UCLA Bioengineers Develop Wearable Device for Voice Disorder Treatment Using Machine Learning

The photo shows a person holding a small, flexible square device between his fingers, showing its elasticity and flexible nature.  The device appears to be made of a black, grid-like material with cutouts that allow it to stretch into different shapes.

Bioengineers at UCLA have developed a thin, flexible device that can help people with voice disorders regain the ability to speak. The device attaches to the neck and translates the movements of the larynx muscles into audible speech using machine learning. This self-powered technology could serve as a non-invasive tool for people who have lost their voice due to vocal cord problems, such as those recovering from laryngeal cancer surgeries or with pathological vocal cord conditions.

The device consists of two main components: a sensing component and an actuation component. The sensing component detects and converts signals generated by laryngeal muscle movements into electrical signals using a soft magnetoelastic mechanism. These electrical signals are then translated into voice signals using a machine learning algorithm. The performance component takes these speech signals and converts them into the desired speech expression. The thin and lightweight device can be easily attached to the throat area using biocompatible tape.

This is an illustrative diagram showing a portable bioelectronic device for speaking without vocal cords.  It shows a side profile of a human figure with focus on the throat area, where the device would be placed.  The device itself is detailed in an exploded view, highlighting its components such as flexible coils, PDMS layers, magnetic induction layers, and a magnetomechanical coupling layer.  The diagram also shows sound waveforms that represent the translation of muscle movements into speech.

The researchers tested the wearable technology on eight healthy adults and achieved an overall prediction accuracy of 94.68% by translating laryngeal movements into corresponding sentences. In the future, the team plans to expand the device’s vocabulary using machine learning and test it on people with speech disorders. This non-invasive, portable solution could provide a convenient option for people receiving treatment or recovering from voice disorders, which affect nearly 30% of the population at some point in their lives.

Fountain: UCLA

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