Feasibility and Validity of Discriminating Yaw Plane Head-on-Trunk Motion Using Inertial Wearable Sensors

Abstract

A consequence of vestibular loss is increased coupling of head-on-trunk motion, particularly in the yaw plane, which adversely affects community mobility in these patients. Inertial sensors may provide a means of better understanding normal decoupling behaviors in community environments, but demonstration of their validity and responsiveness is needed. This paper examined the validity and measurement sensitivity of inertial sensors in quantifying yaw plane head-trunk decoupling during unrestricted and restricted cervical motion conditions in healthy adults. Peak head turn amplitude and velocity, head-trunk coupling, and trunk turn lag were simultaneously measured using wearable inertial sensors and a motion capture system. Agreement between motion capture and the inertial sensors was excellent (intraclass correlation coefficients(2,1) >.75) for all measured outcomes during a static head turn task and for peak head turn velocity and trunk turn lag during a walking task. Cervical collar use significantly reduced head turn amplitude and velocity, and increased coupling of head-on-trunk motion (p<.02). Measurement of head and trunk coordination during gait activities using inertial sensors is valid and feasible. Amplitude and velocity outcomes were most reliable and responsive to experimental alterations in head motion. Using inertial sensors to quantify abnormal kinematics following vestibular loss may provide insights into recovery of head-trunk coordination in these individuals.

Volitional Head Movement Deficits and Alterations in Gait Speed Following Mild Traumatic Brain Injury

“…Previous studies of prescribed head movements during gait activities in individuals post-mTBI have, in most cases, limited their focus to yaw plane head rotations. Similarly, studies examining head rotation kinematics during dynamic tasks in other vestibular-impaired populations have primarily focused on yaw plane directed head movements 5,15,17,28. Our findings extend the results of these studies by confirming that head velocity alterations are not plane specific and may share a common mechanism.…”

“…Custom written algorithms (MATLAB, Mathworks, Natick, Massachusetts) were used to derive the primary outcomes from the IMU data. 26 First, sensors were corrected for alignment based on a static 3-second period at the beginning of each trial. Raw data were filtered using a 6-Hz low-pass filter.…”

“…Further, these methods are valid and sensitive to various vestibulopathies. 4,6,7,[15][16][17] With objective quantification through IMUs, it is possible to investigate the interaction between head kinematics and gait during tasks with volitional head turns.…”

The Validity of Wireless Earbud-Type Wearable Sensors for Head Angle Estimation and the Relationships of Head with Trunk, Pelvis, Hip, and Knee during Workouts

“…Our observations are consistent with previous findings that healthy people have a greater head-trunk correlation than people with unilateral vestibular hypofunction while walking on a treadmill because the vestibular system controls movements of the head and trunk together through the vestibulocervical reflex and vestibulospinal reflex, respectively [40]. In people with vestibular system impairment, the head and trunk tend to move independently because they are controlled by other signals, such as visual signals and somatosensory information, respectively [41]. All participants in the present study were healthy without vestibular function impairment; they showed similar patterns of movement between the head and trunk.…”

Reduced Purposeful Head Movements During Community Ambulation Following Unilateral Vestibular Loss

“…All outcomes were derived from sensor data using previously documented algorithms with demonstrated reliability and responsiveness. 15 In brief, the algorithms determined head turns by numerical integration of gyroscopic velocity data between the period when rotational speed exceeded 3 times the expected noise value of the signal and when the rotational speed dropped below the relevant expected noise value or the rotational velocity changed sign. For each valid head turn, head rotation amplitude was determined by the area under the curve (AUC) of the signal obtained from the head sensor, whereas head rotation velocity was determined as the maximum velocity value obtained from the head sensor.…”