Identifying Current Feelings of Mild and Moderate to High Depression in Young, Healthy Individuals Using Gait and Balance: An Exploratory Study

3D-optical motion capturing examination of sensori- and psychomotor abnormalities in mental disorders: Progress and perspectives

Sensori-/psychomotor abnormalities refer to a wide range of disturbances in individual motor, affective and behavioral functions that are often observed in mental disorders. However, many of these studies have mainly used clinical rating scales, which can be potentially confounded by observer bias and are not able to detect subtle sensori-/psychomotor abnormalities. Yet, an innovative three-dimensional (3D) optical motion capturing technology (MoCap) can provide more objective and quantifiable data about movements and posture in psychiatric patients. To draw attention to recent rapid progress in the field, we performed a systematic review using PubMed, Medline, Embase, and Web of Science until May 01st 2024. We included 55 studies in the qualitative analysis and gait was the most examined movement. The identified studies suggested that sensori-/psychomotor abnormalities in neurodevelopmental, mood, schizophrenia spectrum and neurocognitive disorders are associated with alterations in spatiotemporal parameters (speed, step width, length and height; stance time, swing time, double limb support time, phases duration, adjusting sway, acceleration, etc.) during various movements such as walking, running, upper body, hand and head movements. Some studies highlighted the advantages of 3D optical MoCap systems over traditional rating scales and measurements such as actigraphy and ultrasound gait analyses. 3D optical MoCap systems are susceptible to detecting differences not only between patients with mental disorders and healthy persons but also among at-risk individuals exhibiting subtle sensori-/psychomotor abnormalities. Overall, 3D optical MoCap systems hold promise for objectively examining sensori-/psychomotor abnormalities, making them valuable tools for use in future clinical trials.

Identifying Predictors of Neck Disability in Patients with Cervical Pain Using Machine Learning Algorithms: A Cross-Sectional Correlational Study

(1) Background: Neck pain intensity, psychosocial factors, and physical function have been identified as potential predictors of neck disability. Machine learning algorithms have shown promise in classifying patients based on their neck disability status. So, the current study was conducted to identify predictors of neck disability in patients with neck pain based on clinical findings using machine learning algorithms. (2) Methods: Ninety participants with chronic neck pain took part in the study. Demographic characteristics in addition to neck pain intensity, the neck disability index, cervical spine contour, and surface electromyographic characteristics of the axioscapular muscles were measured. Participants were categorised into high disability and low disability groups based on the median value (22.2) of their neck disability index scores. Several regression and classification machine learning models were trained and assessed using a 10-fold cross-validation method; also, MANCOVA was used to compare between the two groups. (3) Results: The multilayer perceptron (MLP) revealed the highest adjusted R2 of 0.768, while linear discriminate analysis showed the highest receiver characteristic operator (ROC) area under the curve of 0.91. Pain intensity was the most important feature in both models with the highest effect size of 0.568 with p < 0.001. (4) Conclusions: The study findings provide valuable insights into pain as the most important predictor of neck disability in patients with cervical pain. Tailoring interventions based on pain can improve patient outcomes and potentially prevent or reduce neck disability.

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.