Nathan Schilaty

Artificial Intelligence (AI) and digital devices are becoming more pervasive in the monitoring of individuals’ health as device functionalities increase and their presence in daily life continues to grow. Subsequently, these tools can provide transformative healthcare practice by enabling more personalized, proactive, and engaging interventions. This work presents the design and development of an interactive physical therapy system for adolescents that integrates a wearable EMG leg sensor array with a computer-based video game. The system captures EMG signals during therapeutic exercises and uses them to drive in-game mechanics, providing real-time feedback while also aiming to improve therapeutic engagement. A key feature of the system is its ability to adapt based on individual performance and clinician input, allowing personalized therapy progression and reporting. Using this approach, the system supports continuous personalized assessment and customized intervention for children while promoting motivation through dynamic gameplay. The developed system illustrates a promising direction for designing interactive, AI-augmented health technologies that can enhance pediatric rehabilitation outcomes through accessible, game-based experiences. Further, this work contributes to ongoing research in human-computer interaction, AI, and behavioral health by exploring multisensory interactions, social AI elements, and methods for evaluating user engagement.

Traumatic brain injury (TBI), particularly mild forms resulting from blast exposure, remains a diagnostic challenge among veterans due to delayed symptom onset and overlapping psychological conditions such as posttraumatic stress disorder. Current diagnostic methods rely heavily on subjective assessments, contributing to underdiagnosis and inconsistent care. Electroencephalography (EEG) offers a non-invasive, real-time measure of cortical activity with high temporal resolution, making it a promising tool for objective TBI assessment. This sub-study, nested within a randomized controlled trial evaluating hyperbaric oxygen therapy (HBOT) for veterans with TBI, investigates EEG as both a diagnostic modality and a therapeutic monitor. This sub-study adopts the same triple-blinded, randomized, parallel-group design as the parent trial. Participants are veterans with mild to moderate TBI, randomized to receive either HBOT or a sham treatment. EEG data are collected at three time points: baseline (pre-treatment), midpoint (18–21 dives), and post-treatment (2 weeks after completion). EEG recordings are performed during standardized cognitive and motor tasks using a 32-channel wireless headset. Primary outcomes include event-related potential amplitudes and alpha-band spectral power, analyzed for longitudinal changes and group differences. Secondary outcomes include latency measures, spectral power across additional frequency bands, and functional connectivity metrics. Data are modeled using repeated measures analysis to assess treatment effects and individual trajectories. This sub-study aims to validate EEG as a scalable and objective tool for diagnosing and monitoring TBI in clinical settings. By identifying electrophysiological signatures associated with injury severity and treatment response, EEG may enhance diagnostic precision and support personalized care strategies. The integration of EEG within a larger therapeutic trial framework allows for comprehensive evaluation of its clinical utility. Findings may inform future protocols for TBI assessment and contribute to the development of neurophysiological biomarkers that complement existing symptom-based approaches.