Unlike many of the current AAC devices, sEMG-based SSR presents a hands-free tool to allow users to communicate without the added physical reliance on their hands. The wireless nature of sEMG technology means sensors can be fitted to the specific articulatory muscles of interest, as shown in figure 1.
Figure 1 – Depiction of sensor configurations targeting (1) anterior belly of the digastric, mylohyoid, and geniohyoid; (2) platysma, mylohyoid, and stylohoid; (3, 4) platysma, thyrohyoid, omohyoid, and sternohyoid; (5) zygomaticus major and/or minor, levator labii superioris, and levator anguli oris; (6, 7) orbicularis oris; and (8) mentalis.
The sEMG sensors produce high-fidelity data. When speaking about the signal output from the sensors, Jennifer made clear “it is important to note that the sEMG amplitude may vary substantially across speaker, sensor location, and phonemic content but also due to the individual way in which patients stress phrases or words.” As shown in figure 2, when the patient stresses particular words in the phrase, there is a notable increase in the signal amplitude, particularly from the ventral neck muscles (sensors 1-4 as shown in figure 1).
Figure 2 – Example of raw surface electromyographic signals obtained from one speaker with laryngectomy from the token “Mom strongly dislikes appetizers.”
By tuning the custom algorithm to look for not only lexical content (via similarities in sEMG features in the presence of prosodic modulations), but also for categorized phrasal stress (via differences in sEMG features in repetition of the same phrase), the team were able to generate a personalized, digital voice!