Using a Kinect sensor to acquire biological motion: Toolbox and evaluation

Abstract

Biological motion (BM) is the movement of animate entities, which conveys rich social information. To obtain pure BM, researchers nowadays predominantly use point-light displays (PLDs), which depict BM through a set of light points (e.g., 12 points) placed at distinct joints of a moving human body. Most prevalent BM stimuli are created by state-of-the-art motion capture systems. Although these stimuli are highly precise, the motion capture system is expensive and bulky, and its process of constructing a PLD-based BM is time-consuming and complex. These factors impede the investigation of BM mechanisms. In this study, we propose a free Kinect-based biological motion capture (KBC) toolbox based on the Kinect Sensor 2.0 in C++. The KBC toolbox aims to help researchers acquire PLD-based BM in an easy, low-cost, and user-friendly way. We conducted three experiments to examine whether KBC-generated BM can genuinely reflect the processing characteristics of BM: (1) Is BM from this source processed globally in vision? (2) Does its BM (e.g., from the feet) retain detailed local information? and (3) Does the BM convey emotional information? We obtained positive results in response to all three questions. Therefore, we think that the KBC toolbox can be useful in generating BM for future research.

It Is Not Just in Faces! Processing of Emotion and Intention from Biological Motion in Psychiatric Disorders

“…Furthermore, a wide variety of PLD tasks is readily available, some of which have already been shown to have satisfactory psychometric values (Kern et al, 2013 ). Finally, Shi et al ( 2017 ) recently presented a Kinect-based method that allows one to produce PLDs without having access to a full motion-capture laboratory. In this way, point-light stimuli can be tailored to the specific needs of a study using low-cost and user-friendly methods.…”

Neural and Neuromimetic Perception: A Comparative Study of Gender Classification from Human Gait

“…The machine-learning-based skeletal motion capture method mentioned in Ref. [46] is used for capturing the PLD representation of the biological motion of the walkers. When compared with the state-of-the-art optical motion tracking methods (such as Vicon [39]), the anatomical landmarks from the Kinect-generated point clouds can be measured with high test-retest reliability, and the differences in the interclass coefficient correlation between Kinect and Vicon are <0.16 [12][13][14].…”

“…The validity of biological motion captured using the Kinect v2 sensor is established in Ref. [46] with human observers through reflexive attentional orientation and extraction of emotional information from the upright and inverted PLDs.…”

Gender Perception From Gait: A Comparison Between Biological, Biomimetic and Non-biomimetic Learning Paradigms

“…The sensor was placed approximately 1.5 m in front of the treadmill with the front board removed to avoid issues with occlusion. The ML based skeletal motion capture method mentioned in Shi et al (2018) is used for capturing the PLD representation of the biological motion of the walkers. When compared with the state-of-art optical motion tracking methods [such as Vicon ( Clark et al, 2012 ; Pfister et al, 2014 )], the anatomical landmarks from the Kinect-generated point clouds can be measured with high test-retest reliability, and the differences in the interclass coefficient correlation between Kinect and Vicon are <0.16 ( Clark et al, 2012 , 2013 ; Pfister et al, 2014 ; van Diest et al, 2014 ).…”

“…Both systems have been shown to effectively capture >90% variance in full-body segment movements during exergaming ( van Diest et al, 2014 ). The validity of biological motion captured using the Kinect v2 sensor is established in Shi et al (2018) with human observers through reflexive attentional orientation and extraction of emotional information from the upright and inverted PLD.…”