Standalone real-time health monitoring patch based on a stretchable organic optoelectronic system.

Skin-like health care patches (SHPs) are next-generation health care gadgets that will enable seamless monitoring of biological signals in daily life. Skin-conformable sensors and a stretchable display are critical for the development of standalone SHPs that provide real-time information while alleviating privacy concerns related to wireless data transmission. However, the production of stretchable wearable displays with sufficient pixels to display this information remains challenging. Here, we report a standalone organic SHP that provides real-time heart rate information. The 15-µm-thick SHP comprises a stretchable organic light-emitting diode display and stretchable organic photoplethysmography (PPG) heart rate sensor on all-elastomer substrate and operates stably under 30% strain using a combination of stress relief layers and deformable micro-cracked interconnects that reduce the mechanical stress on the active optoelectronic components. This approach provides a rational strategy for high-resolution stretchable displays, enabling the production of ideal platforms for next-generation wearable health care electronics.

Micro Bio Processor Development Kit for Wireless Neural Implants.

Micro Bio Processor version 1.5 (MBPv15) Development Kit is specially engineered to support various function-alities of implantable devices such as bio-signal sensing, neural stimulation, and dual-band wireless connectivity & charging. It provides a convenient way to evaluate the MBPv15 chip solution as a system component by a modular design of hardware and software. As a result, MBPv15 chip solution enables to develop wireless neural implants in a mm-scale form factor with ultra-low power consumption by achieving 1.6 mW for neural spike detection and 9.8 mW for neural stimulation, respectively.

ECG Authentication Hardware Design With Low-Power Signal Processing and Neural Network Optimization With Low Precision and Structured Compression.

Biometrics such as facial features, fingerprint, and iris are being used increasingly in modern authentication systems. These methods are now popular and have found their way into many portable electronics such as smartphones, tablets, and laptops. Furthermore, the use of biometrics enables secure access to private medical data, now collected in wearable devices such as smartwatches. In this work, we present an accurate low-power device authentication system that employs electrocardiogram (ECG) signals as the biometric modality. The proposed ECG processor consists of front-end signal processing of ECG signals and back-end neural networks (NNs) for accurate authentication. The NNs are trained using a cost function that minimizes intra-individual distance over time and maximizes inter-individual distance. Efficient low-power hardware was implemented by using fixed coefficients for ECG signal pre-processing and by using joint optimization of low-precision and structured sparsity for the NNs. We implemented two instances of ECG authentication hardware with 4X and 8X structurally-compressed NNs in 65 nm LP CMOS, which consume low power of 62.37  µW and 75.41  µW for real-time ECG authentication with a low equal error rate of 1.36% and 1.21%, respectively, for a large 741-subject in-house ECG database. The hardware was evaluated at 10 kHz clock frequency and 1.2 V voltage supply.

A Simple and Stable Load Control Algorithm for Time-Varying Harvested Energy in Miniaturized Implantable Devices.

One of the most challenging issues in a miniaturized implantable device is to supply power sufficiently and continuously for stable operation of the device. An optical energy transfer is the most practical solution due to its high efficiency among existing wireless power transmission techniques. In general, large-capacity storage unit and responsive power regulation circuits are essential to overcome the fluctuation of harvested energy, but this conventional architecture cannot be implemented in the millimeter scale device due to the volume limitation. In this paper, we propose a control-theoretic load control algorithm to maximize the utilization of time-varying harvested energy while maintaining a minimum voltage ripple in the storage capacitor. The proposed algorithm estimates available current for the next time slot based on the amount of charge in the storage capacitor and that of harvested energy, then matching system load current with the result. This algorithm is simple in that the number of computations at micro-processor is minimized and stable in that the voltage of the storage capacitor is maintained at the target level. We analyze the theoretical stability of the proposed algorithm and validate its superior performance through simulation.