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Sensors 2016, 16(7), 1037; doi:10.3390/s16071037

Autonomous Quality Control of Joint Orientation Measured with Inertial Sensors

1
Faculty of Medicine and Health Sciences, Orthopedic service, department of surgery, Université de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada
2
Research Center on Aging, Sherbrooke, QC J1H 4C4, Canada
3
Interdisciplinary Institute for Technological Innovation (3IT), Université de Sherbrooke, Sherbrooke, QC J1K 0A5, Canada
4
Département des Sciences de l’activité Physique, Université du Québec à Montréal, Montreal, QC H2X 1Y4, Canada
5
Centre de Recherche Institut Universitaire de Gériatrie de Montréal, Montreal, QC H3W 1W4, Canada
*
Author to whom correspondence should be addressed.
Academic Editor: Vittorio M. N. Passaro
Received: 18 April 2016 / Revised: 29 June 2016 / Accepted: 29 June 2016 / Published: 5 July 2016
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [2911 KB, uploaded 5 July 2016]   |  

Abstract

Clinical mobility assessment is traditionally performed in laboratories using complex and expensive equipment. The low accessibility to such equipment, combined with the emerging trend to assess mobility in a free-living environment, creates a need for body-worn sensors (e.g., inertial measurement units—IMUs) that are capable of measuring the complexity in motor performance using meaningful measurements, such as joint orientation. However, accuracy of joint orientation estimates using IMUs may be affected by environment, the joint tracked, type of motion performed and velocity. This study investigates a quality control (QC) process to assess the quality of orientation data based on features extracted from the raw inertial sensors’ signals. Joint orientation (trunk, hip, knee, ankle) of twenty participants was acquired by an optical motion capture system and IMUs during a variety of tasks (sit, sit-to-stand transition, walking, turning) performed under varying conditions (speed, environment). An artificial neural network was used to classify good and bad sequences of joint orientation with a sensitivity and a specificity above 83%. This study confirms the possibility to perform QC on IMU joint orientation data based on raw signal features. This innovative QC approach may be of particular interest in a big data context, such as for remote-monitoring of patients’ mobility. View Full-Text
Keywords: AHRS; IMU; MIMU; MARG; inertial sensors; attitude and heading reference system; 3D orientation tracking; joint orientation; artificial neural network; inertial motion capture; quality control AHRS; IMU; MIMU; MARG; inertial sensors; attitude and heading reference system; 3D orientation tracking; joint orientation; artificial neural network; inertial motion capture; quality control
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Lebel, K.; Boissy, P.; Nguyen, H.; Duval, C. Autonomous Quality Control of Joint Orientation Measured with Inertial Sensors. Sensors 2016, 16, 1037.

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