hamed heravi

This study aimed to construct a device that could measure leg length discrepancy (LLD) automatically.

The LLD measure device measures LLD with pelvic-tilt method (Program 1) and weight-based method (Programs 2 & 3). Tests were done in 3 phases. 1: Two examiners using the LLD Measure device made -50 to 75 mm artificial LLD in two healthy subjects measuring the degree of pelvic tilt and the load bearing of lower limbs. 2: Sixteen healthy volunteers were asked to stand on the device to measure LLD with program 2 and then with both knees extended to measure LLD with program one. 3: 32 patients who had underwent lower limbs CT scanogram enrolled, and the LLD measurement with program 1 compared with those obtained by CT scanogram.

Data’s obtained in the first phase showed excellent repeatability (intra-class correlation coefficient [ICC] > 0.9) and very good reproducibility (ICC > 80%) except for measuring the limb load while both knees were extended (ICC ≈ 60%). In the second phase, we found no statistically significant difference between measuring LLD using programs 1 and 2 (P = 0.49). In the third phase, there was no statistically significant difference between measuring LLD using program 1 and CT scanogram (P = 0.80).

We have developed a device to measure LLD semiautomatic with less need for examiner expertise. The results of our new device would be reliable and accurate compared to CT measurements.

Body vision is a novel method which examines postural indices through photogrammetric essentials. Nevertheless, its reliability and validity has not been appraised till now. We aimed to evaluate the reliability and validity of body vision system for posture assessment

This was a cross sectional study in which two examiners evaluated photographs of 71 subject using body vision system twice with two-week interval. The Body Vision system involves a Grid wall and a camera fixed in front of the grid wall at about 390 cm distances. Three standing photographs (anterior, right lateral, and posterior view) were captured for participants.

The results for inter-rater reliability analysis showed most of the parameters (74%) had excellent 95% Confidence Interval (CI), 10 % had good to excellent 95% CI, 13% had moderate to good 95% CI, and 1% had poor to moderate 95% CI (Table 2). The results for intra-rater reliability analysis showed 70-72% of the parameters had excellent 95% Confidence Interval (CI), 6-9% had good to excellent 95% CI, 12-13% had moderate to good 95% CI, and 9% had poor to moderate 95% CI. The comparison between known distances and angles on grid wall and those obtained from photogrammetric measurements showed there is no statistical significant difference (p > 0.05). Also the regression analysis showed there is a significant and positive relationship between them (R2 = 1, p < 0.05).

The results of this study showed that body vision system is a valid and reliable tool for measuring postural parameters.

Gait contains important information about the status of the human body and physiological signs. In many medical applications, it is important to monitor and accurately analyze the gait of the patient. Since walking shows the reproducibility signs in several phases, separating these phases can be used for the gait analysis. In this study, a method based on image processing for extracting phases of human gait from RGB-Depth images is presented. The sequence of depth images from the front view has been processed to extract the lower body depth profile and distance features. Feature vector extracted from image is the same as observation vector of hidden Markov model, and the phases of gait are considered as hidden states of the model. After training the model using the images which are randomly selected as training samples, the phase estimation of gait becomes possible using the model. The results confirm the rate of 60–40% of two major phases of the gait and also the mid-stance phase is recognized with 85% precision.

The main purpose of this paper is to introduce afast method for gene prediction in DNA sequences based on the period-3 property in exons. First, the symbolic DNA sequences are converted to digital signal using the EIIP method. Then, to reduce the effect of background noise in the period-3 spectrum, we use the Discrete Wavelet Transform (DWT) at three levels and apply it on the input digital signal. Finally, the Goertzel algorithm is used to extract period-3 components in the filtered DNA sequence. The proposed algorithm leads to increase the speed of process and therefor reduce the computational complexity. Detection of small size exons in DNA sequences, exactly, is another advantage of our algorithm. The proposed algorithm ability in exon prediction is compared with several existing methods at the nucleotide level using: (i) specificity - sensitivity values; (ii) Receiver Operating Curves (ROC); and (iii) area under ROC curve. Simulation results show that our algorithm increases the accuracy of exon detection relative to other methods for exon prediction.