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BASS: Safe Deep Tissue Optical Sensing for Wearable Embedded Systems.

Kourosh Vali¹, Ata Vafi¹, Begum Kasap¹

¹University of California, Davis, Electrical and Computer Engineering Department, USA.

ACM Transactions on Embedded Computing Systems : TECS

|January 24, 2024

Summary

This summary is machine-generated.

This study optimizes deep tissue optical sensing by balancing signal-to-noise ratio (SNR) and patient safety. The BASS algorithm improves SNR through spectral averaging while adhering to safe energy limits.

Keywords:

deep tissue optical sensing design space exploration internet of medical things medical cyber-physical systems multi-objective optimization wearable embedded systems

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Area of Science:

Biomedical Engineering
Optical Sensing Technologies
Wearable Health Devices

Background:

Deep tissue optical sensing, like oximetry, faces challenges in balancing signal quality with patient safety.
Increasing radiated energy improves signal-to-noise ratio (SNR) but poses safety risks.
Embedded system design requires careful consideration of these competing factors.

Purpose of the Study:

To explore the trade-offs between radiated energy and SNR in deep tissue optical sensing.
To propose and validate the BASS algorithm for optimizing deep tissue SNR.
To ensure patient safety by adhering to radiated energy constraints.

Main Methods:

Investigated the design space of light source activation pulses.
Developed and applied the BASS (Bandwidth-Aware Spectral Sampling) algorithm.
Validated the technique through analytical derivations, simulations, and in vivo measurements.

Main Results:

The BASS algorithm effectively optimizes deep tissue SNR via spectral averaging.
The proposed method ensures radiated energy remains within safe upper bounds.
Successful demonstrations were conducted in pregnant sheep models and human subjects.

Conclusions:

The BASS algorithm offers an effective solution for deep tissue optical sensing.
It successfully balances the need for high SNR with critical patient safety requirements.
This approach advances wearable optical sensing for non-superficial tissue applications.