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

Quantifying unsupported sitting posture impairments in humans with cervical spinal cord injury using a head-mounted IMU sensor

STUDY DESIGN

Cross-sectional study.

OBJECTIVES

To evaluate unsupported sitting posture impairments and identify postural regulatory strategies in cervical spinal cord injury (cSCI) participants via a head-mounted IMU sensor.

SETTING

A research lab in the United States of America.

METHODS

cSCI participants and controls maintained postural stability during unsupported sitting with eyes either open or closed. The head-mounted IMU sensor recorded accelerometer data to calculate cumulative sway motion. The postural regulatory strategy was analyzed by assessing the normalized power spectral density (PSD) in four frequency bands: 0-0.1 Hz (visual regulation), 0.1-0.5 Hz (vestibular regulation), 0.5-1 Hz (cerebellar regulation), and >1 Hz (proprioception and muscle control).

RESULTS

Significant increases in postural sway were observed in cSCI participants compared to controls during unsupported sitting. For cSCI participants, normalized PSD significantly increased in the low-frequency bands (0-0.1 Hz and 0.1-0.5 Hz) but decreased in the high-frequency band (>1 Hz) compared to controls.

CONCLUSIONS

cSCI participants were more reliant on visual and vestibular systems for sitting balance, while depending less on proprioception and muscle control compared to controls. These findings suggest that the altered postural regulatory strategy is ineffective in maintaining postural stability during unsupported sitting, emphasizing the importance of proprioception and muscle control for seated postural stability in cSCI participants.

Effect of test duration and sensor location on the reliability of standing balance parameters derived using body-mounted accelerometers

BACKGROUND

Balance parameters derived from wearable sensor measurements during postural sway have been shown to be sensitive to experimental variables such as test duration, sensor number, and sensor location that influence the magnitude and frequency-related properties of measured center-of-mass (COM) and center-of-pressure (COP) excursions. In this study, we investigated the effects of test duration, the number of sensors, and sensor location on the reliability of standing balance parameters derived using body-mounted accelerometers.

METHODS

Twelve volunteers without any prior history of balance disorders were enrolled in the study. They were asked to perform two 2-min quiet standing tests with two different testing conditions (eyes open and eyes closed). Five inertial measurement units (IMUs) were employed to capture postural sway data from each participant. IMUs were attached to the participants' right legs, the second sacral vertebra, sternum, and the left mastoid processes. Balance parameters of interest were calculated for the single head, sternum, and sacrum accelerometers, as well as, a three-sensor combination (leg, sacrum, and sternum). Accelerometer data were used to estimate COP-based and COM-based balance parameters during quiet standing. To examine the effect of test duration and sensor location, each 120-s recording from different sensor locations was segmented into 20-, 30-, 40-, 50-, 60-, 70-, 80-, 90-, 100-, and 110-s intervals. For each of these time intervals, time- and frequency-domain balance parameters were calculated for all sensor locations.

RESULTS

Most COM-based and COP-based balance parameters could be derived reliably for clinical applications (Intraclass-Correlation Coefficient, ICC ≥ 0.90) with a minimum test duration of 70 and 110 s, respectively. The exceptions were COP-based parameters obtained using a sacrum-mounted sensor, especially in the eyes-closed condition, which could not be reliably used for clinical applications even with a 120-s test duration.

CONCLUSIONS

Most standing balance parameters can be reliably measured using a single head- or sternum-mounted sensor within a 120-s test duration. For other sensor locations, the minimum test duration may be longer and may depend on the specific test conditions.

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. The performance of this solution is assessed for both binary classification of concussion patients and multiclass predictions of specific concussion-related neurophysiological impairments. Results are compared with previously reported approaches to ML-based concussion diagnostics. Using phybrata data from a previously reported concussion study population, four different machine learning models (Support Vector Machine, Random Forest Classifier, Extreme Gradient Boost, and Convolutional Neural Network) are first investigated for binary classification of the test population as healthy vs. concussion (Use Case 1). Results are compared for two different data preprocessing pipelines, Time-Series Averaging (TSA) and Non-Time-Series Feature Extraction (NTS). Next, the three best-performing NTS models are compared in terms of their multiclass prediction performance for specific concussion-related impairments: vestibular, neurological, both (Use Case 2). For Use Case 1, the NTS model approach outperformed the TSA approach, with the two best algorithms achieving an F1 score of 0.94. For Use Case 2, the NTS Random Forest model achieved the best performance in the testing set, with an F1 score of 0.90, and identified a wider range of relevant phybrata signal features that contributed to impairment classification compared with manual feature inspection and statistical data analysis. The overall classification performance achieved in the present work exceeds previously reported approaches to ML-based concussion diagnostics using other data sources and ML models. This study also demonstrates the first combination of a wearable IMU-based sensor and ML model that enables both binary classification of concussion patients and multiclass predictions of specific concussion-related neurophysiological impairments.

Investigating the validity of a single tri-axial accelerometer mounted on the head for monitoring the activities of daily living and the timed-up and go test

BACKGROUND

Quantitative assessments of activities of daily living (ADL) play an essential role in evaluating the impact of disease and interventions on people's quality of life. Motion capture systems traditionally used for quantitative assessments of postural transitions and movement associated with ADL are limited to the laboratory setting. Wearable accelerometers can remove these limitations and enable easier-to-use, longer-term, and remote functional evaluations.

OBJECTIVE

To investigate the validity of a single tri-axial accelerometer mounted on the head for monitoring postural transition and the timed-up-and-go test.

METHODS

Two accelerometers with a sampling frequency of 100 Hz were attached to twelve able-bodied study participants' sternum and right mastoid process. We developed algorithms for the functional calibration of accelerometers and the detection of the postural transitions by measuring the head inclination angle and variations of the gravitational components of the accelerometer readout. Participants performed a battery of ADL tests involving a wide variety of postural transitions. The head-mounted accelerometers results were compared with a sternum-mounted accelerometer and validated against a video motion capture system as a gold standard reference.

RESULTS AND SIGNIFICANCE

The results indicate that, utilizing our proposed algorithm, a single tri-axial accelerometer mounted on the head can deliver high accuracy (>95 %), sensitivity (>90 %), and specificity (100 %) for detecting both postural transitions and walking events. Together with the small size and unobtrusive placement of the head-mounted accelerometer, these results demonstrate an attractive solution for the reliable assessment of ADLs and clinical evaluations based on functional tests such as the timed-up-and-go test.

Altered Vestibular Balance Function in Combat Sport Athletes

Combat sports pose a risk for accumulative injuries to the nervous system, yet fighters have remained an understudied population. Here, our purpose was to determine whether repetitive blows to the head have an effect on vestibular balance reflexes in combat sports athletes. We compared lower-limb muscle responses evoked with electrical vestibular stimuluation (EVS) between fighters (boxing/muay thai) and non-fighter controls. Each participant received stochastic vestibular stimulation (0-25 Hz, ±3 mA) over their mastoid processes while they stood relaxed with their head to the left or right. Surface electromyography was recorded from the medial gastrocnemius and soleus muscles bilaterally. Short and medium latency response (SLR/MLR) peaks were significantly delayed in the fighter group compared to controls. SLR and MLR peak amplitudes were also significantly lower in fighters. Fighter-estimated cumulative repetitive head impact (RHI) events demonstrated strong positive correlations with the timing of SLR and MLR peaks. Cumulative RHI events also negatively correlated with peak MLR amplitude and response gain at frequencies above 5 Hz. Our results provide evidence of a progressive vestibular impairment in combat sports athletes, potentially resulting from blows to the head accumulated in sparring practice and competitive bouts throughout their careers. Taken together, EVS-based vestibular assessments may provide a valuable clinical diagnostic tool and help better inform "return-to-play" and career-length decisions for not only combat sports athletes, but potentially other populations at risk of RHIs.