Detecting Psychological Interventions Using Bilateral Electromyographic Wearable Sensors.

This study investigated the impact of auditory stimuli on muscular activation patterns using wearable surface electromyography (EMG) sensors. Employing four key muscles (Sternocleidomastoid Muscle (SCM), Cervical Erector Muscle (CEM), Quadricep Muscles (QMs), and Tibialis Muscle (TM)) and time domain features, we differentiated the effects of four interventions: silence, music, positive reinforcement, and negative reinforcement. The results demonstrated distinct muscle responses to the interventions, with the SCM and CEM being the most sensitive to changes and the TM being the most active and stimulus dependent. Post hoc analyses revealed significant intervention-specific activations in the CEM and TM for specific time points and intervention pairs, suggesting dynamic modulation and time-dependent integration. Multi-feature analysis identified both statistical and Hjorth features as potent discriminators, reflecting diverse adaptations in muscle recruitment, activation intensity, control, and signal dynamics. These features hold promise as potential biomarkers for monitoring muscle function in various clinical and research applications. Finally, muscle-specific Random Forest classification achieved the highest accuracy and Area Under the ROC Curve for the TM, indicating its potential for differentiating interventions with high precision. This study paves the way for personalized neuroadaptive interventions in rehabilitation, sports science, ergonomics, and healthcare by exploiting the diverse and dynamic landscape of muscle responses to auditory stimuli.

The Effects of Vision-Deprived Progressive Resistance Training on One-Repetition Maximum Bench Press Performance: An Exploratory Study.

The objective of this study was to determine whether vision-occluded progressive resistance training would increase upper-extremity movement performance using the one-repetition maximum (1-RM) bench press. Participants (n = 57) were recruited from a historically black college and university (HBCU), cross-matched by sex, age (±1 year), 1-RM (±2.27 kg), 1-RM/weight (±0.1), and 1-RM/lean mass ratio (±0.1), and randomly assigned to either the experimental group (vision occluded) or the control group. Participants performed resistance training for 6 weeks prior to beginning the study, and 1-RM was assessed the week prior to the beginning of the study. Weight and body composition were measured using a BOD POD. Of the 57 participants who started the study, 34 completed the study (Experimental = 16, Control = 18) and were reassessed the week after completing the 6-week-long training protocol. Using a combination of Mann−Whitney U and Wilcoxon signed-rank tests, we found that when accounting for changes in lean muscle mass, individuals who trained with their vision occluded reported significantly greater improvements in 1-RM strength compared to those who did not (p < 0.05). The findings from our study suggest that vision-occluded progressive resistance training increases upper-extremity performance when assessed using the bench press. These findings may have significant practical implications in both sports and rehabilitation, as these techniques may be used to enhance performance in athletes and/or improve rehabilitation effectiveness.

Identifying Individuals Who Currently Report Feelings of Anxiety Using Walking Gait and Quiet Balance: An Exploratory Study Using Machine Learning.

Literature suggests that anxiety affects gait and balance among young adults. However, previous studies using machine learning (ML) have only used gait to identify individuals who report feeling anxious. Therefore, the purpose of this study was to identify individuals who report feeling anxious at that time using a combination of gait and quiet balance ML. Using a cross-sectional design, participants (n = 88) completed the Profile of Mood Survey-Short Form (POMS-SF) to measure current feelings of anxiety and were then asked to complete a modified Clinical Test for Sensory Interaction in Balance (mCTSIB) and a two-minute walk around a 6 m track while wearing nine APDM mobility sensors. Results from our study finds that Random Forest classifiers had the highest median accuracy rate (75%) and the five top features for identifying anxious individuals were all gait parameters (turn angles, variance in neck, lumbar rotation, lumbar movement in the sagittal plane, and arm movement). Post-hoc analyses suggest that individuals who reported feeling anxious also walked using gait patterns most similar to older individuals who are fearful of falling. Additionally, we find that individuals who are anxious also had less postural stability when they had visual input; however, these individuals had less movement during postural sway when visual input was removed.