Mr. Aman Ganglani Profile

Aman Ganglani

at Univ College London

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Proceedings Article | 11 July 2019 Paper

Integrating motion sensing and wearable, modular high-density diffuse optical tomography: preliminary results

Sabrina Brigadoi, Aman Ganglani, Hubin Zhao, Robert Cooper

Proceedings Volume 11074, 1107405 (2019) https://doi.org/10.1117/12.2527197

KEYWORDS: Sensors, Data acquisition, Diffuse optical tomography, Gyroscopes, Motion detection, Diffuse optical imaging, Data conversion, Neuroimaging, Imaging systems, Electronics

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The next generation of diffuse optical imaging systems will consist of wearable and fiber-less devices, to exploit the advantages of diffuse optical imaging over other functional neuroimaging techniques and meet the needs of users to acquire data in real-world settings. Recently, research at UCL gave rise to a novel, modular high-density diffuse optical tomography (DOT) system that was validated by reconstructing activation images over the motor cortex of a thumb-tofinger extension task. The real question, however, is whether these fiber-less systems can be employed whilst the subject performs real-world activities, that is, whether they can provide reliable signals during participant motion. Integrating motion sensors into modular wearable electronics is straightforward. In this study we acquired DOT and motion sensor data whilst participants performed different activities involving motion. In one acquisition, only accelerometer data were acquired while in the second acquisition, all 9-axis of data (accelerometer, gyroscope and magnetometer data) were acquired. Results demonstrated that acceleration data from motion sensors is not enough to detect motion artifacts whilst performing active movement (e.g., walking), since the global motion obscures any subtle motion artifact. Conversely, by combining accelerometer and gyroscope data it seems possible to detect motion artifacts even during walking, that is when a global motion is present. However, not all types of motion artifacts (e.g., eyebrow raising) could be detected even with this full data configuration. Further studies are required to shed light on this important research question.

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