Pixel-based Skin Detection Based on Statistical Models

Skin detection is a preliminary step in many machine vision applications. In this paper, we propose applying the Gamma, Beta, and Laplace distributions for modelling skin color pixels in arbitrary chromaticity spaces used for parametric skin detection. Since the proposed distributions do not inherently consider the correlation between the chromaticity components, a method to eliminate the correlation between the skin chrominance information is also proposed. This enables skin modelling without concerning about the data correlation. We model the skin color pixels by applying the proposed distributions in five different color spaces. The Compaq dataset was used for evaluating the performance of the proposed method. The accuracy of skin detection on the Compaq data set was 88% and showed improvement compared to previous statistical method

Kinect joint dataset

Joint datase

Evaluation of angle representation techniques for measuring shoulder and elbow ROM using Kinect V2 data.

Records of shoulder abduction/adduction/felxion/extension/horizontal abduction/horizontal adduction, elbow flexion/extension<div>for evaluation of angle representation techniques. </div

Vision based online exercise monitoring system with visual feedback for stroke rehabilitation therapy

<div>This is the material used for implementing and testing the system proposed in :</div><div>"Vision based online exercise monitoring system with visual feedback for stroke rehabilitation therapy"</div><div><br></div

Indoor-Outdoor dataset

The data set used in paper  "Indoor-outdoor image classification using dichromatic reflection model and Haralick features" ,  author:A. Nadian-Ghomsheh

Stability of Kinect for range of motion analysis in static stretching exercises.

Physical rehabilitation aims people with physical impairments to enhance and restore their functional ability. The Microsoft Kinect v1 and v2 technologies apply depth information and machine vision techniques to generate 3D coordinates of a set of anatomical landmarks on the human body regarded as Kinect joints. Trigonometry relationship between Kinect joints can be used to extract body Range of Motion (ROM). The purpose of this study was to evaluate stability of Kinect for ROM measurement during static stretching exercises. According to the literature, the stability of Kinect in static exercises has been reported to a limited extent. 13 healthy men participated in this study and performed 5 exercises in 2 different distances from the cameras. Exercises were recorded by Kinect v1 and Kinect v2, concurrently. The stability of Kinect was also evaluated for 5 ROMs including: elbow flexion, shoulder abduction, wrist pronation, wrist flexion, and wrist ulnar deviation. Maximum and average joint displacement errors were used for stability analysis. Results showed that Kinect v2 is more stable compared to Kinect v1. Kinect v2 joints showed displacement error of more than 15 mm for wrist. For the other joints, Kinect showed an average displacement error of less than 10 mm

Body tridimensional planes.

<p>Body tridimensional planes.</p

Stability of Kinect for range of motion analysis in static stretching exercises - Fig 4

<p>Illustration of (A) wrist pronation, (B) flexion, and (C) ulnar deviations. Formulation for extracting each deviation is illustrated respectively.</p

Position and name of Kinect joints.

<p>L, R and C represent Right, Left and Central joints, respectively.</p