On using the Microsoft Kinect$^{\rm TM}$ sensors in the analysis of human motion

TL;DR

This paper develops a methodology for analyzing human motion using Microsoft Kinect sensors, enabling comparison with marker-based systems and adaptable to other motion capture technologies.

Contribution

It introduces a transformation process to compare Kinect data with marker-based systems and proposes a waveform-based similarity assessment method.

Findings

Kinect sensors can reliably capture gait cycle variations.

The methodology allows for comparative analysis of different motion capture systems.

Attention is needed for systematic effects in knee and lower leg measurements.

Abstract

The present paper aims at providing the theoretical background required for investigating the use of the Microsoft Kinect$^{\rm TM}$ (`Kinect', for short) sensors (original and upgraded) in the analysis of human motion. Our methodology is developed in such a way that its application be easily adaptable to comparative studies of other systems used in capturing human-motion data. Our future plans include the application of this methodology to two situations: first, in a comparative study of the performance of the two Kinect sensors; second, in pursuing their validation on the basis of comparisons with a marker-based system (MBS). One important feature in our approach is the transformation of the MBS output into Kinect-output format, thus enabling the analysis of the measurements, obtained from different systems, with the same software application, i.e., the one we use in the analysis of Kinect-captured data; one example of such a transformation, for one popular marker-placement scheme (`Plug-in Gait'), is detailed. We propose that the similarity of the output, obtained from the different systems, be assessed on the basis of the comparison of a number of waveforms, representing the variation within the gait cycle of quantities which are commonly used in the modelling of the human motion. The data acquisition may involve commercially-available treadmills and a number of velocity settings: for instance, walking-motion data may be acquired at $5$ km/h, running-motion data at $8$ and $11$ km/h. We recommend that particular attention be called to systematic effects associated with the subject's knee and lower leg, as well as to the ability of the Kinect sensors in reliably capturing the details in the asymmetry of the motion for the left and right parts of the human body. The previous versions of the study have been withdrawn due to the use of a non-representative database.