Abstract Precision healthcare relies upon ubiquitous biofeedback to optimize therapy individually for nuanced and dynamic needs. However, grand challenges reside in the lack of soft, highly personalizable monitors that are scalable in manufacturing and reversibly interchangeable upon the evolution of needs. Herein, a customizable soft wearable platform is presented that can seamlessly integrate diverse functional modules, including physical and biochemical sensors, stimulators, and energy storage devices, tailored to various health monitoring scenarios, while can self‐repair after certain mechanical damage. The platform supports versatile physiological sensing and therapeutic intervention due to its compatibility with wide‐ranging functional nanomaterials. A bilayer microporous foam embedded in the gel improves sweat management for comfortable and reliable on‐body biomarker monitoring. Furthermore, flexible self‐healing zinc‐air batteries using ion gel electrolytes provide opportunities for self‐powered, closed‐loop systems. On‐body demonstrations validate the platform's capability to monitor physiological and metabolic states under real‐world conditions. This work provides a scalable and adaptable materials‐based solution for real‐time personalized health monitoring, advancing wearable bioelectronics to meet evolving healthcare demands. 
                        more » 
                        « less   
                    
                            
                            M2G: A Monitor of Monitoring Systems with Ground Truth Validation Features for Research-Oriented Residential Applications
                        
                    
    
            Research in the area of internet-of-things, cyber physical- systems, and smart health often employ sensor systems at residences for continuous monitoring. Such research oriented residential monitoring systems (RRMSs) usually face two major challenges, long-term reliable operation management and validation of system functionality with minimal human effort. Targeting these two challenges, this paper describes a monitor of monitoring systems with ground-truth validation capabilities, M2G. It consists of two subsystems, the Monitor2 system and the Ground-truth validation system. The Monitor2 system encapsulates a flexible set of general-purpose components to monitor the operation and connectivity of heterogeneous sensor devices (e.g. smart watches, smart phones, microphones, beacons, etc.), a local base-station, as well as a cloud server. It provides a user-friendly interface and supports different types of RRMSs in various contexts. The system also features a ground truth validation system to support obtaining ground truth in the field. Additionally, customized alerts can be sent to remote administrators and other personnel to report any dysfunction or inaccuracy of the system in real time. M2G is applied to three very different case studies: the M2FED system which monitors family eating dynamics, an in-home wireless sensing system for monitoring nighttime agitation, and the BESI system which monitors behavioral and environmental parameters to predict health events and to provide interventions. The results indicate that M2G is a comprehensive system that (i) requires small cost in time and effort to adapt to an existing RRMS, (ii) provides reliable data collection and reduction in data loss by detecting faults in real-time, and (iii) provides a convenient and timely ground truth validation facility. 
        more » 
        « less   
        
    
                            - Award ID(s):
- 1521722
- PAR ID:
- 10059941
- Date Published:
- Journal Name:
- MASS
- ISSN:
- 0330-9231
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
- 
            
- 
            Abstract In droplet-on-demand liquid metal jetting (DoD-LMJ) additive manufacturing, complex physical interactions govern the droplet characteristics, such as size, velocity, and shape. These droplet characteristics, in turn, determine the functional quality of the printed parts. Hence, to ensure repeatable and reliable part quality it is necessary to monitor and control the droplet characteristics. Existing approaches for in-situ monitoring of droplet behavior in DoD-LMJ rely on high-speed imaging sensors. The resulting high volume of droplet images acquired is computationally demanding to analyze and hinders real-time control of the process. To overcome this challenge, the objective of this work is to use time series data acquired from an in-process millimeter-wave sensor for predicting the size, velocity, and shape characteristics of droplets in DoD-LMJ process. As opposed to high-speed imaging, this sensor produces data-efficient time series signatures that allows rapid, real-time process monitoring. We devise machine learning models that use the millimeter-wave sensor data to predict the droplet characteristics. Specifically, we developed multilayer perceptron-based non-linear autoregressive models to predict the size and velocity of droplets. Likewise, a supervised machine learning model was trained to classify the droplet shape using the frequency spectrum information contained in the millimeter-wave sensor signatures. High-speed imaging data served as ground truth for model training and validation. These models captured the droplet characteristics with a statistical fidelity exceeding 90%, and vastly outperformed conventional statistical modeling approaches. Thus, this work achieves a practically viable sensing approach for real-time quality monitoring of the DoD-LMJ process, in lieu of the existing data-intensive image-based techniques.more » « less
- 
            The increasing penetration of renewable energy resources in distribution systems necessitates high-speed monitoring and control of voltage for ensuring reliable system operation. However, existing voltage control algorithms often make simplifying assumptions in their formulation, such as real-time availability of smart meter measurements (for monitoring), or real-time knowledge of every power injection information (for control). This paper leverages the recent advances made in high-speed state estimation for real-time unobservable distribution systems to formulate a deep reinforcement learning (DRL)-based control algorithm that utilizes the state estimates alone to control the voltage of the entire system. The results obtained for a modified (renewable-rich) IEEE 34-node distribution feeder indicate that the proposed approach excels in monitoring and controlling voltage of active distribution systems.more » « less
- 
            Abstract Microfluidic‐based wearable electrochemical sensors represent a transformative approach to non‐invasive, real‐time health monitoring through continuous biochemical analysis of body fluids such as sweat, saliva, and interstitial fluid. These systems offer significant potential for personalized healthcare and disease management by enabling real‐time detection of key biomarkers. However, challenges remain in optimizing microfluidic channel design, ensuring consistent biofluid collection, balancing high‐resolution fabrication with scalability, integrating flexible biocompatible materials, and establishing standardized validation protocols. This review explores advancements in microfluidic design, fabrication techniques, and integrated electrochemical sensors that have improved sensitivity, selectivity, and durability. Conventional photolithography, 3D printing, and laser‐based fabrication methods are compared, highlighting their mechanisms, advantages, and trade‐offs in microfluidic channel production. The application section summarizes strategies to overcome variability in biofluid composition, sensor drift, and user adaptability through innovative solutions such as hybrid material integration, self‐powered systems, and AI‐assisted data analysis. By analyzing recent breakthroughs, this paper outlines critical pathways for expanding wearable sensor technologies and achieving seamless operation in diverse real‐world settings, paving the way for a new era of digital health.more » « less
- 
            Abstract Stress is one of the main causes that increase the risk of serious health problems. Recent wearable devices have been used to monitor stress levels via electrodermal activities on the skin. Although many biosensors provide adequate sensing performance, they still rely on uncomfortable, partially flexible systems with rigid electronics. These devices are mounted on either fingers or palms, which hinders a continuous signal monitoring. A fully‐integrated, stretchable, wireless skin‐conformal bioelectronic (referred to as “SKINTRONICS”) is introduced here that integrates soft, multi‐layered, nanomembrane sensors and electronics for continuous and portable stress monitoring in daily life. The all‐in‐one SKINTRONICS is ultrathin, highly soft, and lightweight, which overall offers an ergonomic and conformal lamination on the skin. Stretchable nanomembrane electrodes and a digital temperature sensor enable highly sensitive monitoring of galvanic skin response (GSR) and temperature. A set of comprehensive signal processing, computational modeling, and experimental study provides key aspects of device design, fabrication, and optimal placing location. Simultaneous comparison with two commercial stress monitors captures the enhanced performance of SKINTRONICS in long‐term wearability, minimal noise, and skin compatibility. In vivo demonstration of continuous stress monitoring in daily life reveals the unique capability of the soft device as a real‐world applicable stress monitor.more » « less
 An official website of the United States government
An official website of the United States government 
				
			 
					 
					
 
                                    