Coughing is more than just a nuisance; it serves as a vital indicator of health. Monitoring cough frequency can provide early warnings about the exacerbation of respiratory diseases and help in timely interventions, such as the use of inhalers in asthma patients. Edgar Lobaton, a professor of electrical and computer engineering and the lead author of the study, emphasizes cough detection’s potential to transform patient care by offering real-time health insights through continuous monitoring using wearable technology.

Wearable health devices capture data in real-world environments that are complex and noisy, posing a significant challenge for cough-detection algorithms. While prior machine learning models have been trained to identify cough sounds, they often falter when confronted with everyday noises that mimic coughing, such as sneezing, throat clearing, or even the sounds of speech. This limitation largely stems from models encountering unfamiliar sounds — sounds they were not exposed to during initial training phases.

To overcome this, Lobaton and his team devised an innovative approach involving multimodal data inputs collected from chest-worn wearable monitors. These devices gather not only audio signals but also accelerometer data that registers subtle chest movements associated with coughing. The synergy of sound data with motion data offers a richer, more nuanced understanding of cough events, as movement patterns serve as corroborative evidence supporting the acoustic signals.

While movement tracking alone is insufficient due to overlaps with non-cough actions like laughing or groaning, combining it with audio input significantly sharpens detection accuracy. Yuhan Chen, the study’s first author, elaborates that this integrated data fusion approach empowers the model to distinguish cough events with higher confidence. It reduces false alarms—instances where the device mistakenly identifies non-cough sounds as coughs—thus improving reliability.

Building upon previous machine learning advancements, the researchers refined their algorithms to optimize what they term “out-of-distribution detection.” This refers to the model’s enhanced ability to recognize when it encounters unfamiliar sounds and adjust its confidence level accordingly, reducing erroneous cough classifications. Their approach marks a pivotal advancement in wearable biosensing, allowing devices to better generalize across the diverse acoustic environments they experience in daily life.

When subjected to rigorous laboratory testing, the new multimodal cough detection model outperformed existing technologies with a measurable reduction in false positives. This improvement indicates the model’s proficiency in accurately filtering out speech and other nonverbal sounds that historically plagued cough-detection efforts. Such robustness is essential for clinical applications where high specificity and sensitivity directly impact patient health management decisions.

This enhanced cough detection capability opens up transformative possibilities for continuous health monitoring. Wearable devices equipped with these refined models can more effectively track the progression of respiratory diseases, aiding both patients and healthcare providers in managing conditions proactively. The technology’s potential extends to predicting acute health events, such as asthma attacks, enabling timely interventions that could prevent hospitalizations.

The team’s work dovetails with broader efforts to integrate artificial intelligence into personalized medicine, using real-time sensory data to paint a clearer picture of patient health. As wearable health technologies mature, innovations like this multimodal model will be foundational in delivering actionable health insights outside clinical settings, empowering individuals to take charge of their respiratory health.

Despite this progress, the researchers acknowledge ongoing challenges. Future work aims to enhance detection capabilities further, particularly in distinguishing coughs amidst even more variable sounds and environments. There is a clear trajectory toward refining sensor technology, algorithm robustness, and real-world deployment, making cough detection a dependable tool in the health-monitoring arsenal.

This research was supported by the National Science Foundation and NC State’s Center for Advanced Self-Powered Systems of Integrated Sensors and Technologies (ASSIST), underscoring the critical role of interdisciplinary collaboration in advancing wearable biosensor technologies. The study titled “Robust Multimodal Cough Detection with Optimized Out-of-Distribution Detection for Wearables” was published in the IEEE Journal of Biomedical and Health Informatics, offering a landmark reference point for future developments in this area.

In conclusion, the integration of multimodal data from wearable devices marks a vital advancement in cough detection technology. By harnessing both audio and motion inputs and optimizing algorithmic responses to new sound environments, this research paves the way for more reliable, real-world health monitoring solutions that could revolutionize respiratory disease management and chronic care.

Subject of Research: People
Article Title: Robust Multimodal Cough Detection with Optimized Out-of-Distribution Detection for Wearables
News Publication Date: 2-Oct-2025
Web References: IEEE Journal of Biomedical and Health Informatics DOI
Image Credits: Edgar Lobaton, NC State University
Keywords: cough detection, wearable devices, respiratory health, machine learning, multimodal data, accelerometer, audio processing, chronic disease monitoring, asthma prediction, biosensors, artificial intelligence, health technology

Aging, a universal phenomenon, presents complex physiological, neurological, and biomechanical changes that significantly impact quality of life. This collection delves into multidisciplinary approaches combining engineering ingenuity with clinical insights, aiming to develop sophisticated diagnostic, monitoring, and therapeutic tools. The cross-pollination of ideas from experts in robotics, sensor technologies, data analytics, and bioinformatics is palpable throughout this compendium, illuminating the direction biomedical engineering is taking to meet the needs of an aging society.

One of the core themes emphasized in the collection is the integration of wearable technologies and smart devices designed to continuously monitor vital health parameters while facilitating proactive interventions. These technologies leverage advancements in miniaturized sensors, wireless communication, and real-time data processing. Such systems provide unprecedented opportunities for personalized rehabilitation protocols, which dynamically adjust to patients’ changing conditions without requiring constant clinical supervision.

In-depth explorations into robotic-assisted rehabilitation further underpin this collection’s relevance. Innovative robotic exoskeletons and assistive devices have been refined to enhance mobility and functional independence among older adults suffering from stroke, Parkinson’s disease, or musculoskeletal disorders. The research highlights how adaptable control algorithms and human–machine interfaces contribute to intuitive device operation, thereby optimizing patient engagement and therapeutic outcomes.

Another focal point is the employment of artificial intelligence and machine learning techniques to predict health trajectories and tailor individualized treatment regimens. By harnessing large datasets collected from clinical and home environments, researchers are developing predictive models that can identify early signs of functional decline or adverse events. This data-driven approach not only enhances rehabilitation efficacy but also mitigates healthcare costs by preventing hospital readmissions and complications.

The collection also pays significant attention to novel materials and bioengineering strategies that promote tissue regeneration and repair. Advances in biomaterials, such as 3D-printed scaffolds and biocompatible hydrogels, are demonstrated to support cell growth and functional recovery in deteriorated tissues. These developments open new horizons for regenerative medicine applications within the context of aging, particularly for joint and muscle repair.

Importantly, the intersection of cognitive rehabilitation and engineering innovation is well represented. Research articles explore neural interface devices and brain-computer interaction systems developed to support cognitive functions in individuals experiencing dementia or mild cognitive impairment. These technologies enable non-invasive stimulation and monitoring of neural activity, providing valuable avenues for maintaining mental acuity and improving life quality.

Beyond technological innovation, the collection underscores the ethical, societal, and healthcare delivery considerations intrinsic to deploying these novel solutions. Discussions revolve around accessibility, user-centered design, and interoperability with existing healthcare infrastructures to ensure that advancements translate into tangible benefits for diverse populations, including underserved communities.

The assembled works also highlight the role of interdisciplinary collaboration, bringing together engineers, clinicians, behavioral scientists, and policymakers to create holistic strategies addressing aging and rehabilitation. This cooperative spirit is essential for overcoming barriers related to technology adoption, reimbursement, and patient adherence, reflecting a comprehensive vision for the future of geriatric care.

The importance of real-world validation and large-scale clinical trials is another critical aspect addressed within the collection. Emphasis is placed on rigorous testing of devices and algorithms in diverse settings to establish efficacy, safety, and user acceptance. This pragmatic approach bridges the gap between laboratory innovation and everyday clinical practice, fostering regulatory approvals and market readiness.

Data security and privacy considerations, particularly in the context of personal health information collected through ubiquitous monitoring systems, receive thorough examination. The collection advocates for robust cybersecurity measures and transparent data governance frameworks that protect patient confidentiality while enabling data-driven scientific advancements.

As the global population ages, the burden of chronic diseases and functional impairments intensifies, challenging existing healthcare paradigms. The special collection associated with the 2024 International Conference on Aging, Innovation and Rehabilitation embodies a forward-looking ensemble of solutions designed to empower older adults with improved autonomy, enhanced quality of life, and reduced dependency.

The confluence of technological prowess and clinical expertise evident in this collection stands as a testament to the extraordinary potential of biomedical engineering to transform aging and rehabilitation. By fostering innovation grounded in real-world needs and ethical principles, this body of work charts a course toward sustainable, patient-centered healthcare tailored to the demographic realities of the 21st century.

In sum, the BioMedical Engineering OnLine special collection offers a comprehensive and inspiring overview of the state-of-the-art in aging and rehabilitation research. It not only celebrates the breakthroughs presented at the 2024 International Conference but also sets an ambitious agenda for future inquiry and development, heralding a new era in biomedical engineering dedicated to supporting healthy and dignified aging worldwide.

Subject of Research: Aging, Innovation, Rehabilitation, Biomedical Engineering

Article Title: Special collection in association with the 2024 International Conference on Aging, Innovation and Rehabilitation

Article References: Taati, B., Popovic, M. Special collection in association with the 2024 International Conference on Aging, Innovation and Rehabilitation. BioMed Eng OnLine 24, 89 (2025). https://doi.org/10.1186/s12938-025-01427-z

Image Credits: AI Generated

DOI: https://doi.org/10.1186/s12938-025-01427-z

The core of this technology lies in its intricate construction: 22 compact pressure sensors are strategically dispersed from the toe to the heel of the insole, capturing detailed plantar pressure distributions as users move through different activities. Unlike previous attempts at wearable gait monitoring devices, which often struggled with limited energy supplies and inconsistent data capture, this system leverages small solar panels placed atop the user’s footwear to harvest ambient light. The captured solar energy is then stored in diminutive lithium batteries, enabling uninterrupted power supply that sustains long-term, continuous monitoring without sacrificing safety or comfort.

One of the defining features of this smart insole lies in its real-time data transmission capabilities, facilitated by low-energy Bluetooth communications that seamlessly connect the device to smartphones. This enables instantaneous health tracking and sophisticated gait analyses that could alert users and healthcare providers to subtle changes in walking patterns associated with a spectrum of disorders—from biomechanical stresses such as plantar fasciitis to neurological ailments like Parkinson’s disease. The research team, led by assistant professor Jinghua Li and PhD candidate Qi Wang, has focused heavily on ensuring high spatial resolution and sensing accuracy, critical for capturing the intricacies of human gait dynamics.

The biomechanical essence of walking is a personalized and complex interplay of forces and timings, with pressure being sequentially applied from the heel through to the toes during ambulation. This temporal pattern dramatically shifts during running, wherein sensors simultaneously register elevated pressure with a notably shortened stance phase—the duration the foot remains in contact with the ground. By decoding such nuanced differences with advanced sensor fusion, the smart insole presents a profound leap in wearables capable of capturing authentic locomotion data rather than generalized motion.

Moreover, the integration of artificial intelligence through advanced machine learning algorithms allows the system not only to measure but classify eight distinct motion states, covering static postures such as sitting and standing, to dynamic movements including squatting and running. This AI-enabled recognition is a vital bridge towards personalized healthcare applications, as it facilitates real-time posture correction, injury prevention strategies, rehabilitation progress monitoring, and potentially highly customized fitness regimens tailored to an individual’s unique gait signature.

Material innovation is also central to the success of the device. Constructed from flexible and biocompatible materials, the insole maintains comfort and safety during prolonged usage. Remarkably, durability testing reveals that after enduring 180,000 compression-decompression cycles, the system sustains consistent performance without degradation. This resilience underscores the potential for everyday usage under the rigors of repeated foot strikes and continuous deformation, a critical benchmark that many earlier wearable attempts failed to meet.

Such an insoles’ ability to capture continuous plantar pressure maps paves the way for early detection of common and severe health conditions. For instance, diabetic foot ulcers, which arise from abnormal foot pressure distributions, could potentially be prevented through timely alerts derived from gait irregularities detected by the device. Similarly, musculoskeletal conditions like plantar fasciitis could be spotted before symptoms worsen, allowing therapeutic interventions to be implemented during early stages. The smart insole’s sensitivity to subtle neurological changes in gait patterns could further serve as an early biomarker for degenerative diseases such as Parkinson’s, where gait instability and postural control are paramount clinical features.

A particularly notable aspect is the self-powered nature of the insole system. Unlike many wearables reliant on frequent charging or bulky batteries, the innovative embedding of solar cells into footwear harnesses renewable energy seamlessly throughout the day. The ensuing ecological and practical benefits are significant—users can rely on a maintenance-light device that minimizes environmental impact while delivering continuous functionality. This green energy approach is an essential milestone towards sustainable wearable electronics.

While the current iteration already offers robust performance, the research team anticipates several future enhancements. Expanding the dataset to encompass diverse populations is a crucial next step, as individual variations in gait and lifestyle profoundly affect sensor calibration and AI predictive accuracy. By training the machine learning models on heterogeneous user groups, they aim to bolster generalizability and tailor the wearable’s algorithms to better serve global populations with varying biomechanics, fitness levels, and health statuses.

Looking forward, the system could extend beyond health monitoring. Its capability to differentiate a broad range of locomotor activities with high fidelity opens avenues in sports science, occupational health, and even interactive gaming environments where user motion input is essential. Calibration with other biometric sensors might yield integrated health ecosystems that holistically track physical activity, nutrition, and recovery, offering users deeper insights into their well-being through a single wearable platform.

Commercial availability is projected within a three- to five-year horizon, contingent on further development and real-world testing. The research team remains optimistic about the potential impact, envisioning the smart insole not just as a monitoring tool, but as an active companion encouraging healthier movement patterns and personalized self-care. As wearable devices continue to evolve towards pervasive healthcare applications, this smart insole represents a compelling fusion of materials science, renewable energy, and artificial intelligence that sets a new standard for gait monitoring innovations.

By transcending previous limitations in power autonomy, sensing resolution, and intelligent data analytics, this technology embodies a paradigm shift—smart footwear that can fundamentally redefine mobility management. With its promising versatility and reliability, the smart, solar-powered insole is poised to catalyze new frontiers in preventive medicine and rehabilitation, emblematic of how next-generation wearables can empower individuals through real-time, personalized health intelligence.

Subject of Research: Smart insole system for real-time gait monitoring and health diagnostics

Article Title: A Wireless, Self-Powered Smart Insole for Gait Monitoring and Recognition via Nonlinear Synergistic Pressure Sensing

News Publication Date: 16-Apr-2025

Web References: DOI: 10.1126/sciadv.adu1598

Keywords: Wearable devices, Machine learning, Public health, Health care, Solar energy, Solar power, Pressure sensors, Human locomotion

At the core of this technology lies an ingeniously designed enclosed chamber that rests immediately next to the skin surface but avoids direct physical coupling. This chamber captures the subtle fluxes of vaporized molecular substances that naturally diffuse in and out of the skin. These molecular streams—comprising water vapor, volatile organic compounds (VOCs), and carbon dioxide—play critical roles as biomarkers in physiological and pathological processes. By continuously assessing changes in the microclimate within this chamber, the device unlocks a wealth of information about the wearer’s health status and environmental exposures.

The system integrates a sophisticated collection of wireless sensors capable of quantifying the minute changes in molecular concentration and environmental parameters inside the chamber with impeccable precision. This sensor suite is enhanced by a programmable bistable valve mechanism, which orchestrates dynamic control over the chamber’s access to ambient air. By alternately opening and sealing off the chamber, the device induces a time-dependent transient response. Analysis of these sensor readings during controlled exposure and isolation phases enables the differentiation and quantification of inward and outward molecular fluxes, a feature highly valuable in both clinical diagnostics and environmental monitoring.

This non-contact operational mode introduces a transformative advantage in scenarios where maintaining an unbroken skin barrier is paramount. Conventional wound monitoring devices often require direct contact, bearing a risk of further damage to delicate or infected tissue. Here, the new wearable technology mitigates such concerns by relying on proximity without contact, thereby preserving tissue integrity and enabling more frequent and safer monitoring of wound healing progression.

The system’s ability to detect and measure fluxes of water vapor holds particular clinical relevance. Water vapor emanating from the skin correlates with hydration status, inflammation, and metabolic activity. In wound healing contexts, changes in water vapor flux can signify the transition between different healing phases or the onset of infection. Alongside this, the device’s sensitivity to volatile organic compounds enables the detection of biochemical signatures indicative of tissue necrosis, bacterial colonization, or metabolic imbalances, offering a non-invasive window into underlying physiological changes.

Carbon dioxide flux measurement adds yet another dimension to the monitoring capabilities of the wearable platform. Because elevated or diminished CO₂ emission rates relate closely to local metabolic rate and perfusion status, analyzing these fluxes allows researchers and clinicians to infer tissue viability and the efficiency of blood supply. This multifactorial sensing approach, combining water vapor, VOCs, and CO₂, therefore provides comprehensive, multiplexed data streams essential for nuanced clinical insight.

The validation studies conducted using models of healing dermal wounds in both healthy and diabetic mice have produced compelling data. Diabetic wounds notoriously exhibit delayed or aberrant healing dynamics, and the device successfully captured characteristic variations in molecular flux that distinguished these pathological conditions from normal healing trajectories. Moreover, the system demonstrated acute sensitivity to infection-induced shifts in molecular emissions, reinforcing its potential utility for early detection and intervention in wound management protocols.

An additional strength of this technology is its wireless operational framework. By integrating low-power, miniaturized sensors and communication modules, the device eliminates the need for tethered connections, drastically enhancing patient comfort and compliance. This wireless capability also facilitates real-time, remote monitoring, enabling healthcare providers to track wound healing progression or environmental exposures continuously without necessitating in-person visits.

This pioneering device platform represents a paradigm shift in wearable biosensing, driving a transition from invasive or contact-dependent modalities toward a sophisticated, non-contact interface that respects the fragility of human skin and wounds. Its modular design and programmability make it adaptable for a spectrum of applications beyond wound care, including athletic performance monitoring, environmental toxin exposure assessment, and potentially early detection of systemic illnesses through epidermal molecular profiling.

Future research may explore further miniaturization and integration with advanced data analytics powered by machine learning, enabling personalized health insights derived from the complex, time-varying molecular flux data. Moreover, expanding the range of detectable molecular species could unlock new diagnostic possibilities, enhancing the device’s clinical versatility. The innovation distinguishes itself by bridging the gap between fundamental physiological monitoring and practical, user-friendly wearable devices.

In summary, the development of this non-contact wearable device represents a significant leap forward in biosensing technology, offering unprecedented accuracy and safety in measuring epidermal molecular flux. Its multifunctional sensor suite and dynamic environmental control through a bistable valve provide a novel approach to capturing transient molecular signals that reflect physiological and pathological states. This technological leap stands to revolutionize patient monitoring, diagnostics, and personalized care methodologies, with broad implications across medicine, environmental health, and beyond.

As wearable technology continues to evolve, embracing such non-invasive and decoupled sensing strategies will be critical for overcoming existing limitations. By enabling continuous, precise, and non-disruptive monitoring, this platform paves the way toward a future where wearable devices become integral, seamless components of healthcare ecosystems. The research reflects an inspiring intersection of materials science, engineering, and biomedical applications, illustrating the powerful impact of interdisciplinary innovation.

This landmark study, detailed comprehensively in Nature, invites further exploration and refinement, while capturing imaginations with its clever use of physical decoupling to open a window into the body’s molecular landscape without ever touching it. The promise of such technology to transform how clinicians and researchers quantify and interpret epidermal molecular flux marks a new chapter in wearable biosensors and personalized medicine.

Subject of Research: Non-contact wearable device platforms for monitoring epidermal molecular flux.

Article Title: A non-contact wearable device for monitoring epidermal molecular flux.

Article References:
Shin, J., Song, J.W., Flavin, M.T. et al. A non-contact wearable device for monitoring epidermal molecular flux. Nature 640, 375–383 (2025). https://doi.org/10.1038/s41586-025-08825-2

Image Credits: AI Generated

DOI: https://doi.org/10.1038/s41586-025-08825-2