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Assessing Surgical Skill in Orthopaedic Trauma Surgery Training
Item type: Journal Article
Stauffer, Tobias; Schlegel, Tiffany; Diwersi, Nadine; et al. (2026)
Background: Surgical skill assessment in orthopaedic trauma surgery still relies on subjective expert ratings, which limits consistency and scalability. While digitalization offers a path toward objective and scalable assessment, the highly manual and haptic nature of surgery makes tool use difficult to capture digitally, keeping such approaches underdeveloped. This study introduces a digital assessment framework for orthopaedic trauma training that derives digital behavioral metrics (DBM) from tracked surgical tool motion. Specifically, it investigates (1) which DBM indicate technical proficiency and (2) to what extent these DBM are capable of predicting expert-rated surgical performance.
Methods: Twenty-eight participants performed 3 standardized fracture fixations on synthetic bone models of the radius, ulna, and fibula. Tool motion was captured and transformed into a digital twin from which metrics such as path length, smoothness, and task duration were derived. These metrics were statistically compared with the average Global Rating Scale (GRS) obtained from 4 experts who rated each surgical performance. (1) Correlation analysis identified skill-relevant metrics and (2) a predictive model was trained to estimate performance from DBM evaluating its accuracy against the individual expert ratings.
Results: (1) Several DBM were found to be indicative of surgical performance. Measures based on tool path length and time per activity showed strong correlations with expert ratings, reaching coefficients of up to 0.6. Correlation strength varied across tools and procedures. (2) The predictive model achieved a mean absolute difference of 3.8 points from the average GRS score (scale range: 28-70), outperforming the mean interexpert difference of 4.6 points.
Conclusion: DBM were identified as valid indicators of surgical skill. The study further demonstrated their predictive value, showing closer alignment with experts’ average GRS score than individual expert ratings. These findings highlight the feasibility of objective, expert-independent performance assessment in orthopaedic trauma surgery training.
Snow Accumulation Monitoring using GNSS-Interferometric Reflectometry for Antarctica
Item type: Conference Poster
Crocetti, Laura; Watson, Christopher; Schartner, Matthias; et al. (2026)
Antarctica plays a central role in Earth's global climate system and stores most of the planet's freshwater. However, due to the continent's remoteness and extreme conditions, reliable in situ observations of snow accumulation remain rare. This gap in measurements makes it difficult to constrain ice sheet models and accurately project Antarctica's contribution to global sea level rise. In particular, regions such as the Totten Glacier in East Antarctica are of interest due to the significant mass loss since the 1990s, dominated by changes in coastal ice dynamics. In the context of Antarctica, GNSS Interferometric Reflectometry (GNSS-IR) presents an efficient and sustainable approach to monitor changes in snow accumulation with the potential to offer insights into regional surface mass balance models.This contribution investigates a unique in situ dataset of six GNSS stations deployed on the Totten Glacier, operated seasonally between November 2016 and January 2019. These stations were originally designed to track ice motion, but they also capture reflections from the snow surface. By applying GNSS-IR, time series of snow accumulation are generated – once with the traditional retrieval approach using the gnssrefl software, and once by testing a novel machine learning-based retrieval framework. The derived snow accumulation time series are cross-referenced with outputs from regional surface mass balance models. The results provide insights into the spatio-temporal patterns of snow accumulation over the Totten Glacier and showcase the potential of GNSS-IR for environmental sensing.
The EPOS Research Infrastructure: a federated approach to integrate solid Earth science data and services
Item type: Journal Article
Cocco, Massimo; Freda, Carmela; Atakan, Kuvvet; et al. (2022)
The European Plate Observing System (EPOS) is a Research Infrastructure (RI) committed to enabling excellent science through the integration, accessibility, use and re-use of solid Earth science data, research products and services, as well as by promoting physical access to research facilities. This article presents and describes the EPOS RI and introduces the contents of its Delivery Framework. In November 2018, EPOS ERIC (European Research Infrastructure Consortium) has been granted by the European Commission and was established to design and implement a long-term plan for the integration of research infrastructures for solid Earth science in Europe. Specifically, the EPOS mission is to create and operate a highly distributed and sustainable research infrastructure to provide coordinated access to harmonized, interoperable and quality-controlled data from diverse solid Earth science disciplines, together with tools for their use in analysis and modelling. EPOS relies on leading-edge e-science solutions and is committed to open access, thus enabling a step towards the change in multidisciplinary and cross-disciplinary scientific research in Earth science. The EPOS architecture and its Delivery Framework are discussed in this article to present the contributions to open science and FAIR (Findable, Accessible, Interoperable, and Reusable) data management, as well as to emphasize the community building process that supported the design, implementation and construction of the EPOS RI.
“Are You Okay, Honey?”: Recognizing Emotions Among Couples Managing Diabetes in Daily Life Using Multimodal Real-World Smartwatch Data
Item type: Journal Article
Boateng, George; Zhao, Xiangyu; Speichert, Malgorzata; et al. (2026)
Couples generally manage chronic diseases together and the management takes an emotional toll on both patients and their romantic partners. Consequently, recognizing the emotions of each partner in daily life could provide insight into their emotional well-being in chronic disease management. Currently, the process of assessing each partner’s emotions is manual, time-intensive, and costly. Despite the existence of works on emotion recognition among couples, none of these works have used data collected from couples’ interactions in daily life. In this work, we collected 85 h (1021 5-min samples) of real-world multimodal smartwatch sensor data (speech, heart rate, accelerometer, and gyroscope) and self-reported emotion data (n = 612) from 26 partners (13 couples) managing diabetes mellitus type 2 in daily life. We extracted physiological, movement, acoustic, and linguistic features, and trained machine learning models (support vector machine and random forest) to recognize each partner’s self-reported emotions (valence and arousal). Our results from the best models—balanced accuracies of 63.8% and 78.1% for arousal and valence respectively—are better than the results from (1) chance, (2) prior work that also used data from German-speaking, Swiss-based couples, and (3) partners’ perceptions of each other’s emotions. This work contributes toward building automated emotion recognition systems that would eventually enable partners to monitor their emotions in daily life and enable the delivery of interventions to improve their emotional well-being.
INFLUENCE OF PATERNAL ENVIRONMENTAL EXPOSURES ON ALLELE-SPECIFIC GENE EXPRESSION IN PREIMPLANTATION EMBRYOS
Item type: Doctoral Thesis
Leonard C, Steg (2026)