Exploration of Low-Power Flexible Stress Monitoring Classifiers for Conformal Wearables

• URL: http://arxiv.org/abs/2508.19661v1
• Date: Wed, 27 Aug 2025 08:19:52 GMT
• Title: Exploration of Low-Power Flexible Stress Monitoring Classifiers for Conformal Wearables
• Abstract summary: State-of-the-art approaches use rigid, silicon-based wearables, which are not optimized for lightweight, flexible wear.<n>FE offer flexibility and low manufacturing costs, enabling real-time stress monitoring circuits.<n>This work presents the first comprehensive design space exploration of low-power, flexible stress classifiers.
• Score: 3.195176634942016
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Conventional stress monitoring relies on episodic, symptom-focused interventions, missing the need for continuous, accessible, and cost-efficient solutions. State-of-the-art approaches use rigid, silicon-based wearables, which, though capable of multitasking, are not optimized for lightweight, flexible wear, limiting their practicality for continuous monitoring. In contrast, flexible electronics (FE) offer flexibility and low manufacturing costs, enabling real-time stress monitoring circuits. However, implementing complex circuits like machine learning (ML) classifiers in FE is challenging due to integration and power constraints. Previous research has explored flexible biosensors and ADCs, but classifier design for stress detection remains underexplored. This work presents the first comprehensive design space exploration of low-power, flexible stress classifiers. We cover various ML classifiers, feature selection, and neural simplification algorithms, with over 1200 flexible classifiers. To optimize hardware efficiency, fully customized circuits with low-precision arithmetic are designed in each case. Our exploration provides insights into designing real-time stress classifiers that offer higher accuracy than current methods, while being low-cost, conformable, and ensuring low power and compact size.

Related papers

• Gated Condition Injection without Multimodal Attention: Towards Controllable Linear-Attention Transformers [69.60693631859411]
  This paper explores controllable diffusion models built upon linear attention architectures.<n>We propose a novel controllable diffusion framework tailored for linear attention backbones like SANA.<n>Our approach achieves state-of-the-art controllable generation performance based on linear-attention models.
  arXiv  Detail & Related papers  (2026-03-29T12:31:34Z)
• Sample-Efficient Neurosymbolic Deep Reinforcement Learning [49.60927398960061]
  We propose a neuro-symbolic Deep RL approach that integrates background symbolic knowledge to improve sample efficiency.<n>Online reasoning is performed to guide the training process through two mechanisms.<n>We show improved performance over a state-of-the-art reward machine baseline.
  arXiv  Detail & Related papers  (2026-01-06T09:28:53Z)
• FAIM: Frequency-Aware Interactive Mamba for Time Series Classification [87.84511960413715]
  Time series classification (TSC) is crucial in numerous real-world applications, such as environmental monitoring, medical diagnosis, and posture recognition.<n>We propose FAIM, a lightweight Frequency-Aware Interactive Mamba model.<n>We show that FAIM consistently outperforms existing state-of-the-art (SOTA) methods, achieving a superior trade-off between accuracy and efficiency.
  arXiv  Detail & Related papers  (2025-11-26T08:36:33Z)
• Bespoke Co-processor for Energy-Efficient Health Monitoring on RISC-V-based Flexible Wearables [2.569738171261988]
  We present a mechanically flexible RISC-V that integrates a bespoke multiply-accumulate co-processor with fixed coefficients to maximize energy efficiency and minimize latency.<n>Results demonstrate near-real-time performance across several healthcare datasets, with our circuits operating within the power budget of existing flexible batteries.
  arXiv  Detail & Related papers  (2025-11-08T12:17:34Z)
• Invited Paper: Feature-to-Classifier Co-Design for Mixed-Signal Smart Flexible Wearables for Healthcare at the Extreme Edge [2.2604925169016923]
  We present a holistic mixed-signal feature-to-classifier co-design framework for flexible smart wearable systems.<n>To the best of our knowledge, we design the first analog feature extractors in FE, significantly reducing feature extraction cost.<n>We propose a hardware-aware NAS-inspired feature selection strategy within ML training, enabling efficient, application-specific designs.
  arXiv  Detail & Related papers  (2025-08-27T07:26:11Z)
• MuFlex: A Scalable, Physics-based Platform for Multi-Building Flexibility Analysis and Coordination [17.55434238676]
  MuFlex is a scalable, open-source platform for benchmarking and testing control strategies for multi-building flexibility coordination.<n>The platform capabilities were demonstrated in a case study coordinating demand flexibility across four office buildings.
  arXiv  Detail & Related papers  (2025-08-19T05:44:06Z)
• Stress Monitoring in Healthcare: An Ensemble Machine Learning Framework Using Wearable Sensor Data [0.0]
  This study introduces a dataset comprising physiological signals, electrodermal activity, heart rate and skin temperature.<n>To overcome these challenges, the dataset underwent preprocessing with the Synthetic Minority Over sampling Technique (SMOTE)<n>Advanced machine learning models including Random Forest, XGBoost and a Multi-Layer Perceptron (MLP) were evaluated and combined into a Stacking to leverage their collective predictive strengths.
  arXiv  Detail & Related papers  (2025-07-10T09:47:56Z)
• Code-as-Monitor: Constraint-aware Visual Programming for Reactive and Proactive Robotic Failure Detection [56.66677293607114]
  We propose Code-as-Monitor (CaM) for both open-set reactive and proactive failure detection.<n>To enhance the accuracy and efficiency of monitoring, we introduce constraint elements that abstract constraint-related entities.<n>Experiments show that CaM achieves a 28.7% higher success rate and reduces execution time by 31.8% under severe disturbances.
  arXiv  Detail & Related papers  (2024-12-05T18:58:27Z)
• Provably Transformers Harness Multi-Concept Word Semantics for Efficient In-Context Learning [53.685764040547625]
  Transformer-based large language models (LLMs) have displayed remarkable creative prowess and emergence capabilities.<n>This work provides a fine mathematical analysis to show how transformers leverage the multi-concept semantics of words to enable powerful ICL and excellent out-of-distribution ICL abilities.
  arXiv  Detail & Related papers  (2024-11-04T15:54:32Z)
• Bespoke Approximation of Multiplication-Accumulation and Activation Targeting Printed Multilayer Perceptrons [0.8274768545559366]
  Printed Electronics (PE) offer unparalleled features such as non-recurring engineering costs, ultra-low manufacturing costs, and on-demand fabrication. PE face certain limitations due to their large feature sizes, that impede the realization of complex circuits. We propose an automated framework for designing ultra-low power Multilayer Perceptron (MLP) classifiers.
  arXiv  Detail & Related papers  (2023-12-29T14:16:11Z)
• Revisiting GANs by Best-Response Constraint: Perspective, Methodology, and Application [49.66088514485446]
  Best-Response Constraint (BRC) is a general learning framework to explicitly formulate the potential dependency of the generator on the discriminator. We show that even with different motivations and formulations, a variety of existing GANs ALL can be uniformly improved by our flexible BRC methodology.
  arXiv  Detail & Related papers  (2022-05-20T12:42:41Z)
• Toward Fast, Flexible, and Robust Low-Light Image Enhancement [87.27326390675155]
  We develop a new Self-Calibrated Illumination (SCI) learning framework for fast, flexible, and robust brightening images in real-world low-light scenarios. Considering the computational burden of the cascaded pattern, we construct the self-calibrated module which realizes the convergence between results of each stage. We make comprehensive explorations to SCI's inherent properties including operation-insensitive adaptability and model-irrelevant generality.
  arXiv  Detail & Related papers  (2022-04-21T14:40:32Z)
• Constrained multi-objective optimization of process design parameters in settings with scarce data: an application to adhesive bonding [48.7576911714538]
  Finding the optimal process parameters for an adhesive bonding process is challenging. Traditional evolutionary approaches (such as genetic algorithms) are then ill-suited to solve the problem. In this research, we successfully applied specific machine learning techniques to emulate the objective and constraint functions.
  arXiv  Detail & Related papers  (2021-12-16T10:14:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.