Human Head Yaw Estimation Based on Two 3-Axis Accelerometers

Abstract

The estimation of the orientation of an object, and a human head in particular, can be defined by the Euler angles: the yaw, pitch and roll. The robust and drift-free estimation of those angles is usually achieved with the data from several sensors such as accelerometers, gyroscopes and magnetometers, processed with sensor fusion algorithms. However, wearable devices such as hearing instruments are rarely equipped with all those sensors, and might usually only have a single accelerometer embedded per device. While it is possible to retrieve a correct estimation of the roll and pitch using only a single accelerometer, estimating the yaw is a more challenging task as accelerations around the gravity vector cannot be detected by an accelerometer. In the context of binaural communication devices and spatial hearing, the yaw of the head is a key information that helps achieving dynamic binaural synthesis. This work proposes an algorithm that aims at estimating the yaw of a human head by using only two 3-axis accelerometers that are placed at each side of the head. The algorithm is evaluated with acceleration measurements of human subjects achieving a realistic task. The results suggest that the strategy used is promising as a good performance can be achieved for a majority of the measures as long as an individualized set of parameters can be selected. The results also suggest that the performance of the current implementation is sensitive to sensor displacement after calibration or a wrong estimation during initialization.

Effects of Head-Tracking Artefacts on Externalization and Localization in Azimuth With Binaural Wearable Devices

“…Indeed, a significant enhancement in externalization was provided using a simplified dynamic binaural rendering algorithm designed to be implementable on wearable devices. All HRTFs and BRIRs were generic, and the length of the BRIRs was always truncated to 10 ms. A simulation of a yaw estimation mismatch, as can be obtained with a scaled-down head-tracking algorithm [5], did not affect the perception of externalization in the listening test reported in this study.…”

“…The binaural rendering used in this experiment is also adapted to the constraints of wearable hearing devices, with the use of non-individualized and low-computational cost spatialization algorithms. The results were expected to assess the advantages provided by the head-tracking algorithm described in [5] for binaural synthesis in the context of remote microphone systems for wearable devices.…”

“…A head-tracking algorithm relying solely on the use of two three-axis accelerometers was developed in [5]. One of the main limitations of this algorithm comes from the difficulty in tracking slower parts of the motion, as the low values of accelerations have amplitudes too small to be distinguished from the noise of the accelerometers.…”

“…A method yielding similar results was implemented using the computation described in APPENDIX A. The value of γ v was set empirically to 0.8, which leads to similar range of errors as the algorithm in [5]. An example of the resulting artefact estimation is depicted in Fig.…”

“…A head-tracking algorithm based on two three-axis accelerometers was developed in [5]. The algorithm design was constrained by the limitations of the used wearable hearing devices.…”