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Closed-Loop Multimodal Neuromodulation of Vagus Nerve for Control of Heart Rate. 用于控制心率的迷走神经闭环多模态神经调控技术
Pub Date : 2024-07-01 Epub Date: 2024-09-05 DOI: 10.23919/acc60939.2024.10644421
Shane A Bender, David B Green, Kevin L Kilgore, Niloy Bhadra, Jeffery L Ardell, Tina L Vrabec

The use of electrical current to modulate neurons for autonomic regulation requires the ability to both up-regulate and down-regulate the nervous system. An implanted system employing this electrical neuromodulation would also need to adapt to changes in autonomic state in real-time. Stimulation of autonomic nerves at frequencies in the range 1-30 Hz has been a well-established technique for increasing neural activity. Vagus nerve stimulation (VNS) has been shown to be sensitive to frequency adjustments, which can be used to more precisely control the effect as compared to amplitude modulation. Kilohertz frequency alternating current (KHFAC) is a proven technique for blocking action potential conduction to reduce neural activity. Additionally, KHFAC can be reliably modulated by simple amplitude modulation. Although there are many types of commonly used closed-loop controllers, many conventional methods do not respond well to long system delays or discontinuities. Fuzzy logic control (FLC) is a state-based controller that can describe the discontinuities of the system linguistically and then translate the state transition to a continuous output signal. In our preparation, a single bipolar electrode was placed on the vagus nerve and controlled by a fuzzy logic controller to deliver both stimulation and KHFAC to control heart rate. The FLC was able to both change the heart rate to selected values and maintain the heart rate at a constant value in response to a physiological perturbation.

利用电流调节神经元以实现自主神经调节,需要同时具备上调和下调神经系统的能力。采用这种神经电调控的植入系统还需要实时适应自律神经状态的变化。以 1-30 赫兹的频率刺激自律神经是一种行之有效的增加神经活动的技术。迷走神经刺激(VNS)已被证明对频率调整很敏感,与振幅调制相比,频率调整可用于更精确地控制效果。千赫兹频率交流电(KHFAC)是一种行之有效的阻断动作电位传导以减少神经活动的技术。此外,KHFAC 还可以通过简单的振幅调制进行可靠调制。虽然常用的闭环控制器种类繁多,但许多传统方法都不能很好地应对长时间的系统延迟或不连续性。模糊逻辑控制(FLC)是一种基于状态的控制器,它可以用语言描述系统的不连续性,然后将状态转换转化为连续的输出信号。在我们的研究中,迷走神经上放置了一个双极电极,并由模糊逻辑控制器控制,以提供刺激和 KHFAC 来控制心率。模糊逻辑控制器既能将心率变为选定值,又能在生理扰动时将心率保持在恒定值。
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引用次数: 0
System Identification and Hybrid Model Predictive Control in Personalized mHealth Interventions for Physical Activity. 身体活动个性化mHealth干预中的系统识别和混合模型预测控制。
Pub Date : 2023-05-01 Epub Date: 2023-07-03 DOI: 10.23919/acc55779.2023.10156652
Mohamed El Mistiri, Owais Khan, Daniel E Rivera, Eric Hekler

The application of control systems principles in behavioral medicine includes developing interventions that can be individualized to promote healthy behaviors, such as sustained engagement in adequate levels of physical activity (PA). This paper presents the use of system identification and control engineering methods in the design of behavioral interventions through the novel formalism of a control-optimization trial (COT). The multiple stages of a COT, from experimental design in system identification through controller implementation, are illustrated using participant data from Just Walk, an intervention to promote walking behavior in sedentary adults. ARX models for individual participants are estimated using multiple estimation and validation data combinations, with the model leading to the best performance over a weighted norm being selected. This model serves as the internal model in a hybrid MPC controller formulated with three degree-of-freedom (3DoF) tuning that properly balances the requirements of physical activity interventions. Its performance in a realistic closed-loop setting is evaluated via simulation. These results serve as proof of concept for the COT approach, which is currently being evaluated with human participants in the clinical trial YourMove.

控制系统原理在行为医学中的应用包括开发可以个性化的干预措施,以促进健康行为,例如持续参与适当水平的体育活动(PA)。本文通过控制优化试验(COT)的新形式,介绍了系统识别和控制工程方法在行为干预设计中的应用。COT的多个阶段,从系统识别的实验设计到控制器的实现,都是使用Just Walk的参与者数据来说明的,这是一种促进久坐成年人步行行为的干预措施。使用多个估计和验证数据组合来估计单个参与者的ARX模型,并选择在加权范数上产生最佳性能的模型。该模型用作混合MPC控制器中的内部模型,该控制器采用三自由度(3DoF)调节来适当平衡身体活动干预的要求。通过仿真评估其在现实闭环设置中的性能。这些结果证明了COT方法的概念,该方法目前正在YourMove临床试验的人类参与者中进行评估。
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引用次数: 0
Reinforcement Learning Data-Acquiring for Causal Inference of Regulatory Networks. 监管网络因果推断的强化学习数据获取。
Pub Date : 2023-05-01 Epub Date: 2023-07-03 DOI: 10.23919/acc55779.2023.10155867
Mohammad Alali, Mahdi Imani

Gene regulatory networks (GRNs) consist of multiple interacting genes whose activities govern various cellular processes. The limitations in genomics data and the complexity of the interactions between components often pose huge uncertainties in the models of these biological systems. Meanwhile, inferring/estimating the interactions between components of the GRNs using data acquired from the normal condition of these biological systems is a challenging or, in some cases, an impossible task. Perturbation is a well-known genomics approach that aims to excite targeted components to gather useful data from these systems. This paper models GRNs using the Boolean network with perturbation, where the network uncertainty appears in terms of unknown interactions between genes. Unlike the existing heuristics and greedy data-acquiring methods, this paper provides an optimal Bayesian formulation of the data-acquiring process in the reinforcement learning context, where the actions are perturbations, and the reward measures step-wise improvement in the inference accuracy. We develop a semi-gradient reinforcement learning method with function approximation for learning near-optimal data-acquiring policy. The obtained policy yields near-exact Bayesian optimality with respect to the entire uncertainty in the regulatory network model, and allows learning the policy offline through planning. We demonstrate the performance of the proposed framework using the well-known p53-Mdm2 negative feedback loop gene regulatory network.

基因调控网络(GRNs)由多个相互作用的基因组成,这些基因的活性控制着各种细胞过程。基因组学数据的局限性和成分之间相互作用的复杂性往往会给这些生物系统的模型带来巨大的不确定性。同时,使用从这些生物系统的正常条件下获得的数据推断/估计GRN的成分之间的相互作用是一项具有挑战性的任务,在某些情况下,这是一项不可能完成的任务。扰动是一种众所周知的基因组学方法,旨在激发靶向成分从这些系统中收集有用的数据。本文使用带扰动的布尔网络对GRN进行建模,其中网络的不确定性表现为基因之间的未知相互作用。与现有的启发式和贪婪数据获取方法不同,本文提供了在强化学习环境中数据获取过程的最优贝叶斯公式,其中动作是扰动,奖励措施逐步提高推理精度。我们开发了一种具有函数近似的半梯度强化学习方法来学习接近最优的数据获取策略。对于监管网络模型中的整个不确定性,所获得的策略产生了接近精确的贝叶斯最优,并允许通过规划离线学习策略。我们使用众所周知的p53-Mdm2负反馈环基因调控网络证明了所提出的框架的性能。
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引用次数: 2
Idiographic Dynamic Modeling for Behavioral Interventions with Mixed Data Partitioning and Discrete Simultaneous Perturbation Stochastic Approximation. 具有混合数据划分和离散同时扰动随机近似的行为干预的特征动态建模。
Pub Date : 2023-05-01 Epub Date: 2023-07-03 DOI: 10.23919/acc55779.2023.10156304
Rachael T Kha, Daniel E Rivera, Predrag Klasnja, Eric Hekler

This paper presents the use of discrete simultaneous perturbation stochastic approximation (DSPSA) as a routine method to efficiently determine features and parameters of idiographic (i.e. single subject) dynamic models for personalized behavioral interventions using various partitions of estimation and validation data. DSPSA is demonstrated as a valuable method to search over model features and regressor orders of AutoRegressive with eXogenous input estimated models using participant data from Just Walk (a behavioral intervention to promote physical activity in sedentary adults); results of DSPSA are compared to those of exhaustive search. In Just Walk, DSPSA efficiently and quickly estimates models of walking behavior, which can then be used to develop control systems to optimize the impacts of behavioral interventions. The use of DSPSA to evaluate models using various partitions of individual data into estimation and validation data sets also highlights data partitioning as an important feature of idiographic modeling that should be carefully considered.

本文介绍了使用离散同时扰动随机近似(DSPSA)作为一种常规方法,使用各种估计和验证数据分区,有效地确定个性化行为干预的具体(即单受试者)动态模型的特征和参数。DSPSA被证明是一种有价值的方法,可以使用Just Walk(一种促进久坐成年人体育活动的行为干预)的参与者数据来搜索具有原始输入估计模型的自回归的模型特征和回归阶数;将DSPSA的结果与穷举搜索的结果进行了比较。在Just Walk中,DSPSA高效快速地估计步行行为的模型,然后可以用于开发控制系统,以优化行为干预的影响。使用DSPSA评估模型,将单个数据划分为估计和验证数据集,也突出了数据划分是具体建模的一个重要特征,应仔细考虑。
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引用次数: 0
Integral Quadratic Constraints with Infinite-Dimensional Channels. 具有无限维通道的积分二次约束。
Pub Date : 2023-05-01 Epub Date: 2023-07-03 DOI: 10.23919/acc55779.2023.10156335
Aleksandr Talitckii, Matthew M Peet, Peter Seiler

Modern control theory provides us with a spectrum of methods for studying the interconnection of dynamic systems using input-output properties of the interconnected subsystems. Perhaps the most advanced framework for such input-output analysis is the use of Integral Quadratic Constraints (IQCs), which considers the interconnection of a nominal linear system with an unmodelled nonlinear or uncertain subsystem with known input-output properties. Although these methods are widely used for Ordinary Differential Equations (ODEs), there have been fewer attempts to extend IQCs to infinite-dimensional systems. In this paper, we present an IQC-based framework for Partial Differential Equations (PDEs) and Delay Differential Equations (DDEs). First, we introduce infinite-dimensional signal spaces, operators, and feedback interconnections. Next, in the main result, we propose a formulation of hard IQC-based input-output stability conditions, allowing for infinite-dimensional multipliers. We then show how to test hard IQC conditions with infinite-dimensional multipliers on a nominal linear PDE or DDE system via the Partial Integral Equation (PIE) state-space representation using a sufficient version of the Kalman-Yakubovich-Popov lemma (KYP). The results are then illustrated using four example problems with uncertainty and nonlinearity.

现代控制理论为我们提供了一系列利用互连子系统的输入输出特性研究动态系统互连的方法。也许这种输入输出分析最先进的框架是使用积分二次约束(IQCs),它考虑了标称线性系统与具有已知输入输出特性的未建模非线性或不确定子系统的互连。尽管这些方法被广泛用于常微分方程(ODEs),但将IQC扩展到无限维系统的尝试较少。在本文中,我们提出了一个基于IQC的偏微分方程(PDE)和延迟微分方程(DDE)框架。首先,我们介绍了无限维信号空间、算子和反馈互连。接下来,在主要结果中,我们提出了一个基于硬IQC的输入输出稳定条件的公式,允许无限维乘法器。然后,我们展示了如何使用Kalman Yakubovich-Popov引理(KYP)的充分版本,通过偏积分方程(PIE)状态空间表示,在标称线性PDE或DDE系统上测试具有无限维乘法器的硬IQC条件。然后使用四个具有不确定性和非线性的示例问题来说明结果。
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引用次数: 0
Estimation of Locomotive Adhesion Coefficients and Slip Ratios 机车附着系数和滑移率的估计
Pub Date : 2023-01-01 DOI: 10.23919/ACC55779.2023.10155953
C. V. V. D. Merwe, J. D. L. Roux
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引用次数: 0
Optimal Energy Shaping Control for a Backdrivable Hip Exoskeleton. 可反向驱动髋关节外骨骼的最佳能量成形控制。
Pub Date : 2023-01-01 Epub Date: 2023-07-03 DOI: 10.23919/acc55779.2023.10155839
Jiefu Zhang, Jianping Lin, Vamsi Peddinti, Robert D Gregg

Task-dependent controllers widely used in exoskeletons track predefined trajectories, which overly constrain the volitional motion of individuals with remnant voluntary mobility. Energy shaping, on the other hand, provides task-invariant assistance by altering the human body's dynamic characteristics in the closed loop. While human-exoskeleton systems are often modeled using Euler-Lagrange equations, in our previous work we modeled the system as a port-controlled-Hamiltonian system, and a task-invariant controller was designed for a knee-ankle exoskeleton using interconnection-damping assignment passivity-based control. In this paper, we extend this framework to design a controller for a backdrivable hip exoskeleton to assist multiple tasks. A set of basis functions that contains information of kinematics is selected and corresponding coefficients are optimized, which allows the controller to provide torque that fits normative human torque for different activities of daily life. Human-subject experiments with two able-bodied subjects demonstrated the controller's capability to reduce muscle effort across different tasks.

外骨骼中广泛使用的任务相关控制器跟踪预先定义的轨迹,这些轨迹过度约束了具有剩余自愿移动性的个体的意志运动。另一方面,能量塑造通过在闭环中改变人体的动态特性来提供任务不变的帮助。虽然人类外骨骼系统通常使用欧拉-拉格朗日方程建模,但在我们之前的工作中,我们将该系统建模为端口控制的哈密顿系统,并使用基于互连阻尼分配无源性的控制为膝踝外骨骼设计了任务不变控制器。在本文中,我们扩展了这个框架,为可向后驱动的髋关节外骨骼设计了一个控制器,以辅助多个任务。选择一组包含运动学信息的基函数,并优化相应的系数,这允许控制器为日常生活的不同活动提供符合标准人类扭矩的扭矩。对两名身体健全的受试者进行的人体受试者实验证明了控制器在不同任务中减少肌肉力量的能力。
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引用次数: 0
Model Predictive Control Strategies for Optimized mHealth Interventions for Physical Activity. 优化移动医疗体育锻炼干预的模型预测控制策略。
Pub Date : 2022-06-01 Epub Date: 2022-09-05 DOI: 10.23919/acc53348.2022.9867350
Mohamed El Mistiri, Daniel E Rivera, Predrag Klasnja, Junghwan Park, Eric Hekler

Many individuals fail to engage in sufficient physical activity (PA), despite its well-known health benefits. This paper examines Model Predictive Control (MPC) as a means to deliver optimized, personalized behavioral interventions to improve PA, as reflected by the number of steps walked per day. Using a health behavior fluid analogy model representing Social Cognitive Theory, a series of diverse strategies are evaluated in simulated scenarios that provide insights into the most effective means for implementing MPC in PA behavioral interventions. The interplay of measurement, information, and decision is explored, with the results illustrating MPC's potential to deliver feasible, personalized, and user-friendly behavioral interventions, even under circumstances involving limited measurements. Our analysis demonstrates the effectiveness of sensibly formulated constrained MPC controllers for optimizing PA interventions, which is a preliminary though essential step to experimental evaluation of constrained MPC control strategies under real-life conditions.

尽管体力活动(PA)对健康的益处众所周知,但许多人却没有参加足够的体力活动。本文研究了模型预测控制 (MPC),将其作为提供优化的个性化行为干预的一种手段,以改善每天步行步数所反映的体育锻炼。利用代表社会认知理论的健康行为流体类比模型,在模拟场景中对一系列不同策略进行了评估,从而深入了解在 PA 行为干预中实施 MPC 的最有效方法。我们探讨了测量、信息和决策之间的相互作用,结果表明 MPC 具有提供可行、个性化和用户友好型行为干预的潜力,即使在测量有限的情况下也是如此。我们的分析表明了合理制定的受限 MPC 控制器在优化 PA 干预方面的有效性,这是在现实生活条件下对受限 MPC 控制策略进行实验评估的一个初步但必不可少的步骤。
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引用次数: 0
Enhanced Social Cognitive Theory Dynamic Modeling and Simulation Towards Improving the Estimation of "Just-In-Time" States. 改进社会认知理论动态建模与仿真以改进“及时”状态的估计。
Pub Date : 2022-06-01 DOI: 10.23919/acc53348.2022.9867493
Mohamed El Mistiri, Daniel E Rivera, Predrag Klasnja, Junghwan Park, Eric Hekler

Insufficient physical activity (PA) is commonplace in society, in spite of its significant impact on personal health and well-being. Improved interventions are clearly needed. One of the challenges faced in behavioral interventions is a lack of understanding of multi-timescale dynamics. In this paper we rely on a dynamical model of Social Cognitive Theory (SCT) to gain insights regarding a control-oriented experimental design for a behavioral intervention to improve PA. The intervention (Just Walk JITAI) is designed with the aim to better understand and estimate ideal times for intervention and support based on the concept of "just-in-time" states. An innovative input signal design strategy is used to study the just-in-time state dynamics through the use of decision rules based on conditions of need, opportunity and receptivity. Model simulations featuring within-day effects are used to assess input signal effectiveness. Scenarios for adherent and non-adherent participants are presented, with the proposed experimental design showing significant potential for reducing notification burden while providing informative data to support future system identification and control design efforts.

身体活动不足(PA)在社会上很普遍,尽管它对个人健康和福祉有重大影响。显然需要改进干预措施。行为干预面临的挑战之一是缺乏对多时间尺度动力学的理解。在本文中,我们依靠社会认知理论(SCT)的动态模型来获得关于行为干预改善PA的面向控制的实验设计的见解。干预(Just Walk JITAI)的设计目的是基于“及时”状态的概念,更好地理解和估计干预和支持的理想时间。采用一种创新的输入信号设计策略,通过基于需求、机会和可接受性条件的决策规则来研究实时状态动力学。模型模拟具有日内效应,用于评估输入信号的有效性。提出了依从性和非依从性参与者的场景,所提出的实验设计显示了减少通知负担的巨大潜力,同时提供了信息数据,以支持未来的系统识别和控制设计工作。
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引用次数: 1
Model Personalization in Behavioral Interventions using Model-on-Demand Estimation and Discrete Simultaneous Perturbation Stochastic Approximation. 基于模型按需估计和离散同步摄动随机逼近的行为干预模型个性化。
Pub Date : 2022-06-01 DOI: 10.23919/acc53348.2022.9867669
Rachael T Kha, Daniel E Rivera, Predrag Klasnja, Eric Hekler

This paper presents the use of discrete Simultaneous Perturbation Stochastic Approximation (DSPSA) to optimize dynamical models meaningful for personalized interventions in behavioral medicine, with emphasis on physical activity. DSPSA is used to determine an optimal set of model features and parameter values which would otherwise be chosen either through exhaustive search or be specified a priori. The modeling technique examined in this study is Model-on-Demand (MoD) estimation, which synergistically manages local and global modeling, and represents an appealing alternative to traditional approaches such as ARX estimation. The combination of DSPSA and MoD in behavioral medicine can provide individualized models for participant-specific interventions. MoD estimation, enhanced with a DSPSA search, can be formulated to provide not only better explanatory information about a participant's physical behavior but also predictive power, providing greater insight into environmental and mental states that may be most conducive for participants to benefit from the actions of the intervention. A case study from data collected from a representative participant of the Just Walk intervention is presented in support of these conclusions.

本文介绍了使用离散同步摄动随机逼近(DSPSA)来优化行为医学中个性化干预的动力学模型,重点是身体活动。DSPSA用于确定一组最优的模型特征和参数值,否则这些特征和参数值将通过穷穷搜索或先验指定来选择。本研究中检验的建模技术是模型-按需(MoD)估计,它协同管理局部和全局建模,代表了传统方法(如ARX估计)的一种有吸引力的替代方法。DSPSA和MoD在行为医学中的结合可以为参与者的干预提供个性化的模型。通过DSPSA搜索增强的MoD估计不仅可以更好地解释参与者的身体行为,还可以提供预测能力,更深入地了解环境和精神状态,这可能是最有利于参与者从干预行动中受益的。本研究从“Just Walk”干预的一位有代表性的参与者那里收集了数据,并提出了一个案例研究来支持这些结论。
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引用次数: 3
期刊
Proceedings of the ... American Control Conference. American Control Conference
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