Autonomous Quality Control of Joint Orientation Measured with Inertial Sensors
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 u...
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| Format: | Article |
| Language: | English |
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MDPI AG
2016-07-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/16/7/1037 |
| _version_ | 1811279954553339904 |
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| author | Karina Lebel Patrick Boissy Hung Nguyen Christian Duval |
| author_facet | Karina Lebel Patrick Boissy Hung Nguyen Christian Duval |
| author_sort | Karina Lebel |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T01:05:02Z |
| format | Article |
| id | doaj.art-0dbc3f60a17e4078915bb812428056de |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-13T01:05:02Z |
| publishDate | 2016-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-0dbc3f60a17e4078915bb812428056de2022-12-22T03:09:21ZengMDPI AGSensors1424-82202016-07-01167103710.3390/s16071037s16071037Autonomous Quality Control of Joint Orientation Measured with Inertial SensorsKarina Lebel0Patrick Boissy1Hung Nguyen2Christian Duval3Faculty of Medicine and Health Sciences, Orthopedic service, department of surgery, Université de Sherbrooke, Sherbrooke, QC J1H 5N4, CanadaFaculty of Medicine and Health Sciences, Orthopedic service, department of surgery, Université de Sherbrooke, Sherbrooke, QC J1H 5N4, CanadaDépartement des Sciences de l’activité Physique, Université du Québec à Montréal, Montreal, QC H2X 1Y4, CanadaDépartement des Sciences de l’activité Physique, Université du Québec à Montréal, Montreal, QC H2X 1Y4, CanadaClinical 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.http://www.mdpi.com/1424-8220/16/7/1037AHRSIMUMIMUMARGinertial sensorsattitude and heading reference system3D orientation trackingjoint orientationartificial neural networkinertial motion capturequality control |
| spellingShingle | Karina Lebel Patrick Boissy Hung Nguyen Christian Duval Autonomous Quality Control of Joint Orientation Measured with Inertial Sensors Sensors AHRS IMU MIMU MARG inertial sensors attitude and heading reference system 3D orientation tracking joint orientation artificial neural network inertial motion capture quality control |
| title | Autonomous Quality Control of Joint Orientation Measured with Inertial Sensors |
| title_full | Autonomous Quality Control of Joint Orientation Measured with Inertial Sensors |
| title_fullStr | Autonomous Quality Control of Joint Orientation Measured with Inertial Sensors |
| title_full_unstemmed | Autonomous Quality Control of Joint Orientation Measured with Inertial Sensors |
| title_short | Autonomous Quality Control of Joint Orientation Measured with Inertial Sensors |
| title_sort | autonomous quality control of joint orientation measured with inertial sensors |
| topic | AHRS IMU MIMU MARG inertial sensors attitude and heading reference system 3D orientation tracking joint orientation artificial neural network inertial motion capture quality control |
| url | http://www.mdpi.com/1424-8220/16/7/1037 |
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