Investigating how facial movements can be used to characterize and quantify speech is important, in particular, to aid those suffering from motor control speech disorders. Here, we sought to investigate how facial action units (AUs), previously used to classify human expressions and emotion, could be used to quantify and understand unimpaired human speech. Fourteen (14) adult participants (30.1 ± 7.9 years old), fluent in English, with no speech impairments, were examined. Within each data collection session, 6 video trials per participant per phoneme were acquired (i.e., 102 trials total/phoneme). The participants were asked to vocalize the vowels /æ/, /ɛ/, /ɪ/, /ɒ/, and /ʊ/; the consonants /b/, /n/, /m/, /p/, /h/, /w/, and /d/; and the diphthongs /eI/, /ʌɪ/, /i/, /a:/, and /u:/. Through the use of Python Py-Feat, our analysis displayed the AU contributions for each phoneme. The important implication of our methodological findings is that AUs could be used to quantify speech in populations with no speech disability; this has the potential to be broadened toward providing feedback and characterization of speech changes and improvements in impaired populations. This would be of interest to persons with speech disorders, speech language pathologists, engineers, and physicians.

In the aging population, imbalance leading to falls is of critical concern; thus, it is imperative to determine and quantify neuromuscular changes because of rehabilitative balance training. (1) Background: Previous studies that have examined changes in balance due to rehabilitative training placed a focus on clinical measures (i.e., behavioral, kinetic, and kinematic outputs); however, irregularities due to abnormalities of underlying neural origin were unrevealed by the aforementioned measures. (2) Methods: Examining muscle activity was used to determine strategies pre- and post-six weeks of balance training in twenty-three healthy older adults (69.5 ± 5.7 years old) and five survivors of stroke (66.4 ± 9.48 years old). Surface electromyographic (sEMG) signals were recorded from eight of the lower limb muscles while participants performed forward walking (FW), forward tandem walking (FT-W), backward walking (BW), and backward tandem walking (BT-W) tasks. The sEMG data were then conditioned and muscle synergies were extracted using non-negative matrix factorization (NNMF). (3) Results: It was observed that muscle synergies and activation patterns changed for pre- versus post-balance training in older adults (i.e., healthy individuals and those who had suffered from stroke). (4) Conclusions: From our findings, it was indicative that muscle activation and muscle synergies could be used to quantify and inform rehabilitative balance training in older adults.