46 citations · 54 across the 15 of their papers we have counts for
23 papers
PocketTSLM: Deploying an On-Device Soft-Prompt Time-Series Language Model for Privacy-Preserving Smartphone Reasoning
Max Rosenblattl, Nikhil Krishnaswamy, Leon Nissen +9
Consumer phones and wearables increasingly collect physiological time-series data, yet most mobile systems treat these streams as summaries or text rather than as temporal evidence…
TimeCap: A Scalable Open-Source Pipeline for Captioning Wearable Health Time-Series Data
Max Rosenblattl, Milana Gurbanova, Karl Aurel Deck +16
Time-series data generated continuously across industrial, environmental, and especially health domains produce multimodal signals whose scale and heterogeneity make their processi…
On the Gage-Hamilton-Grayson Theorem for free boundary curves
Yueheng Bao, Lorenzo Sarnataro
We provide an alternative and independent proof of the analogue of the Gage-Hamilton-Grayson Theorem for embedded free boundary curve shortening flows in a convex planar domain est…
TimeNet: An Extensible Unified Data Infrastructure for Next-Generation Temporal Foundation Models
Martin Maritsch, Timo Stoffregen, Thomas Kaar +36
Temporal Foundation Models (TFMs) aim to generalize across domains, datasets, and tasks. Yet, their development remains constrained by fragmented, task-specific data formats, annot…
OpenTSLM TeeMoE: A Unified Time-Series Language Model for Forecasting, Contextual Prediction, and Reasoning
Tony Chen, Timo Stoffregen, Maxwell Xu +8
Real-world time-series applications increasingly require models that can handle time series forecasting, context-conditioned prediction, and language-based temporal reasoning. Yet…
Beyond Screen Time: Demonstrating the Value of App Activity Logs to Understand User Behavior Further
Ole Schmitt, Pauline Gieseler, Frederik Riedel +4
Research on smartphone use remains fragmented, with studies differing in measures, methods, and platforms, limiting what we know about everyday behavior. We argue that event-level…
The CAST-framework: Measure and model social media use as a multi-level phenomenon through real-world applications
David Grüning, Jasper Doeninghaus, Zina Efchary +8
Designing social media experiences that support well-being requires understanding when, how, and for whom use matters. Screen-time totals omit content and context, and connecting t…
How do people plan digitally: An in-the-wild investigation of task planning through a smartphone app
Srija Halder, Isabella Zimmermann, Linnea Körte +3
Daily planning supports goal attainment and productivity, and people increasingly delegate it to digital tools. Plans are postponed, revised, and left unresolved rather than execut…
TimeRLM: Recursive Language Models Enable Precise Anomaly Localization in Long-Context Time-Series
Nicolas Zumarraga, Lorenzo Steno, Ning Wang +9
Precise anomaly localization over long-context time series is a crucial task in monitoring applications across clinical care, industrial operations, financial services, and logisti…
OpenMHC: Accelerating the Science of Wearable Foundation Models
Narayan Schuetz, Yuze Bai, Lianggang Pan +16
Mobile and wearable devices offer an unprecedented opportunity for continuous, passive health monitoring and active health coaching. However, the largest wearable datasets are not…
The ABC of digital health: A framework for translating digital health interventions into real-world applications
David Grüning, Vincent Beermann, Jan Enkmann +3
Research-based digital health interventions are often presented as potential solutions for extending health care in the real world. Yet the vast majority of these interventions fai…
Patient-Level Multimodal Question Answering from Multi-Site Auscultation Recordings
Fan Wu, Tsai-Ning Wang, Nicolas Zumarraga +8
Auscultation is a vital diagnostic tool, yet its utility is often limited by subjective interpretation. While general-purpose Audio-Language Models (ALMs) excel in general domains,…
How Well Do Multimodal Models Reason on ECG Signals?
Maxwell A. Xu, Harish Haresamudram, Catherine W. Liu +11
While multimodal large language models offer a promising solution to the "black box" nature of health AI by generating interpretable reasoning traces, verifying the validity of the…
TS-Haystack: A Multi-Task Retrieval Benchmark for Long-Context Time-Series Reasoning
Nicolas Zumarraga, Thomas Kaar, Ning Wang +12
Time Series Language Models (TSLMs) promise reasoning over real-world temporal data, but their ability to retrieve and reason over long time-series remains largely untested. We int…
Bloom: Designing for LLM-Augmented Behavior Change Interactions
Matthew Jörke, Defne Genç, Valentin Teutschbein +7
Large language models (LLMs) offer novel opportunities to support health behavior change, yet existing work has narrowly focused on text-only interactions. Building on decades of H…
OpenTSLM: Time-Series Language Models for Reasoning over Multivariate Medical Text- and Time-Series Data
Patrick Langer, Thomas Kaar, Max Rosenblattl +19
LLMs have emerged as powerful tools for interpreting multimodal data. In medicine, they hold particular promise for synthesizing large volumes of clinical information into actionab…
Spezi Data Pipeline: Streamlining FHIR-based Interoperable Digital Health Data Workflows
Vasiliki Bikia, Paul Schmiedmayer, Aydin Zahedivash +6
The increasing adoption of digital health technologies has amplified the need for robust, interoperable solutions to manage complex healthcare data. We present the Spezi Data Pipel…
Toward Scalable Access to Neurodevelopmental Screening: Insights, Implementation, and Challenges
Andreas Bauer, William Bosl, Oliver Aalami +1
Children with neurodevelopmental disorders require timely intervention to improve long-term outcomes, yet early screening remains inaccessible in many regions. A scalable solution…
Medicine on the Edge: Comparative Performance Analysis of On-Device LLMs for Clinical Reasoning
Leon Nissen, Philipp Zagar, Vishnu Ravi +5
The deployment of Large Language Models (LLM) on mobile devices offers significant potential for medical applications, enhancing privacy, security, and cost-efficiency by eliminati…
Dynamic Fog Computing for Enhanced LLM Execution in Medical Applications
Philipp Zagar, Vishnu Ravi, Lauren Aalami +3
The ability of large language models (LLMs) to transform, interpret, and comprehend vast quantities of heterogeneous data presents a significant opportunity to enhance data-driven…
GPTCoach: Towards LLM-Based Physical Activity Coaching
Matthew Jörke, Shardul Sapkota, Lyndsea Warkenthien +4
Mobile health applications show promise for scalable physical activity promotion but are often insufficiently personalized. In contrast, health coaching offers highly personalized…
LLM on FHIR -- Demystifying Health Records
Paul Schmiedmayer, Adrit Rao, Philipp Zagar +4
Objective: To enhance health literacy and accessibility of health information for a diverse patient population by developing a patient-centered artificial intelligence (AI) solutio…
The Path to a Modular and Standards-based Digital Health Ecosystem
Paul Schmiedmayer, Vishnu Ravi, Oliver Aalami
Software engineering for digital health applications entails several challenges, including heterogeneous data acquisition, data standardization, software reuse, security, and priva…