Inertial-Based Human Motion Capture: A Technical Summary of Current Processing Methodologies for Spatiotemporal and Kinematic Measures

Abstract

Inertial-based motion capture (IMC) has been suggested to overcome many of the limitations of traditional motion capture systems. The validity of IMC is, however, suggested to be dependent on the methodologies used to process the raw data collected by the inertial device. The aim of this technical summary is to provide researchers and developers with a starting point from which to further develop the current IMC data processing methodologies used to estimate human spatiotemporal and kinematic measures. The main workflow pertaining to the estimation of spatiotemporal and kinematic measures was presented, and a general overview of previous methodologies used for each stage of data processing was provided. For the estimation of spatiotemporal measures, which includes stride length, stride rate, and stance/swing duration, measurement thresholding and zero-velocity update approaches were discussed as the most common methodologies used to estimate such measures. The methodologies used for the estimation of joint kinematics were found to be broad, with the combination of Kalman filtering or complimentary filtering and various sensor to segment alignment techniques including anatomical alignment, static calibration, and functional calibration methods identified as being most common. The effect of soft tissue artefacts, device placement, biomechanical modelling methods, and ferromagnetic interference within the environment, on the accuracy and validity of IMC, was also discussed. Where a range of methods have previously been used to estimate human spatiotemporal and kinematic measures, further development is required to reduce estimation errors, improve the validity of spatiotemporal and kinematic estimations, and standardize data processing practices. It is anticipated that this technical summary will reduce the time researchers and developers require to establish the fundamental methodological components of IMC prior to commencing further development of IMC methodologies, thus increasing the rate of development and utilisation of IMC.

Scenario-Based Sensed Human Motion Editing and Validation Through the Motion-Sphere

“…(e.g., Xsens, Perception Neuron, and Vicon) used to track and record human actions require a controlled environment, pre and post processing operations, and countermeasures to reduce sensor drift errors [3], [4]. The 3D character animation software (e.g., 3ds Max and MotionBuilder) used to synthesize human motion, requires proficient skills and tedious efforts.…”

Measuring Spatiotemporal Parameters on Treadmill Walking Using Wearable Inertial System

“…The study of the validity and reliability of IGA instruments is an essential asset that must be considered in clinical practice. This especially in the case of a new IGA instrument such as a wearable inertial mocap system that is not considered the gold standard as an optical system [ 16 , 33 ]. In fact, according to Muro-de-la-Herran [ 11 ] and Najafi et al [ 34 ], the difference of using wearable respects not wearable sensors as optical systems is the possibility of evaluating the subject in a non-confined lab on different terrain and distances promoting patient’s autonomy and active role enhancing usability.…”

“…Moreover, study these technologies is also important for mathematicians, designers and engineers. Design and technical issues can also affect the accuracy of these technologies [ 33 , 36 , 37 , 38 ].…”

D-H Parameter Method-based Wearable Motion Tracking

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