Onera Health Showcases ONE010 Biomedical-Lab-on-Chip for Wearable Devices

By Abhishek Jadhav

Freelance Tech Writer

February 10, 2022


Onera Health Showcases ONE010 Biomedical-Lab-on-Chip for Wearable Devices
Image Provided by Onera Health

We witnessed many advancements at CES 2022, and nothing is left behind in the embedded health industry. Onera Health launched the Onera Biomedical-Lab-on-Chip, an ultra-low-power biosignal sensor subsystem for wearable devices. This biomedical compact chip is designed for processing multiple biosignals creating a massive opportunity for health devices.

The wearable embedded chip features a wide range of on-chip biomedical readouts with integrated data processing and power management. There are ten analog biomedical readouts designed for ExG sensors covering electrocardiographic heart activity sensors, electroencephalographic brain activity sensors, electromyographic (EMG) muscle activity sensors and electrooculographic (EOG) eye movement sensors. The tightly packed chip also provides the readouts for bioimpedance and photoplethysmographic sensors.

These features sit on top of the powerful ARM M4F processor with an integrated 320kB of SRAM and 768kB of flash storage. The hardware also comes with several accelerators on-chip, including matrix processor, sampling rate, data comparator, data synchronization, FIFO and DMA. The digital interfaces support for the Onera Biomedical-Lab-on-Chip include the SPI, I2C, I2S, UART and 48 GPIOs. Additionally, the power management unit offers LDO to regulate the output voltage even when the supply voltage is very close to the output voltage.

Onera Biomedical-Lab-on-Chip: Compact Yet Powerful

The enabling technology behind the low-power ONE010 wearable chip is the ARM M4F powerful processor, exclusively developed with signal processing capabilities. The blend of high-performance signal processing functionalities, low-power, and low-cost satisfies the market needs. Even though the embedded processor was intended to operate for industrial applications, it is surprising to see the integration into health wearable chips. 

Image Credit: ARM Processor Datasheet

The highly-efficient embedded processor, M4F, is based on the Armv7E-M architecture with 3 stage pipeline and branch prediction. The support for the single-precision floating-point unit (FPU) adds to the signal processing capabilities and the memory protection unit. To understand any embedded processor, it is crucial to analyze the block diagram, which gives us an idea of the highly integrated setup. The CPU Armv7-M is directly connected to the FPU, memory protection unit, and configurable interrupt controller. 

The nested vectored interrupt controller is also integrated with the processor core to achieve a low latency interrupt processor, which is very important to monitor any biomedical signals. These external interrupts are configurable from 1 to 240, giving enough space for all the external triggers. The dynamic reprioritization of the interrupts gives a chance to the device manufacturer to maintain the priority as per the requirements. 

Image Credit: ARM Processor Datasheet

In the three-stage pipeline, the first stage acts as a fetching (prefetching stage), proceeds to the second stage of decode, which takes care of the instruction decode and register read to get the information. The third stage is the execution stage. Most of the processing is done on the fetched information from the address registers.

Final Thoughts on ONE010 Wearable Chip

The ARM powerful embedded processor makes it viable for the Dutch-American BioMed company, Onera to design such an ultra-low-power biosignal hub. According to the manufacturer, the chip operates on a single power source between 0.8V to 3.6V, thanks to the power management unit. The future would be interesting to speculate more innovation in the health wearable industry integrating the ONE010 Biomedical-Lab-on-Chip. 


Abhishek Jadhav is an engineering student, freelance tech writer, RISC-V Ambassador, and leader of the Open Hardware Developer Community.

More from Abhishek