Inertial Measurement Units’ Reliability for Measuring Knee Joint Angle during Road Cycling

We explore the reliability of joint angles in road cycling obtained using inertial measurement units. The considered method relies on 3D accelerometer and gyroscope measurements obtained from two such units, appropriately attached to two adjacent body parts, measuring the angle of the connecting joint. We investigate the effects of applying a simple drift compensation technique and an error-state Kalman filter. We consider the knee joint angle in particular, and conduct two measurement trials, a 5 and a 20 minute one, for seven subjects, in a closed, supervised laboratory environment and use optical motion tracking system measurements as reference. As expected from an adaptive solution, the Kalman filter gives more stable results. The root mean square errors per pedalling cycle are below 3.2°, for both trials and for all subjects, implying that inertial measurement units are not only reliable for short measurements, as is usually assumed, but can be reliably used for longer measurements as well. Considering the accuracy of the results, the presented method can be reasonably extended to open, unsupervised environments and other joint angles. Implementing the presented method supports the development of cheaper and more efficient monitoring equipment, as opposed to using expensive motion tracking systems. Consequently, cyclists can have an affordable way of position tracking, leading to not only better bicycle fitting, but to the avoidance and prevention of certain injuries as well.

[1]  Reed D. Gurchiek,et al.  Evaluation of Error-State Kalman Filter Method for Estimating Human Lower-Limb Kinematics during Various Walking Gaits , 2022, Sensors.

[2]  Sajjad Boorghan Farahan,et al.  9-DOF IMU-Based Attitude and Heading Estimation Using an Extended Kalman Filter with Bias Consideration , 2022, Sensors.

[3]  H. Rouhani,et al.  A Full-State Robust Extended Kalman Filter for Orientation Tracking During Long-Duration Dynamic Tasks Using Magnetic and Inertial Measurement Units , 2021, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[4]  Reed D. Gurchiek,et al.  Error-state Kalman filter for lower-limb kinematic estimation: Evaluation on a 3-body model , 2021, PloS one.

[5]  Noel C. Perkins,et al.  Robust Error-State Kalman Filter for Estimating IMU Orientation , 2021, IEEE Sensors Journal.

[6]  Fernando López-Peña,et al.  A Kalman Filter for Nonlinear Attitude Estimation Using Time Variable Matrices and Quaternions , 2020, Sensors.

[7]  Bruno Watier,et al.  Cycling Biomechanics and Its Relationship to Performance , 2020, Applied Sciences.

[8]  Sašo Tomažič,et al.  Computationally Efficient 3D Orientation Tracking Using Gyroscope Measurements , 2020, Sensors.

[9]  Hiroki Yokota,et al.  Framework for visual-feedback training based on a modified self-organizing map to imitate complex motion , 2020, Proceedings of the Institution of Mechanical Engineers, Part P: Journal of Sports Engineering and Technology.

[10]  Jeroen Swart,et al.  Cycling Biomechanics Optimization-the (R) Evolution of Bicycle Fitting. , 2019, Current sports medicine reports.

[11]  Luis Benages Pardo,et al.  Detection of Tennis Activities with Wearable Sensors , 2019, Sensors.

[12]  João Gama,et al.  Human Activity Recognition Using Inertial Sensors in a Smartphone: An Overview , 2019, Sensors.

[13]  Nicolas Bideau,et al.  Estimation of 3D Knee Joint Angles during Cycling Using Inertial Sensors: Accuracy of a Novel Sensor-to-Segment Calibration Procedure Based on Pedaling Motion , 2019, Sensors.

[14]  Aaron Martínez,et al.  Development of an Automatic Alpine Skiing Turn Detection Algorithm Based on a Simple Sensor Setup , 2019, Sensors.

[15]  A. Umek,et al.  Smart sport equipment: SmartSki prototype for biofeedback applications in skiing , 2018, Personal and Ubiquitous Computing.

[16]  Alessio Vecchio,et al.  Real-Time Identification Using Gait Pattern Analysis on a Standalone Wearable Accelerometer , 2017, Comput. J..

[17]  G. Tack,et al.  Differences in the Joint Movements and Muscle Activities of Novice according to Cycle Pedal Type , 2016 .

[18]  Matteo Gadaleta,et al.  IDNet: Smartphone-based Gait Recognition with Convolutional Neural Networks , 2016, Pattern Recognit..

[19]  Paul J. M. Havinga,et al.  Complex Human Activity Recognition Using Smartphone and Wrist-Worn Motion Sensors , 2016, Sensors.

[20]  Matjaz B. Juric,et al.  Inertial Sensor-Based Gait Recognition: A Review , 2015, Sensors.

[21]  Kamiar Aminian,et al.  A Bayesian approach for pervasive estimation of breaststroke velocity using a wearable IMU , 2015, Pervasive Mob. Comput..

[22]  Gregory J. Pottie,et al.  Integrated Inertial Sensors and Mobile Computing for Real-Time Cycling Performance Guidance via Pedaling Profile Classification , 2015, IEEE Journal of Biomedical and Health Informatics.

[23]  Saso Tomazic,et al.  Early Improper Motion Detection in Golf Swings Using Wearable Motion Sensors: The First Approach , 2013, Sensors.

[24]  Jung-Keun Lee,et al.  Estimation of Attitude and External Acceleration Using Inertial Sensor Measurement During Various Dynamic Conditions , 2012, IEEE Transactions on Instrumentation and Measurement.

[25]  James C. Martin,et al.  Joint-specific power production during submaximal and maximal cycling. , 2011, Medicine and science in sports and exercise.

[26]  Angelo M. Sabatini,et al.  Kalman-Filter-Based Orientation Determination Using Inertial/Magnetic Sensors: Observability Analysis and Performance Evaluation , 2011, Sensors.

[27]  Saso Tomazic,et al.  Angle Estimation of Simultaneous Orthogonal Rotations from 3D Gyroscope Measurements , 2011, Sensors.

[28]  Patria A Hume,et al.  Effects of Bicycle Saddle Height on Knee Injury Risk and Cycling Performance , 2011, Sports medicine.

[29]  Nejc Sarabon,et al.  Biomechanics of Cycling , 2010 .

[30]  Huosheng Hu,et al.  Reducing Drifts in the Inertial Measurements of Wrist and Elbow Positions , 2010, IEEE Transactions on Instrumentation and Measurement.

[31]  Michael J. Agnew,et al.  Accuracy of inertial motion sensors in static, quasistatic, and complex dynamic motion. , 2009, Journal of biomechanical engineering.

[32]  C. Hautier,et al.  EMG normalization to study muscle activation in cycling. , 2008, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

[33]  W. Peveler,et al.  Effects of Saddle Height on Economy in Cycling , 2008, Journal of strength and conditioning research.

[34]  M A Brodie,et al.  Dynamic accuracy of inertial measurement units during simple pendulum motion , 2008, Computer methods in biomechanics and biomedical engineering.

[35]  Paul Lukowicz,et al.  Gesture spotting with body-worn inertial sensors to detect user activities , 2008, Pattern Recognit..

[36]  D. Sanderson,et al.  Gastrocnemius and soleus muscle length, velocity, and EMG responses to changes in pedalling cadence. , 2006, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

[37]  Peter H. Veltink,et al.  Measuring orientation of human body segments using miniature gyroscopes and accelerometers , 2005, Medical and Biological Engineering and Computing.

[38]  Cheryl A. Wozniak Timmer Cycling biomechanics: a literature review. , 1991 .

[39]  Steven A. Kautz,et al.  The Pedaling Technique of Elite Endurance Cyclists: Changes with Increasing Workload at Constant Cadence , 1991 .

[40]  G. Németh,et al.  Joint Motions of the Lower Limb during Ergometer Cycling. , 1988, The Journal of orthopaedic and sports physical therapy.

[41]  Halil Ersin Soken,et al.  Robust Attitude Estimation Using IMU-Only Measurements , 2021, IEEE Transactions on Instrumentation and Measurement.

[42]  R. Bini,et al.  A comparison of static and dynamic measures of lower limb joint angles in cycling: application to bicycle fitting , 2016 .