Melissa Lacroix
Research Subjects
Research Interests
Melissa Lacroix has a total of 11 co-authors. Here are the top co-authors by number of shared publications:
- Drew Commandeur
Motion and Mobility Laboratory, University of Victoria, Victoria, BC V8P 5C2, Canada
2 shared publications - Joel Greenshields
Canadian Sport Institute Ontario, Toronto, ON M1C 0C7, Canada
2 shared publications - Marc Klimstra
Motion and Mobility Laboratory, University of Victoria, Victoria, BC V8P 5C2, Canada
2 shared publications - Ming-Chang Tsai
Canadian Sport Institute Pacific, Victoria, BC V9E 2C5, Canada
2 shared publications - Patrick Cormier
Canadian Sport Institute Pacific, Victoria, British Columbia, Canada
2 shared publications - Daniel Geneau
School of Exercise Science, Physical and Health Education, University of Victoria, Victoria, BC V8W 3P2, Canada
1 shared publication
Latest Publications
- Open Access
Wheelchair Rugby Sprint Force-Velocity Modeling Using Inertial Measurement Units and Sport Specific Parameters: A Proof of Concept
Background: Para-sports such as wheelchair rugby have seen increased use of inertial measurement units (IMU) to measure wheelchair mobility. The accessibility and accuracy of IMUs have enabled the quantification of many wheelchair metrics and the ability to further advance analyses such as force-velocity (FV) profiling. However, the FV modeling approach has not been refined to include wheelchair specific parameters. Purpose: The purpose of this study was to compare wheelchair rugby sprint FV profiles, developed from a wheel-mounted IMU, using current mono-exponential modeling techniques against a dynamic resistive force model with wheelchair specific resistance coefficients. Methods: Eighteen athletes from a national wheelchair rugby program performed 2 × 45 m all-out sprints on an indoor hardwood court surface. Results: Velocity modelling displayed high agreeability, with an average RMSE of 0.235 ± 0.07 m/s−1 and r2 of 0.946 ± 0.02. Further, the wheelchair specific resistive force model resulted in greater force and power outcomes, better aligning with previously collected measures. Conclusions: The present study highlights the proof of concept that a wheel-mounted IMU combined with wheelchair-specific FV modelling provided estimates of force and power that better account for the resistive forces encountered by wheelchair rugby athletes.
Open AccessWheelchair Rugby Sprint Force-Velocity Modeling Using Inertial Measurement Units and Sport Specific Parameters: A Proof of Concept
Background: Para-sports such as wheelchair rugby have seen increased use of inertial measurement units (IMU) to measure wheelchair mobility. The accessibility and accuracy of IMUs have enabled the quantification of many wheelchair metrics and the ability to further advance analyses such as force-velocity (FV) profiling. However, the FV modeling approach has not been refined to include wheelchair specific parameters. Purpose: The purpose of this study was to compare wheelchair rugby sprint FV profiles, developed from a wheel-mounted IMU, using current mono-exponential modeling techniques against a dynamic resistive force model with wheelchair specific resistance coefficients. Methods: Eighteen athletes from a national wheelchair rugby program performed 2 × 45 m all-out sprints on an indoor hardwood court surface. Results: Velocity modelling displayed high agreeability, with an average RMSE of 0.235 ± 0.07 m/s−1 and r2 of 0.946 ± 0.02. Further, the wheelchair specific resistive force model resulted in greater force and power outcomes, better aligning with previously collected measures. Conclusions: The present study highlights the proof of concept that a wheel-mounted IMU combined with wheelchair-specific FV modelling provided estimates of force and power that better account for the resistive forces encountered by wheelchair rugby athletes.
- Open Access
A Simple and Valid Method to Calculate Wheelchair Frame Rotation Using One Wheel-Mounted IMU
Wheelchair sports have been using Inertial Measurement Units (IMU) to measure mobility metrics during training, testing and competition. Presently, the most suitable solution to calculate wheelchair speed and frame rotation is the 3IMU method as there is uncertainty about the ability of a one wheel-mounted IMU (1IMU) approach to calculate wheelchair frame rotational kinematics. A new method for calculating wheelchair frame rotational kinematics using a single wheel-mounted IMU is presented and compared to a criterion measurement using a wheelchair-frame-mounted IMU. Goodness-of-fit statistics demonstrate very strong linear relationships between wheelchair frame angular velocity calculated from the wheel-mounted IMUs and a wheelchair-frame-mounted IMU. Root mean square error (RMSE), mean absolute error (MAE) and Bland–Altman analysis show very small differences between the wheelchair frame angular velocity calculated from the wheel-mounted IMUs and the wheelchair-frame-mounted IMU. This study has demonstrated a simple and accurate approach to estimating wheelchair frame rotation using one wheel-mounted IMU during an elite wheelchair athlete agility task. Future research is needed to reexamine and compare wheelchair mobility metrics determined using the 3IMU and 1IMU solutions using this new approach.
Open AccessA Simple and Valid Method to Calculate Wheelchair Frame Rotation Using One Wheel-Mounted IMU
Wheelchair sports have been using Inertial Measurement Units (IMU) to measure mobility metrics during training, testing and competition. Presently, the most suitable solution to calculate wheelchair speed and frame rotation is the 3IMU method as there is uncertainty about the ability of a one wheel-mounted IMU (1IMU) approach to calculate wheelchair frame rotational kinematics. A new method for calculating wheelchair frame rotational kinematics using a single wheel-mounted IMU is presented and compared to a criterion measurement using a wheelchair-frame-mounted IMU. Goodness-of-fit statistics demonstrate very strong linear relationships between wheelchair frame angular velocity calculated from the wheel-mounted IMUs and a wheelchair-frame-mounted IMU. Root mean square error (RMSE), mean absolute error (MAE) and Bland–Altman analysis show very small differences between the wheelchair frame angular velocity calculated from the wheel-mounted IMUs and the wheelchair-frame-mounted IMU. This study has demonstrated a simple and accurate approach to estimating wheelchair frame rotation using one wheel-mounted IMU during an elite wheelchair athlete agility task. Future research is needed to reexamine and compare wheelchair mobility metrics determined using the 3IMU and 1IMU solutions using this new approach.