Phybrata Sensors and Machine Learning for Enhanced Neurophysiological Diagnosis and Treatment.

Concussion injuries remain a significant public health challenge. A significant unmet clinical need remains for tools that allow related physiological impairments and longer-term health risks to be identified earlier, better quantified, and more easily monitored over time. We address this challenge by combining a head-mounted wearable inertial motion unit (IMU)-based physiological vibration acceleration ("phybrata") sensor and several candidate machine learning (ML) models.

Physiological Vibration Acceleration (Phybrata) Sensor Assessment of Multi-System Physiological Impairments and Sensory Reweighting Following Concussion.

Objective: To assess the utility of a head-mounted wearable inertial motion unit (IMU)-based physiological vibration acceleration ("phybrata") sensor to support the clinical diagnosis of concussion, classify and quantify specific concussion-induced physiological system impairments and sensory reweighting, and track individual patient recovery trajectories.

Methods: Data were analyzed from 175 patients over a 12-month period at three clinical sites. Comprehensive clinical concussion assessments were first completed for all patients, followed by testing with the phybrata sensor.

Monitoring of postural sway with a head-mounted wearable device: effects of gender, participant state, and concussion.

To assess the utility of a head-mounted wearable inertial motion unit (IMU)-based sensor and 3 proposed measures of postural sway to detect outliers in athletic populations at risk of balance impairments. Descriptive statistics are used to define a normative reference range of postural sway (eyes open and eyes closed) in a cross-sectional sample of 347 college students using a wireless head-mounted IMU-based sensor. Three measures of postural sway were derived: linear sway power, eyes closed vs eyes open sway power ratio (Ec/Eo ratio), and weight-bearing asymmetry (L-R ratio), and confidence intervals for these measures were calculated.