Purpose This study aimed to evaluate a novel communication system designed to translate surface electromyographic (sEMG) signals from articulatory muscles into speech using a personalized, digital voice. The system was evaluated for word recognition, prosodic classification, and listener perception of synthesized speech. Method sEMG signals were recorded from the face and neck as speakers with ( n = 4) and without ( n = 4) laryngectomy subvocally recited (silently mouthed) a speech corpus comprising 750 phrases (150 phrases with variable phrase-level stress). Corpus tokens were then translated into speech via personalized voice synthesis ( n = 8 synthetic voices) and compared against phrases produced by each speaker when using their typical mode of communication ( n = 4 natural voices, n = 4 electrolaryngeal [EL] voices). Naïve listeners ( n = 12) evaluated synthetic, natural, and EL speech for acceptability and intelligibility in a visual sort-and-rate task, as well as phrasal stress discriminability via a classification mechanism. Results Recorded sEMG signals were processed to translate sEMG muscle activity into lexical content and categorize variations in phrase-level stress, achieving a mean accuracy of 96.3% ( SD = 3.10%) and 91.2% ( SD = 4.46%), respectively. Synthetic speech was significantly higher in acceptability and intelligibility than EL speech, also leading to greater phrasal stress classification accuracy, whereas natural speech was rated as the most acceptable and intelligible, with the greatest phrasal stress classification accuracy. Conclusion This proof-of-concept study establishes the feasibility of using subvocal sEMG-based alternative communication not only for lexical recognition but also for prosodic communication in healthy individuals, as well as those living with vocal impairments and residual articulatory function. Supplemental Material https://doi.org/10.23641/asha.14558481
Gait analysis is widely used to assess deficits following sports injuries or monitor recovery during rehabilitation [1]. While traditional technologies such as motion capture systems and instrumented force plates can be used to measure kinematics during relevant phases of gait, they are confined to lab environments. Advances in inertial measurement unit (IMU) technology provide alternative prospects for portable assessment of both gait events and kinematics. PURPOSE: Evaluate IMU-based system (wearable sensors and autonomous detection algorithms) for detecting gait events and monitoring range of motion (ROM) under controlled gait variations similar to those observed following an injury [1][2][3]. METHODS: 10 healthy participants (5 M, 5 F, 26.5±2 y.o.) were instrumented with 6 IMUs (Delsys Inc., USA) placed on the sacrum and sternum and bilaterally on the lower/upper legs. Force sensitive resisters (FSR; placed under each foot) and motion capture (Vicon, UK) outcomes were used as gold standard for validating gait events and kinematic measures, respectively. Subjects walked on a treadmill with normal gait followed by gait alterations including: 1) reduced sagittal knee ROM by >30%, 2) reduced sagittal hip ROM by >20%, and 3) increased trunk obliquity by >10%. These gait variations were chosen for their relevance to sport-related injuries such as ACL tear or abdominal strain [1-3]. Our detection algorithms used lower leg IMU data to identify heel strike and toe off, with cycle duration as the time between heel strikes; and IMU data from upper/lower leg, upper leg/sacrum, and sternum to compute knee, hip, and trunk ROM respectively. RESULTS: Cycle duration was detected with <0.5% error with respect to FSRs, and heel strike and toe off were detected within <5% of gait duration across trials, subjects, and gait variations. Knee, hip and trunk ROM were within 5 degrees of those obtained using motion capture for both normal and altered gait. CONCLUSION: Our wearable IMU-based system can accurately detect gait events and calculate gait kinematics during a range of controlled gait variations similar to those resulting from sports injuries. 1] Gokeler et al.
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