Characterizing the Impact of Sampling Rate and Filter Design on the Morphology of Lower Limb Angular Velocities

The angular velocity of lower limb segments during walking is useful for calculating temporal–spatial parameters and joint kinematics in the inertial measurement unit (IMU)-based gait analysis. While many IMU-based methods for gait analysis have been proposed, configurations of data acquisition and signal conditioning parameter have not been standardized across studies. This study examined the effect of sampling rate and filter design on the characteristic angular velocity waveform obtained from IMUs donned on the lower limbs. Specifically, the frequency analysis of this waveform was performed, and the difference in timing and magnitude of the signal at gait events was evaluated relative to the initial signal. A minimum sampling rate of 35 Hz and a lowpass filter (LPF) cutoff of greater than 10 Hz were found to be appropriate parameters for acquisition and conditioning of the lower limb angular velocity signals. The required low minimum sampling rate suggests that gyroscope data are appropriate for use in bandwidth and battery life limited mobile health applications. Our results also showed that while the commonly employed LPF cutoffs of 5 Hz or less may be appropriate for mid-stance detection, they were not optimal for calculation of heel-strike, toe-off, mid-swing, or angular displacement, obtained with heel-strike and toe-off as delimiters.

[1]  J. Smeathers.  Transient Vibrations Caused by Heel Strike , 1989, Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine.

[2]  M H Granat,et al.  Virtual artificial sensor technique for functional electrical stimulation. , 1998, Medical engineering & physics.

[3]  M H Granat,et al.  A practical gait analysis system using gyroscopes. , 1999, Medical engineering & physics.

[4]  Kamiar Aminian,et al.  Spatio-temporal parameters of gait measured by an ambulatory system using miniature gyroscopes. , 2002, Journal of biomechanics.

[5]  K. Aminian,et al.  Evaluation of an ambulatory system for gait analysis in hip osteoarthritis and after total hip replacement. , 2004, Gait & posture.

[6]  K. Aminiana,et al.  Evaluation of an ambulatory system for gait analysis in hip osteoarthritis and after total hip replacement , 2004 .

[7]  Andreas Krause,et al.  Trading off prediction accuracy and power consumption for context-aware wearable computing , 2005, Ninth IEEE International Symposium on Wearable Computers (ISWC'05).

[8]  Angelo M. Sabatini,et al.  Assessment of walking features from foot inertial sensing , 2005, IEEE Transactions on Biomedical Engineering.

[9]  J. Allum,et al.  Gait event detection using linear accelerometers or angular velocity transducers in able-bodied and spinal-cord injured individuals. , 2006, Gait & posture.

[10]  B M Jolles,et al.  Functional calibration procedure for 3D knee joint angle description using inertial sensors. , 2009, Journal of biomechanics.

[11]  Diana Hodgins,et al.  Inertial sensor-based knee flexion/extension angle estimation. , 2009, Journal of biomechanics.

[12]  Aviral Shrivastava,et al.  Power-accuracy tradeoffs in human activity transition detection , 2010, 2010 Design, Automation & Test in Europe Conference & Exhibition (DATE 2010).

[13]  B. Galna,et al.  Quantification of soft tissue artifact in lower limb human motion analysis: a systematic review. , 2010, Gait & posture.

[14]  Q Li,et al.  Walking speed estimation using a shank-mounted inertial measurement unit. , 2010, Journal of biomechanics.

[15]  Adrian Burns,et al.  An adaptive gyroscope-based algorithm for temporal gait analysis , 2010, Medical & Biological Engineering & Computing.

[16]  Qingguo Li,et al.  A concurrent comparison of inertia sensor-based walking speed estimation methods , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[17]  Jung-Keun Lee,et al.  Quasi real-time gait event detection using shank-attached gyroscopes , 2011, Medical & Biological Engineering & Computing.

[18]  Bijan Najafi,et al.  Laboratory in a box: Wearable sensors and its advantages for gait analysis , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[19]  Thomas Seel,et al.  Joint axis and position estimation from inertial measurement data by exploiting kinematic constraints , 2012, 2012 IEEE International Conference on Control Applications.

[20]  Edwin van Asseldonk,et al.  Use of Inertial Sensors for Ambulatory Assessment of Center-of-Mass Displacements During Walking , 2012, IEEE Transactions on Biomedical Engineering.

[21]  Qingguo Li,et al.  Inertial sensors in estimating walking speed and inclination: an evaluation of sensor error models , 2012, Medical & Biological Engineering & Computing.

[22]  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.

[23]  Matthew Keally,et al.  AdaSense: Adapting sampling rates for activity recognition in Body Sensor Networks , 2013, 2013 IEEE 19th Real-Time and Embedded Technology and Applications Symposium (RTAS).

[24]  Thomas Seel,et al.  IMU-Based Joint Angle Measurement for Gait Analysis , 2014, Sensors.

[25]  Jeffrey M. Hausdorff,et al.  Estimation of step-by-step spatio-temporal parameters of normal and impaired gait using shank-mounted magneto-inertial sensors: application to elderly, hemiparetic, parkinsonian and choreic gait , 2014, Journal of NeuroEngineering and Rehabilitation.

[26]  Dympna O'Sullivan,et al.  Real-world Gyroscope-based Gait Event Detection and Gait Feature Extraction , 2014, eTELEMED 2014.

[27]  Angelo M. Sabatini,et al.  How Angular Velocity Features and Different Gyroscope Noise Types Interact and Determine Orientation Estimation Accuracy , 2015, Sensors.

[28]  Morris Vanegas,et al.  Characterization of inertial measurement unit placement on the human body upon repeated donnings , 2015, 2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN).

[29]  Christine Azevedo Coste,et al.  Implementation and Validation of a Stride Length Estimation Algorithm, Using a Single Basic Inertial Sensor on Healthy Subjects and Patients Suffering from Parkinson’s Disease , 2015 .

[30]  Annika Plate,et al.  Gait recording with inertial sensors--How to determine initial and terminal contact. , 2016, Journal of biomechanics.

[31]  E. Allseits,et al.  The development and concurrent validity of a real-time algorithm for temporal gait analysis using inertial measurement units. , 2017, Journal of biomechanics.

[32]  R. Gailey,et al.  Measurement of lower limb segmental excursion using inertial sensors during single limb stance. , 2018, Journal of Biomechanics.

[33]  E Allseits,et al.  A practical step length algorithm using lower limb angular velocities. , 2018, Journal of biomechanics.