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.

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.