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The existing machine-vision surface roughness measurement technique extracts relevant evaluation indices from grayscale images without using the strong sensitivity of color information. In addition, most of these measurements use a micro-vision imaging method to measure a small area and cannot make an overall assessment of the workpiece's surface. To address these issues, a method of measuring surface roughness that uses an ordinary light source and a macro-vision perspective to generate a red and green color index for each pixel is proposed in the present study. A comparison test is conducted on a set of test samples before and after surface contamination using the color index and gray-level algebraic averaging, the square of the main component of the Fourier transform in the frequency domain, and the entropy. A strong correlation between the color index and the surface roughness is established; this correlation is not only higher than that of other indices but also present despite contamination and very robust. Verification using a regression model based on a support vector machine proves that the proposed method not only has a simple apparatus and makes measurement easy but also provides high precision and is suitable over a wide measurement range. The impact of the red and green color blocks, the lighting, and the direction of the surface texture on the correlation between the color index and the roughness are also assessed and discussed in this paper.

The purpose of the current study was to design and evaluate the effectiveness of virtual reality training in improving recovery reactions and reducing fall frequency in older adults. Twenty-four older adults were recruited and randomly assigned to two groups (virtual reality training and control). Both groups underwent three sessions including baseline slip, training and transfer of training on slippery surface. Both groups experienced two slips, one during baseline and the other during the transfer of training trial. The training group underwent twelve simulated slips using a visual perturbation induced by tilting a virtual reality scene while walking on the treadmill and the control group performed normal walking during the training session. Kinematic and kinetic data were collected during all the sessions. Results demonstrated a reduced incidence of falls in the training group during the transfer of training trial as compared to the control group. The training group was able to transfer reactive control strategies learned during training to the second slip trial. The reactive adjustments included reduced slip distance. Additionally, gait parameters reflective of gait instability (stride length, step width, variability in stride velocity) reduced after walking in the VR environment for 15–20 min. The results indicated a beneficial effect of the virtual reality training in reducing slip severity and recovery kinematics in healthy older adults.

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