An energy-efficient, high-precision measurement system using waveform similarity

Researchers at the University of Osaka have developed an energy-efficient and high-precision measurement system leveraging the inherent similarity between waveforms generated by the same type of signal source.

Unlike black-box approaches such as generative AI, the system is built on the explicit theoretical framework of compressed sensing. This innovative approach drastically reduces the amount of data required for accurate signal reproduction, leading to significant energy savings.

Demonstrated with an electroencephalogram (EEG) measuring system, the technology achieved world-leading energy efficiency using only commercially available electronic components, consuming a mere 72μW. This breakthrough paves the way for long-term, battery-powered wearable devices and self-powered, battery-free IoT devices that can operate on minimal energy harvested from the environment, with broad applications in health care, disaster prevention, and environmental monitoring.

The proliferation of wearable devices and IoT sensors has highlighted the critical challenges of battery life and charging requirements. Achieving high-precision measurements while minimizing energy consumption has proven particularly difficult, demanding new technological breakthroughs. Conventional methods of reducing energy consumption in sensors often compromise waveform reproduction accuracy.

Addressing this trade-off, the University of Osaka research group built upon their 2023 waveform similarity-based measurement theory to develop a system that achieves both energy efficiency and high precision.

The core of this innovation lies in exploiting the inherent similarity between waveforms emanating from a common source. This allows for significant data reduction while maintaining high-fidelity signal reconstruction.

The researchers implemented an EEG measurement system using readily available components, including a general-purpose microcontroller (nRF52840). This system minimized power consumption to an impressive 72μW for all measurement operations, from analog-to-digital conversion to wireless transmission.

By leveraging waveform similarities between previously recorded EEG data from other subjects and the current subject’s data, the system achieved high-accuracy waveform reproduction, demonstrating a Normalized Mean Squared Error (NMSE) of 0.116 averaged over 500 measurements.

The successful demonstration of this energy-efficient, high-precision measurement system using off-the-shelf components for EEG measurement has far-reaching implications. It opens exciting new possibilities for wearable devices capable of continuous, long-term bio-signal monitoring powered by compact, lightweight batteries.

Furthermore, it enables the development of self-powered, battery-free IoT devices and infrastructure monitoring sensors using energy harvesting technologies. These advancements promise significant contributions to sustainable development across diverse fields, including health care, elderly care, disaster preparedness, and environmental monitoring.