People with speech disorders, including those with pathological conditions of the vocal cords or recovering from larynx cancer surgeries, often encounter difficulty or inability to speak. This scenario may soon undergo a significant change.
Voice disorders are widespread in all age groups and demographic groups, with studies indicating that about 30% of people will experience at least one voice disorder in their lives. However, with therapeutic approaches such as surgery and voice therapy, voice recovery can take up to a year several months, with some invasive techniques requiring a mandatory postoperative vocal rest period.
A team of UCLA engineers developed a soft, thin and elastic device, a little more than 1 square inch, which can be applied to the outer skin of the throat to help people with vocal cord problems recover their vocal function.
The new bioelectrical system, devised by Jun Chen, associate professor of bioengineering at UCLA’s Samueli School of Engineering, together with his colleagues, is able to detect the movements of an individual’s larynx muscles and turn these signals into an audible speech with the aid of Machine Learning technology, with 95% accuracy.
People with speech disorders, including those with pathological conditions of the vocal cords or recovering from larynx cancer surgeries, often encounter difficulty or inability to speak. This scenario may soon undergo a significant change.
Voice disorders are widespread in all age groups and demographic groups, with studies indicating that about 30% of people will experience at least one voice disorder in their lives. However, with therapeutic approaches such as surgery and voice therapy, voice recovery can take up to a year several months, with some invasive techniques requiring a mandatory postoperative vocal rest period.
A team of UCLA engineers developed a soft, thin and elastic device, a little more than 1 square inch, which can be applied to the outer skin of the throat to help people with vocal cord problems recover their vocal function.
The new bioelectrical system, devised by Jun Chen, associate professor of bioengineering at UCLA’s Samueli School of Engineering, together with his colleagues, is able to detect the movements of an individual’s larynx muscles and turn these signals into an audible speech with the aid of Machine Learning technology, with 95% accuracy.
This innovation represents the latest result of Chen’s efforts to help people with disabilities. Previously, his team had developed a wearable glove capable of translating the Sign Language into a real-time speech, facilitating communication between the deaf and those who do not know LIS.
The wearable device is designed to be flexible enough to follow movements and capture the activity of the larynx muscles under the skin. It has the shape of a patch and is composed of two main components: a self-powered sensory component, which detects and converts the signals generated by muscle movements into electrical signals, and an actuation component, which translates these signals into audible speech.
Both components consist of a layer of biocompatible silicone with elastic properties and a magnetic induction layer composed of copper coils. In the middle there is an additional layer containing micromagneti. Using a soft, magnetoelastic detection mechanism developed by the Chen team, the device is able to detect changes in the magnetic field caused by the movements of the larynx muscles.
With dimensions of 1.2 inches on each side and a weight of about 7 grams, the device is only 0.06 inches thick. It is also equipped with a biocompatible double-sided tape, which allows easy positioning on the user’s throat, and which can be reused in case of need.
