicineVolume: 212Issue: NovemberScopus© Citations 10 -
PublicationWhen Do Drivers Interact with In-Vehicle Well-being Interventions? An Exploratory Analysis of a Longitudinal Study on Public Roads(Association for Computing Machinery, 2021)
;Mishra, Varun ;Liu, Shu ;Berger, Thomas ;Kotz, DavidType: journal articleJournal: Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.Volume: 5Issue: 1