Validation of a low-cost inertial motion capture system for whole-body motion analysis

Robert-Lachaîne, Xavier; Mecheri, Hakim; Muller, Antoine; Larue, Christian; Plamondon, André

doi:10.1016/j.jbiomech.2019.109520

Cited by 54 publications

(45 citation statements)

References 28 publications

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“…In (4), M is the number of magnetometers and 0 is the initial raw reading of i-th magnetometer. We can see that even though different magnetometers are yet uncalibrated, the data sequence is repeated and reaches a minimum (nearzero) in each rotation, i.e.…”

Section: B Data Collectionmentioning

confidence: 99%

“…These are often packaged as one unit and called Magnetic-Inertial Measurement Unit (MIMU). In many applications like wearable body sensor networks for motion capture or pedestrian navigation, often more than one MIMU units are used in a distributed fashion or as an array [3,4]. The MEMS accelerometer, rate gyro and magnetometer are not only affected by systematic errors of bias, sensitivity, non-orthogonality and misalignment, but also undergo stochastic errors.…”

Section: Introductionmentioning

confidence: 99%

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Simultaneous End User Calibration of Multiple Magnetic Inertial Measurement Units With Associated Uncertainty

et al. 2021

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A fast method to simultaneously calibrate multiple MEMS Magnetic Inertial Measurement Units (MIMUs) accurately in the field is needed in many application areas. The MEMS MIMUs require calibration of systematic errors of bias, sensitivity, non-orthogonality and misalignment, which vary with temperature and use. Even after calibration, the sensors undergo stochastic errors in static and dynamic conditions and thus uncertainty of output must also be modeled. We propose a method for easy and fast calibration of multiple MIMUs together, while mounted on a single platform. The precise alignment of sensors is not assumed. Our method calibrates both fixed array of MEMS MIMUs or many independent MIMUs simultaneously using kinematic constraints. The novelty of our approach is that the uncertainty of sensors output is also learned as part of our model. Compared with existing state-of-art methods, our algorithm gives more consistent readings of all MIMUs and our framework also predicts the associated uncertainty of the sensor output. The uncertainty prediction of individual sensors is particularly helpful in the sensor fusion.

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Section: B Data Collectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Simultaneous End User Calibration of Multiple Magnetic Inertial Measurement Units With Associated Uncertainty

et al. 2021

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“…A six-camera Bonita Vicon 3D OMC system (Vicon Motion Systems Ltd., Oxford, UK), sampling at 100 Hz, was used as the reference for joint ROM and spatiotemporal estimations [41]. The capture volume was approximately 3 m × 2 m × 2 m. Fifteen 14 mm reflective markers were attached to the landmarks reported in Figure 2.…”

Section: Omc Marker Placement and Processingmentioning

confidence: 99%

Validation of Spatiotemporal and Kinematic Measures in Functional Exercises Using a Minimal Modeling Inertial Sensor Methodology

Hindle

Keogh

Lorimer

2020

Sensors

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This study proposes a minimal modeling magnetic, angular rate and gravity (MARG) methodology for assessing spatiotemporal and kinematic measures of functional fitness exercises. Thirteen healthy persons performed repetitions of the squat, box squat, sandbag pickup, shuffle-walk, and bear crawl. Sagittal plane hip, knee, and ankle range of motion (ROM) and stride length, stride time, and stance time measures were compared for the MARG method and an optical motion capture (OMC) system. The root mean square error (RMSE), mean absolute percentage error (MAPE), and Bland–Altman plots and limits of agreement were used to assess agreement between methods. Hip and knee ROM showed good to excellent agreement with the OMC system during the squat, box squat, and sandbag pickup (RMSE: 4.4–9.8°), while ankle ROM agreement ranged from good to unacceptable (RMSE: 2.7–7.2°). Unacceptable hip and knee ROM agreement was observed for the shuffle-walk and bear crawl (RMSE: 3.3–8.6°). The stride length, stride time, and stance time showed good to excellent agreement between methods (MAPE: (3.2 ± 2.8)%–(8.2 ± 7.9)%). Although the proposed MARG-based method is a valid means of assessing spatiotemporal and kinematic measures during various exercises, further development is required to assess the joint kinematics of small ROM, high velocity movements.

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“…The overview of IMU motion-capture technologies mentioned above is given in Table 2. Several studies comparing IMU-based motion-capture systems with optical systems for different regions of the body have been carried out in the literature [3][4][5][6][7][8][9][10]. The focus of our research is a universal, whole-body motion-capture system using wearable IMU sensors.…”

Section: Introductionmentioning

confidence: 99%

Wearable Sensor Clothing for Body Movement Measurement during Physical Activities in Healthcare

Ancāns

Greitāns

Cacurs

et al. 2021

Sensors

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This paper presents a wearable wireless system for measuring human body activities, consisting of small inertial sensor nodes and the main hub for data transmission via Bluetooth for further analysis. Unlike optical and ultrasonic technologies, the proposed solution has no movement restrictions, such as the requirement to stay in the line of sight, and it provides information on the dynamics of the human body’s poses regardless of its location. The problem of the correct placement of sensors on the body is considered, a simplified architecture of the wearable clothing is described, an experimental set-up is developed and tests are performed. The system has been tested by performing several physical exercises and comparing the performance with the commercially available BTS Bioengineering SMART DX motion capture system. The results show that our solution is more suitable for complex exercises as the system based on digital cameras tends to lose some markers. The proposed wearable sensor clothing can be used as a multi-purpose data acquisition device for application-specific data analysis, thus providing an automated tool for scientists and doctors to measure patient’s body movements.

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Validation of a low-cost inertial motion capture system for whole-body motion analysis

Cited by 54 publications

References 28 publications

Simultaneous End User Calibration of Multiple Magnetic Inertial Measurement Units With Associated Uncertainty

Simultaneous End User Calibration of Multiple Magnetic Inertial Measurement Units With Associated Uncertainty

Validation of Spatiotemporal and Kinematic Measures in Functional Exercises Using a Minimal Modeling Inertial Sensor Methodology

Wearable Sensor Clothing for Body Movement Measurement during Physical Activities in Healthcare

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