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Evaluation of a Machine Learning Algorithm to Classify Ultrasonic Transducer Misalignment and Deployment Using

Des Brennan¹, Paul Galvin¹

¹Tyndall National Institute, University College, T12 K8AF Cork, Ireland.

Sensors (Basel, Switzerland)

|January 23, 2024

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Machine learning algorithms accurately detect ultrasonic transducer misalignment in medical devices. This technology enables real-time correction, enhancing power transfer for implanted and wearable systems.

Area of Science:

Biomedical Engineering
Machine Learning
Signal Processing

Background:

Ultrasonic (US) power transfer is crucial for implanted/wearable medical devices.
Transducer misalignment due to body motion disrupts efficient power delivery.
Accurate detection and correction of misalignment are essential for system reliability.

Purpose of the Study:

To evaluate machine learning (ML) algorithms for classifying ultrasonic transducer misalignment.
To develop a system for real-time detection and potential correction of misalignment.
To enable enhanced power transfer in medical devices through improved alignment.

Main Methods:

Acquired over 700 US signals across various transducer misalignments.
Trained and evaluated ML algorithms including autoencoder, convolutional neural network (CNN), and neural network (NN) using signal envelopes and spectrograms.

Keywords:

TinyML machine learning ultrasonic

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Deployed the best-performing ML model onto a TinyML device using TensorFlow Lite and Edge Impulse for edge processing.

Main Results:

Achieved >99% accuracy in classifying transducer misalignment extent.
Demonstrated near real-time (<350 ms) signal classification on the TinyML device.
Validated the feasibility of deploying ML algorithms on low-power, memory-restricted edge devices.

Conclusions:

ML-based classification of US transducer misalignment is highly accurate and efficient.
TinyML deployment enables real-time alignment correction for enhanced power transfer.
This approach can be applied to US arrays (CMUT, PMUT) for beam-steering, significantly improving implanted and body-worn systems.