A low cost real-time motion tracking approach using webcam technology

Abstract

Physical therapy is an important component of gait recovery for individuals with locomotor dysfunction. There is a growing body of evidence that suggests that incorporating a motor learning task through visual feedback of movement trajectory is a useful approach to facilitate therapeutic outcomes. Visual feedback is typically provided by recording the subject’s limb movement patterns using a three-dimensional motion capture system and displaying it in real-time using customized software. However, this approach can seldom be used in the clinic because of the technical expertise required to operate this device and the cost involved in procuring a three-dimensional motion capture system. In this paper, we describe a low cost two-dimensional real-time motion tracking approach using a simple webcam and an image processing algorithm in LabVIEW Vision Assistant. We also evaluated the accuracy of this approach using a high precision robotic device (Lokomat) across various walking speeds. Further, the reliability and feasibility of real-time motion-tracking were evaluated in healthy human participants. The results indicated that the measurements from the webcam tracking approach were reliable and accurate. Experiments on human subjects also showed that participants could utilize the real-time kinematic feedback generated from this device to successfully perform a motor learning task while walking on a treadmill. These findings suggest that the webcam motion tracking approach is a feasible low cost solution to perform real-time movement analysis and training.

Interlimb transfer of motor skill learning during walking: No evidence for asymmetric transfer

“…The participant then walked on a motorized treadmill (Woodway USA) at 2.0 mph with their hands placed over a custom-built treadmill rail system (Figure 2a). The sagittal plane kinematics of the participant while walking on the treadmill was recorded using a high-definition camera and tracked in real-time using an established algorithm that has been described elsewhere [28]. After a two minute warm-up period, baseline kinematic data were recorded for one minute.…”

“…The paradigm involves a foot trajectory-tracking task that necessitates participants to produce greater hip and knee flexion by coordinating their muscle activation patterns during the swing phase of the gait, and has been used to promote motor recovery after stroke [23–28]. Here, we used this functional paradigm to study the extent of interlimb transfer and test whether there is an asymmetry in the amount of transfer between the dominant and the non-dominant legs.…”

A Novel Application of Eddy Current Braking for Functional Strength Training During Gait

“…EMG data were recorded at 1000 Hz, and the kinematic data were recorded at 30 Hz using a real-time tracking system described elsewhere. 24 Briefly, retroreflective markers placed on the hip, knee, and ankle joints were tracked using an image processing algorithm written in LabVIEW Vision Assistant. A three-point model was then created from the hip, knee, and ankle markers to obtain sagittal plane hip and knee kinematics using the following equations: $${\theta }_{Hip}=\arctan 2\left(\right[x_{knee}-x_{hip}]\mid [y_{hip}-y_{knee}\left]\right)$$ $${\theta }_{Knee}=(90-\text{Hip Angle})-\left(\arctan 2\right([y_{ankle}-y_{knee}]\mid [x_{ankle}-x_{knee}]\left)\right)$$ Where θ Hip (relative to the vertical trunk) and θ Knee represent the anatomical joint angles, x hip , x knee and x ankle represent the x-coordinates, and y hip , y knee and y ankle represent the y-coordinates of the markers over the respective anatomical landmarks.…”

A low-cost system for coil tracking during transcranial magnetic stimulation

“…These included two Logitech HD Pro Webcam C920 (1080p, 30 FPS), a SunPAK 6600DX heavy-duty tripod, a Rigid Industries floodlight, and 19 mm diameter spherical retroreflective markers (B&L Engineering, Santa Ana, CA, USA). A similar configuration using a single camera has previously been developed for 2D motion capture (Krishnan et al, 2015). The cameras were connected to a Windows computer via an USB 2.0 cable.…”