Development of a Robust, Simple, and Affordable Human Gait Analysis System Using Bottom-Up Pose Estimation With a Smartphone Camera.

Gait analysis is used in many fields such as Medical Diagnostics, Osteopathic medicine, Comparative and Sports-related biomechanics, etc. The most commonly used system for capturing gait is the advanced video camera-based passive marker system such as VICON. However, such systems are expensive, and reflective markers on subjects can be intrusive and time-consuming. Moreover, the setup of markers for certain rehabilitation patients, such as people with stroke or spinal cord injuries, could be difficult. Recently, some markerless systems were introduced to overcome the challenges of marker-based systems. However, current markerless systems have low accuracy and pose other challenges in gait analysis with people in long clothing, hiding the gait kinematics. The present work attempts to make an affordable, easy-to-use, accurate gait analysis system while addressing all the mentioned issues. The system in this study uses images from a video taken with a smartphone camera (800 × 600 pixels at an average rate of 30 frames per second). The system uses OpenPose, a 2D real-time multi-person keypoint detection technique. The system learns to associate body parts with individuals in the image using Convolutional Neural Networks (CNNs). This bottom-up system achieves high accuracy and real-time performance, regardless of the number of people in the image. The proposed system is called the "OpenPose based Markerless Gait Analysis System" (OMGait). Ankle, knee, and hip flexion/extension angle values were measured using OMGait in 16 healthy volunteers under different lighting and clothing conditions. The measured kinematic values were compared with a standard video camera based normative dataset and data from a markerless MS Kinect system. The mean absolute error value of the joint angles from the proposed system was less than 90 for different lighting conditions and less than 110 for different clothing conditions compared to the normative dataset. The proposed system is adequate in measuring the kinematic values of the ankle, knee, and hip. It also performs better than the markerless systems like MS Kinect that fail to measure the kinematics of ankle, knee, and hip joints under dark and bright light conditions and in subjects with long robe clothing.

Continuous flow real-time PCR device using multi-channel fluorescence excitation and detection.

High throughput automation is greatly enhanced using techniques that employ conveyor belt strategies with un-interrupted streams of flow. We have developed a 'conveyor belt' analog for high throughput real-time quantitative Polymerase Chain Reaction (qPCR) using droplet emulsion technology. We developed a low power, portable device that employs LED and fiber optic fluorescence excitation in conjunction with a continuous flow thermal cycler to achieve multi-channel fluorescence detection for real-time fluorescence measurements. Continuously streaming fluid plugs or droplets pass through tubing wrapped around a two-temperature zone thermal block with each wrap of tubing fluorescently coupled to a 64-channel multi-anode PMT. This work demonstrates real-time qPCR of 0.1-10 µL droplets or fluid plugs over a range of 7 orders of magnitude concentration from 1 × 10(1) to 1 × 10(7). The real-time qPCR analysis allows dynamic range quantification as high as 1 × 10(7) copies per 10 µL reaction, with PCR efficiencies within the range of 90-110% based on serial dilution assays and a limit of detection of 10 copies per rxn. The combined functionality of continuous flow, low power thermal cycling, high throughput sample processing, and real-time qPCR improves the rates at which biological or environmental samples can be continuously sampled and analyzed.

Detection of constituent layers of histological oral sub-mucous fibrosis: images using the hybrid segmentation algorithm.

This paper concentrates on the segmentation of histological images of oral sub-mucous fibrosis (OSF) into its constituent layers. In this regard hybrid segmentation algorithm shows very interesting results. The segmentation results depict the superiority of hybrid segmentation algorithm (HSA) in comparison to region growing algorithm (RGA). In clinical sense, the presented method provides an automatic means for segmenting histological layers (reference class map provided by the expert). The method shows potential in mimicking clinical acumen to act as a support system to oncologist.