A least-squares identification algorithm for estimating squat exercise mechanics using a single inertial measurement unit

Bonnet, Vincent; Mazzà, Claudia; Fraisse, Philippe; Cappozzo, Aurelio

doi:10.1016/j.jbiomech.2012.02.014

Cited by 32 publications

(31 citation statements)

References 27 publications

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“…Unfortunately, if the markers' 3D positions are not well estimated, model singularities may occur. To cope with this issue, other inverse kinematics methods, inspired by the robotics field, enabling real-time estimate, and handling redundancy or singularities have been proposed [2]. However, handling occlusion cases, i.e.…”

Section: Optimal Joint Angles Calculationmentioning

confidence: 99%

“…Recently, inertial measurement units (IMUs) became a popular tool to quantify human motion outside the laboratory [1]. However, despite strong advantages in estimating global spatio-temporal parameters [1], the accurate estimate of 3D joint angles (JA) with IMU remains difficult due to non-linear drift occurring when integrating measured accelerations and angular velocities [2]. Consequently, to the best of our knowledge, no system can provide the absolute position of the sensor without a prior calibration phase and a strongly dependent model based approach [1].…”

Section: Introductionmentioning

confidence: 99%

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Toward an affordable and user-friendly visual motion capture system

Bonnet

Sylla

Cherubini

et al. 2014

2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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The present study aims at designing and evaluating a low-cost, simple and portable system for arm joint angle estimation during grasping-like motions. The system is based on a single RGB-D camera and three customized markers. The automatically detected and tracked marker positions were used as inputs to an offline inverse kinematic process based on bio-mechanical constraints to reduce noise effect and handle marker occlusion. The method was validated on 4 subjects with different motions. The joint angles were estimated both with the proposed low-cost system and, a stereophotogrammetric system. Comparative analysis shows good accuracy with high correlation coefficient (r= 0.92) and low average RMS error (3.8 deg).

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Section: Optimal Joint Angles Calculationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Toward an affordable and user-friendly visual motion capture system

Bonnet

Sylla

Cherubini

et al. 2014

2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

View full text Add to dashboard Cite

show abstract

“…While there are some low cost devices for quantifying spatiotemporal parameters of gait (e.g., cadence, step length and symmetry, step and stride duration, etc.) (Bergamini et al, 2013; Salarian et al, 2010; Spain et al, 2012), there are not many low cost instrumentations for kinematic evaluation (Bonnet et al, 2012; Picerno et al, 2008). Further, these devices typically don’t provide real-time data, which may be of particular interest to clinicians due to its application for gait training in virtual environment.…”

Section: Introductionmentioning

confidence: 99%

A low cost real-time motion tracking approach using webcam technology

Krishnan

Washabaugh

Seetharaman

2015

Journal of Biomechanics

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Physical therapy is an important component of gait recovery for individuals with locomotor dysfunction. There is a growing body of evidence that suggests that incorporating a motor learning task through visual feedback of movement trajectory is a useful approach to facilitate therapeutic outcomes. Visual feedback is typically provided by recording the subject’s limb movement patterns using a three-dimensional motion capture system and displaying it in real-time using customized software. However, this approach can seldom be used in the clinic because of the technical expertise required to operate this device and the cost involved in procuring a three-dimensional motion capture system. In this paper, we describe a low cost two-dimensional real-time motion tracking approach using a simple webcam and an image processing algorithm in LabVIEW Vision Assistant. We also evaluated the accuracy of this approach using a high precision robotic device (Lokomat) across various walking speeds. Further, the reliability and feasibility of real-time motion-tracking were evaluated in healthy human participants. The results indicated that the measurements from the webcam tracking approach were reliable and accurate. Experiments on human subjects also showed that participants could utilize the real-time kinematic feedback generated from this device to successfully perform a motor learning task while walking on a treadmill. These findings suggest that the webcam motion tracking approach is a feasible low cost solution to perform real-time movement analysis and training.

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“…González {gonzalezde, hayashibe, fraisse}-at-lirmm.fr the position of each segment to be known. Usually, the segment's orientation is determined by a number of inertial sensors [6], [7] or by means of video analysis used to track a set of markers attached to the subject. This segment based approach to CoM estimation is dependent on the reliability of the anthropometric table, certain errors can occur if the subject does not match the population from which the table originated.…”

Section: Introductionmentioning

confidence: 99%

Online identification and visualization of the statically equivalent serial chain via constrained Kalman filter

González

Hayashibe

Fraisse

2013

2013 IEEE International Conference on Robotics and Automation

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A human's center of mass (CoM) trajectory is useful to evaluate the dynamic stability during daily life activities such as walking and standing up. To estimate the subjectspecific CoM position in the home environment, we make use of a statically equivalent serial chain (SESC) developed with a portable measurement system. In this paper we implement a constrained Kalman filter to achieve an online estimation of the SESC parameters while accounting for the human body's bilateral symmetry. This results in constraining SESC parameters to be consistent with the human skeletal model used. The proposed identification method can inform the subject or the therapist, in real-time, about the quality of the on-going CoM estimation. This information can be helpful to reduce the identification time and establish a personalized protocol. A Kinect is used as a markerless motion capture system for measuring limb orientations while the Wii board is used to measure the subject's center of pressure (CoP) during the identification phase. CoP measurements and Kinect data were recorded for four able-bodied subjects. The recorded data was then given to the proposed recursive algorithm to identify the parameters of the SESC online. A cross-validation test was performed to verify the identification performance. The results for these subjects are shown and discussed.

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A least-squares identification algorithm for estimating squat exercise mechanics using a single inertial measurement unit

Cited by 32 publications

References 27 publications

Toward an affordable and user-friendly visual motion capture system

Toward an affordable and user-friendly visual motion capture system

A low cost real-time motion tracking approach using webcam technology

Online identification and visualization of the statically equivalent serial chain via constrained Kalman filter

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