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System Identification of User Engagement in mHealth Behavioral Interventions ⁎

IFAC-PapersOnLine 2024年第15期58卷 508-513页

作者： Mohamed El Mistiri Steven De La Torre Karine Tung Benjamin M. Marlin Misha Pavel Predrag Klasnja Donna Spruijt-Metz Daniel E. Rivera Control Systems Engineering Lab Arizona State University Tempe AZ 85281 USA The Herbert Wertheim School of Public Health and Human Longevity Science University of California San Diego CA 92093 USA Manning College of Information and Computer Sciences University of Massachusetts Amherst Amherst MA 01003 USA Khoury College of Computer Sciences & Bouve College of Health Sciences Northeastern University Boston MA 02115 USA Division of Biomedical and Health Informatics School of Information University of Michigan Ann Arbor MU 48109 USA USC Dornsife Center for Economic and Social Research Department of Psychology and Population and Public Health Sciences University of Southern California Los Angeles CA 90089 USA

Digital behavior change interventions (DBCIs) such as “just-in-time” adaptive interventions (JITAIs) have demonstrated efficacy for increasing physical activity behavior. However, the effectiveness of these interventions is heavily dependent upon user engagement. Despite the inherent dynamic nature of engagement, as it varies over time based on an individual’s changing environment, context, and psychological state, the current understanding of engagement primarily comes from static snapshots of the behavior. The availability of intensive longitudinal data from JITAIs provides a unique opportunity to build and test dynamic models of behavior change from a process systems lens, relying on prediction-error methods from system identification. However, data missingness is a significant practical consideration in this process. Therefore, in this work we address missingness using a Bayesian imputation approach, which we evaluate using data from the HeartSteps II JITAI. Ultimately, the methods presented support the discovery of key factors that impact engagement behavior over time and can play an important role in the development of large-scale personalized interventions.

关键词： eHealth Bayesian methods Computational Social sciences Time series modelling

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Large Language Models for Disease Diagnosis: A Scoping Review

arXiv

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arXiv 2024年

作者： Zhou, Shuang Xu, Zidu Zhang, Mian Xu, Chunpu Guo, Yawen Zhan, Zaifu Ding, Sirui Wang, Jiashuo Xu, Kaishuai Fang, Yi Xia, Liqiao Yeung, Jeremy Zha, Daochen Lin, Mingquan Zhang, Rui Division of Computational Health Sciences Department of Surgery University of Minnesota MinneapolisMN United States School of Nursing Columbia University New YorkNY United States Erik Jonsson School of Engineering and Computer Science University of Texas at Dallas RichardsonTX United States Department of Computing The Hong Kong Polytechnic University Hong Kong Department of Informatics University of California Irvine IrvineCA United States Department of Electrical and Computer Engineering University of Minnesota MinneapolisMN United States Bakar Computational Health Sciences Institute University of California San Francisco San FranciscoCA United States Shanghai China Department of Industrial and Systems Engineering The Hong Kong Polytechnic University Hong Kong Department of Computer Science Rice University HoustonTX United States

Automatic disease diagnosis has become increasingly valuable in clinical practice. The advent of large language models (LLMs) has catalyzed a paradigm shift in artificial intelligence, with growing evidence supporting the efficacy of LLMs in diagnostic tasks. Despite the growing attention in this field, many critical research questions remain under-explored. For instance, what diseases and LLM techniques have been investigated for diagnostic tasks? How can suitable LLM techniques and evaluation methods be selected for clinical decision-making? To answer these questions, we performed a comprehensive analysis of LLM-based methods for disease diagnosis. This scoping review examined the types of diseases, associated organ systems, relevant clinical data, LLM techniques, and evaluation methods reported in existing studies. Furthermore, we offered guidelines for data preprocessing and the selection of appropriate LLM techniques and evaluation strategies for diagnostic tasks. We also assessed the limitations of current research and delineated the challenges and future directions in this research field. In summary, our review outlined a blueprint for LLM-based disease diagnosis, helping to streamline and guide future research endeavors. Copyright © 2024, The Authors. All rights reserved.

关键词： Diseases

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Holistic Small-Signal Modeling and AI-Assisted Region-Based Stability Analysis of Autonomous AC and DC Microgrids 12

Holistic Small-Signal Modeling and AI-Assisted Region-Based ...

引用

12th Annual IEEE Energy Conversion Congress and Exposition, ECCE 2020

作者： Men, Yuxi Ding, Lizhi Du, Yuhua Lu, Xiaonan Zhao, Dongbo Cao, Yue Temple University College of Engineering PhiladelphiaPA United States Argonne Nationa Laboratory Energy Systems Division LemontIL United States Oregon State University School of Electrical Engineering and Computer Science CorvallisOR United States

ISBN: (纸本)9781728158266

In this paper, a holistic small-signal model of hybrid AC and DC microgrids is developed, including AC subsection, DC subsection, and interface inverters between AC and DC buses. Based on the derived complete small-signal model, a region-based stability analysis approach is proposed and developed. Meanwhile, to obtain the steady-state operating points used in the regionbased stability analysis, practical and effective power flow calculation is conducted for droop-controlled hybrid AC and DC microgrids. Rather than following a conventional point-by-point stability evaluation procedure, the stability region implemented in this work is derived based on the selected cross-domain parameters from either control systems or main power circuits. Furthermore, an artificial intelligence (AI) assisted Kernel Ridge Regression (KRR) algorithm is implemented to derive the stability boundary with enhanced computational efficiency. Simulation tests are presented to demonstrate the effectiveness of the proposed method. © 2020 IEEE.

关键词： Electric load flow

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Traffic light and moving object detection for a guide-dog robot

Traffic light and moving object detection for a guide-dog ro...

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3rd Asian Conference on Artificial Intelligence Technology, ACAIT 2019

作者： Chen, Qiang Chen, Yinong Zhu, Jinhui De Luca, Gennaro Zhang, Mei Guo, Ying College of Computer and Information Science Southwest University Chongqing China School of Computing Informatics and Decision Systems Engineering Arizona State University Tempe United States School of Software Engineering South China University of Technology Guangzhou China School of Automation Science and Control Engineering South China University of Technology Guangzhou China School of Information Science and Technology Qingdao University of Science and Technology Qingdao China

Guide dogs are helpful for visually impaired people for navigating through the streets. However, it is expensive and time consuming to train a guide dog. In addition, a guide dog cannot decide when and where to cross a street safely, and it is up to the human to decide. Here, the authors propose a framework for creating a guide dog robot by using artificial intelligence and other technologies. The proposed framework is based on an Intel UP squared board, together with a Neural Compute Stick Movidius to process the images gathered from a GoPro camera. MobileNet single shot detector (SSD) is the main framework to detect the moving objects in the environment. The final decision is made after fusing the information gathered from all the sources. The authors also apply the Amazon Alexa device for the voice communication between the guide dog robot and the visually impaired person. A prototype of the proposed system is implemented and tested. Experimental results show that the proposed framework can process the information at a traffic intersection scene and can guide a blind person to cross the street safely. © 2020 Institution of engineering and Technology. All rights reserved.

关键词： Object detection

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Community detection for gossip dynamics with stubborn agents

arXiv

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arXiv 2020年

作者： Xing, Yu He, Xingkang Fang, Haitao Johansson, Karl Henrik Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 School of Mathematical Sciences University of Chinese Academy of Sciences Beijing100049 China Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology StockholmSE-10044 Sweden

We consider a community detection problem for gossip dynamics with stubborn agents in this paper. It is assumed that the communication probability matrix for agent pairs has a block structure. More specifically, we assume that the network can be divided into two communities, and the communication probability of two agents depends on whether they are in the same community. Stability of the model is investigated, and expectation of stationary distribution is characterized, indicating under the block assumption, the stationary behaviors of agents in the same community are similar. It is also shown that agents in different communities display distinct behaviors if and only if state averages of stubborn agents in different communities are not identical. A community detection algorithm is then proposed to recover community structure and to estimate communication probability parameters. It is verified that the community detection part converges in finite time, and the parameter estimation part converges almost surely. Simulations are given to illustrate algorithm performance. Copyright © 2020, The Authors. All rights reserved.

关键词： Population dynamics

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FaSTrack:A Modular Framework for Real-Time Motion Planning and Guaranteed Safe Tracking

FaSTrack:A Modular Framework for Real-Time Motion Planning a...

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作者： Chen, Mo Herbert, Sylvia L. Hu, Haimin Pu, Ye Fisac, Jaime Fernandez Bansal, Somil Han, Soojean Tomlin, Claire J. School of Computing Science Simon Fraser University BurnabyBCV5A 1S6 Canada Department of Mechanical and Aerospace Engineering University of California San Diego San DiegoCA92122 United States Department of Electrical Engineering Princeton University PrincetonNJ08536 United States Department of Electrical and Electronic Engineering University of Melbourne ParkvilleVIC3010 Australia Department of Electrical Engineering and Computer Sciences University of California Berkeley BerkeleyCA94704 United States Control and Dynamical Systems Program California Institute of Technology Los AngelesCA90019 United States

Real-time, guaranteed safe trajectory planning is vital for navigation in unknown environments. However, real-time navigation algorithms typically sacrifice robustness for computation speed. Alternatively, provably safe trajectory planning tends to be too computationally intensive for real-time replanning. We propose FaSTrack, Fast and Safe Tracking, a framework that achieves both real-time replanning and guaranteed safety. In this framework, real-time computation is achieved by allowing any trajectory planner to use a simplified planning model of the system. The plan is tracked by the system, represented by a more realistic, higher dimensional tracking model. We precompute the tracking error bound (TEB) due to mismatch between the two models and due to external disturbances. We also obtain the corresponding tracking controller used to stay within the TEB. The precomputation does not require prior knowledge of the environment. We demonstrate FaSTrack using Hamilton-Jacobi reachability for precomputation and three different real-time trajectory planners with three different tracking-planning model pairs. © 1963-2012 IEEE.

关键词： Real time systems

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Optimization-based on-road path planning for articulated vehicles

arXiv

引用

arXiv 2020年

作者： Oliveira, Rui Ljungqvist, Oskar Lima, Pedro F. Wahlberg, Bo Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Division of Automatic Control Linköping University Linköping Sweden Scania CV Autonomous Transport Solutions Södertälje Sweden

Maneuvering an articulated vehicle on narrow road stretches is often a challenging task for a human driver. Unless the vehicle is accurately steered, parts of the vehicle’s bodies may exceed its assigned drive lane, resulting in an increased risk of collision with surrounding traffic. In this work, an optimization-based path-planning algorithm is proposed targeting on-road driving scenarios for articulated vehicles composed of a tractor and a trailer. To this end, we model the tractor-trailer vehicle in a road-aligned coordinate frame suited for on-road planning. Based on driving heuristics, a set of different optimization objectives is proposed, with the overall goal of designing a path planner that computes paths which minimize the off-track of the vehicle bodies swept area, while remaining on the road and avoiding collision with obstacles. The proposed optimization-based path-planning algorithm, together with the different optimization objectives, is evaluated and analyzed in simulations on a set of complicated and practically relevant on-road planning scenarios using the most challenging tractor-trailer dimensions. Copyright © 2020, The Authors. All rights reserved.

关键词： Motion planning

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Privacy-Preserving Set-Based Estimation Using Partially Homomorphic Encryption

arXiv

引用

arXiv 2020年

作者： Alanwar, Amr Gaßmann, Victor He, Xingkang Said, Hazem Sandberg, Henrik Johansson, Karl Henrik Althoff, Matthias School of Computer Science and Engineering Constructor University Germany Department of Computer Engineering Technical University of Munich Germany Division of Decision and Control Systems Kth Royal Institute of Technology Sweden Department of Computer Engineering Ain Shams University Egypt

The set-based estimation has gained a lot of attention due to its ability to guarantee state enclosures for safety-critical systems. However, collecting measurements from distributed sensors often requires outsourcing the set-based operations to an aggregator node, raising many privacy concerns. To address this problem, we present set-based estimation protocols using partially homomorphic encryption that preserve the privacy of the measurements and sets bounding the estimates. We consider a linear discrete-time dynamical system with bounded modeling and measurement uncertainties. Sets are represented by zonotopes and constrained zonotopes as they can compactly represent high-dimensional sets and are closed under linear maps and Minkowski addition. By selectively encrypting parameters of the set representations, we establish the notion of encrypted sets and intersect sets in the encrypted domain, which enables guaranteed state estimation while ensuring privacy. In particular, we show that our protocols achieve computational privacy using the cryptographic notion of computational indistinguishability. We demonstrate the efficiency of our approach by localizing a real mobile quadcopter using ultra-wideband wireless devices. Copyright © 2020, The Authors. All rights reserved.

关键词： Uncertainty analysis

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Maximizing Privacy in MIMO Cyber-Physical systems Using the Chapman-Robbins Bound

Maximizing Privacy in MIMO Cyber-Physical Systems Using the ...

引用

IEEE Conference on decision and control

作者： Rijad Alisic Marco Molinari Philip E. Paré Henrik Sandberg Division of Decision and Control Systems KTH Royal Institute of Technology Sweden Division of Applied Thermodynamics and Refrigeration KTH Royal Institute of Technology Sweden School of Electrical and Computer Engineering Purdue University United States

ISBN: (数字)9781728174471

ISBN: (纸本)9781728174488

Privacy breaches of cyber-physical systems could expose vulnerabilities to an adversary. Here, privacy leaks of step inputs to linear time-invariant systems are mitigated through additive Gaussian noise. Fundamental lower bounds on the privacy are derived, which are based on the variance of any estimator that seeks to recreate the input. Fully private inputs are investigated and related to transmission zeros. Thereafter, a method to increase the privacy of optimal step inputs is presented and a privacy-utility trade-off bound is derived. Finally, these results are verified on data from the KTH Live-In Lab Testbed, showing good correspondence with theoretical results.

关键词： Privacy Noise measurement Smart buildings Signal to noise ratio MIMO communication Intelligent sensors Databases

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Linear system identification under multiplicative noise from multiple trajectory data

arXiv

引用

arXiv 2020年

作者： Xing, Yu Gravell, Ben He, Xingkang Johansson, Karl H. Summers, Tyler Key Lab of Systems and Control Academy of Mathematics and Systems Science Chinese Academy of Sciences School of Mathematical Sciences University of Chinese Academy of Sciences Beijing China Department of Mechanical Engineering University of Texas Dallas RichardsonTX United States Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

The study of multiplicative noise models has a long history in control theory but is re-emerging in the context of complex networked systems and systems with learning-based control. We consider linear system identification with multiplicative noise from multiple state-input trajectory data. We propose exploratory input signals along with a least-squares algorithm to simultaneously estimate nominal system parameters and multiplicative noise covariance matrices. The asymptotic consistency of the least-squares estimator is demonstrated by analyzing first and second moment dynamics of the system. The results are illustrated by numerical simulations. Copyright © 2020, The Authors. All rights reserved.

关键词： Linear systems

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