Intraoperative monitoring of neuromuscular function with soft, skin-mounted wireless devices.

Peripheral nerves are often vulnerable to damage during surgeries, with risks of significant pain, loss of motor function, and reduced quality of life for the patient. Intraoperative methods for monitoring nerve activity are effective, but conventional systems rely on bench-top data acquisition tools with hard-wired connections to electrode leads that must be placed percutaneously inside target muscle tissue. These approaches are time and skill intensive and therefore costly to an extent that precludes their use in many important scenarios. Here we report a soft, skin-mounted monitoring system that measures, stores, and wirelessly transmits electrical signals and physical movement associated with muscle activity, continuously and in real-time during neurosurgical procedures on the peripheral, spinal, and cranial nerves. Surface electromyography and motion measurements can be performed non-invasively in this manner on nearly any muscle location, thereby offering many important advantages in usability and cost, with signal fidelity that matches that of the current clinical standard of care for decision making. These results could significantly improve accessibility of intraoperative monitoring across a broad range of neurosurgical procedures, with associated enhancements in patient outcomes.

A wearable computing platform for developing cloud-based machine learning models for health monitoring applications.

Wearable sensors have the potential to enable clinical-grade ambulatory health monitoring outside the clinic. Technological advances have enabled development of devices that can measure vital signs with great precision and significant progress has been made towards extracting clinically meaningful information from these devices in research studies. However, translating measurement accuracies achieved in the controlled settings such as the lab and clinic to unconstrained environments such as the home remains a challenge. In this paper, we present a novel wearable computing platform for unobtrusive collection of labeled datasets and a new paradigm for continuous development, deployment and evaluation of machine learning models to ensure robust model performance as we transition from the lab to home. Using this system, we train activity classification models across two studies and track changes in model performance as we go from constrained to unconstrained settings.

A stretchable and flexible system for skin-mounted measurement of motion tracking and physiological signals.

In this paper, we present a stretchable wearable system capable of i) measuring multiple physiological parameters and ii) transmitting data via radio frequency to a smart phone. The electrical architecture consists of ultra thin sensors (<; 20 µm thick) and a conformal network of associated active and passive electronics in a mesh-like geometry that can mechanically couple with the curvilinear surfaces of the human body. Spring-like metal interconnects between individual chips on board the device allow the system to accommodate strains approaching ~30% A representative example of a smart patch that measures movement and electromyography (EMG) signals highlights the utility of this new class of medical skin-mounted system in monitoring a broad range of neuromuscular and cardiovascular diseases.