<p>Monitoring of postural sway with a head-mounted wearable device: effects of gender, participant state, and concussion</p>

Abstract

Objective: 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. Methods: 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. Questionnaires were used to identify potentially confounding state variables. A prospective study of postural sway changes in 47 professional, college, and high school athletes was then carried out in on-field settings to provide estimates of session-to-session variability and the influence of routine physical activity on sway measures. Finally, pre-post-injury changes in sway are measured for a participant who was diagnosed with a concussion. Results: Despite the heterogenous population and sampling environments, well-defined confidence intervals were established for all 3 sway measures. Men demonstrated significantly greater sway than women. Two state variables significantly increased sway: the use of nicotine and prescription medications. In the athletes, session-to-session variability and changes due to routine physical activity remained well within 95% confidence intervals defined by the cross-sectional sample for all 3 sway measures. The increase in sway power following a diagnosed concussion was more than an order of magnitude greater than the increases due to session-to-session variability, physical activity, or other participant state variables. Conclusion: The proposed postural sway measures and head-mounted wearable sensor demonstrate analytic utility for on-field detection of abnormal sway that could be potentially useful when making remove-from-activity and return-to-activity decisions for athletes at risk of impact-induced balance impairments.

<p>Physiological Vibration Acceleration (Phybrata) Sensor Assessment of Multi-System Physiological Impairments and Sensory Reweighting Following Concussion</p>

“…35 Session-to-session variability and changes due to routine physical activity and potential confounding variables remain well within the 95% CIs for all three sway measures. 35 We have also shown that the device enables on-field detection of abnormal sway to support concussion diagnoses and remove-from-activity/return-to-activity decisions for athletes at risk of impact-induced balance impairments, even in the absence of individual baseline measurements. 35 In the case of concussions and subconcussive head impact exposure, head-mounted devices can also provide the dual functions of continuously monitoring the intensity and frequency of head impacts during athletic activity 36 and quantifying resulting changes in postural stability and balance performance.…”

“…35 We have also shown that the device enables on-field detection of abnormal sway to support concussion diagnoses and remove-from-activity/return-to-activity decisions for athletes at risk of impact-induced balance impairments, even in the absence of individual baseline measurements. 35 In the case of concussions and subconcussive head impact exposure, head-mounted devices can also provide the dual functions of continuously monitoring the intensity and frequency of head impacts during athletic activity 36 and quantifying resulting changes in postural stability and balance performance. 35 The aims of the present study are to investigate applications of the above device as a phybrata sensor, expand our previous investigations to include larger cohorts of clinical concussion patients, and investigate the utility of the device for diagnosing concussions and classifying neurological versus vestibular impairments in patients with diagnosed concussions.…”

“…We have previously reported the utilization of a headmounted wearable inertial motion unit (IMU)-based sensor and three quantitative, physiologically intuitive measures of postural sway to detect outliers in populations at risk of balance impairments. 35 Normative reference ranges and well-defined 95% confidence intervals (CIs) have been established for all three postural sway measures across heterogenous populations and sampling environments. 35 Session-to-session variability and changes due to routine physical activity and potential confounding variables remain well within the 95% CIs for all three sway measures.…”

“…35 Normative reference ranges and well-defined 95% confidence intervals (CIs) have been established for all three postural sway measures across heterogenous populations and sampling environments. 35 Session-to-session variability and changes due to routine physical activity and potential confounding variables remain well within the 95% CIs for all three sway measures. 35 We have also shown that the device enables on-field detection of abnormal sway to support concussion diagnoses and remove-from-activity/return-to-activity decisions for athletes at risk of impact-induced balance impairments, even in the absence of individual baseline measurements.…”

“…35 In the case of concussions and subconcussive head impact exposure, head-mounted devices can also provide the dual functions of continuously monitoring the intensity and frequency of head impacts during athletic activity 36 and quantifying resulting changes in postural stability and balance performance. 35 The aims of the present study are to investigate applications of the above device as a phybrata sensor, expand our previous investigations to include larger cohorts of clinical concussion patients, and investigate the utility of the device for diagnosing concussions and classifying neurological versus vestibular impairments in patients with diagnosed concussions. We show that the device is able to detect normal and pathological features and patterns in phybrata signals that are inherent to human balance stabilization and arise from cerebellar and cortical integration and processing of multiple afferent feedback inputs (visual, vestibular, somatosensory), efferent feedforward motor signals, and musculoskeletal responses.…”