Kevin Albert Kasper

Continuously operating wearables offer detailed insight into chronic health conditions and have the potential to reshape diagnostic and screening tools. However, the energy demands and large datasets created by constant monitoring over weeks to months are difficult or impossible to integrate into existing clinical practice, limiting the utility of this device class. Machine learning offers the opportunity to condense these large datasets into streamlined, digestible trends with the potential for significant clinical impact, although off-device inference requires advanced network infrastructure and substantial power availability for radios. Here, we introduce a device framework that integrates artificial intelligence with clinical grade biosignal acquisition at the edge, performing on-device inference with clinical grade fidelity over extended durations with no interaction required by the wearer. We utilize this framework to perform gait-based frailty assessment during in vivo trials (N₁ = 16) with results that match gold standard diagnostic tools. Clinical utility, model stability, and on-device inference are validated through in vivo trials (N₂ = 14) and ten-day-long extended wear experiments, demonstrating continuous operation without wearer intervention and autonomous longitudinal analysis of high sampling rate biosignals.

Advances in chronic wearable biosignal acquisition are reviewed, emphasizing biointerfaces, wireless power, multimodal sensing, analytics, and challenges in epidermal turnover, accuracy, power and clinical translation.

Biophysical signals such as motion and optically acquired hemodynamics represent foundational sensing modalities for wearables. Expansion of this toolset is vital for the progression of digital medicine. Current efforts utilize biofluids such as sweat and interstitial fluid with primarily adhesively mounted sensors that are fundamentally limited by epidermal turnover. A class of potential biomarkers that is largely unexplored are gaseous emissions from the body. In this work, we introduce an approach to capture emission of gas from the skin with a leaky cavity designed to allow for diffusion-based ambient gas exchange with the environment. This approach, coupled with differential measurement of ambient and in-cavity gas concentrations, allows for the real-time analysis of sweat rate, VOCs, and CO₂ while performing everyday tasks. The resulting biosignals are recorded with temporal resolutions that exceed current methodology, providing unparalleled insight into physiological processes without requiring sensor replacement over weeks at a time.

Substantial hurdles in achieving a digitally connected body with seamless, chronic, high-fidelity organ interfaces include challenges of sourcing energy and ensuring reliable connectivity. Operation is currently limited by batteries that occupy large volumes. Wireless, battery-free operation is therefore paramount, requiring a system-level solution that enables seamless connection of wearable and implantable devices. Here, we present a technological framework that enables wireless, battery-free implant operation in freely moving subjects, with streaming of high-fidelity information from low-displacement, battery-free implants with little user interaction. This is accomplished using at-distance wirelessly recharged, wearable biosymbiotic devices for powering and communication with fully implantable NFC-enabled implants. We demonstrate this capability with osseosurface electronics that stream bone health insight. Eleven-month-long large animal studies highlight the ability of implants to relay information on bone health without negative impact on the subjects. Clinical translatability is shown through fracture healing studies that demonstrate biomarkers of bone union.

Continuous monitoring of electrophysiological biosignals such as electrocardiogram (ECG) and bioimpedance (BioZ) rely on gel‐based electrodes and adhesive skin interfaces requiring active patient interaction inherently restricting chronic use. Current solutions aimed at addressing seamless, comfortable, and reliable recordings with dry electrodes use battery power and are limited to days of operation with clinical‐grade fidelity. Here, current limitations are overcome by uniting at‐distance wirelessly powered wearable electronics with carbon‐doped filament deposition modeling (FDM) printed dry electrodes that overcome impedance degradation by seamless integration into textile and biosymbiotic systems, allowing for high‐fidelity operation over indefinite timescales. These capabilities are demonstrated with ECG during work, activity, and sleep and BioZ recordings documenting gains in forearm training over weeks.

Advances in soft materials, miniaturized electronics, sensors, stimulators, radios, and battery-free power supplies are resulting in a new generation of fully implantable organ interfaces that leverage volumetric reduction and soft mechanics by eliminating electrochemical power storage. This device class offers the ability to provide high-fidelity readouts of physiological processes, enables stimulation, and allows control over organs to realize new therapeutic and diagnostic paradigms. Driven by seamless integration with connected infrastructure, these devices enable personalized digital medicine. Key to advances are carefully designed material, electrophysical, electrochemical, and electromagnetic systems that form implantables with mechanical properties closely matched to the target organ to deliver functionality that supports high-fidelity sensors and stimulators. The elimination of electrochemical power supplies enables control over device operation, anywhere from acute, to lifetimes matching the target subject with physical dimensions that supports imperceptible operation. This review provides a comprehensive overview of the basic building blocks of battery-free organ interfaces and related topics such as implantation, delivery, sterilization, and user acceptance. State of the art examples categorized by organ system and an outlook of interconnection and advanced strategies for computation leveraging the consistent power influx to elevate functionality of this device class over current battery-powered strategies is highlighted.