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The trick is to be able to record only the light from that spot as it is illuminated, rather than try to pick out the spot from the entire bright scene. “Even though we’re not sending a huge amount of photons, at short time scales, we’re sending a lot more energy to that spot than the energy sent by the sun,” he explains. A new depth-sensing camera technology is able to sense the shape of a lit CFL bulb (left) that would create blinding glare for a conventional camera (right).īut as a projector scans a laser across the scene, the spots illuminated by the laser beam are brighter, if only briefly, notes Kyros Kutulakos, a professor of computer science at the University of Toronto. The problem is that these devices use compact projectors that operate at low power, so their faint patterns are washed out and undetectable when the camera captures ambient light from a scene. Depending on how these patterns are deformed or how much time it takes light to reflect back to the camera, it is possible to calculate the 3D contours of the scene. Here’s the trickĭepth cameras work by projecting a pattern of dots or lines over a scene. It also could be readily incorporated into most smartphones. This combination of features could make this imaging technology suitable for many applications, including medical imaging, inspection of shiny parts, and sensing for robots used to explore the moon and planets. This not only makes it possible for the camera to work under extremely bright light or amidst highly reflected or diffused light-it can capture the shape of a lightbulb that has been turned on, for instance, and see through smoke-but also makes it extremely energy efficient. One prototype based on this model synchronizes a laser projector with a common rolling-shutter camera-the type of camera used in most smartphones-so that the camera detects light only from points being illuminated by the laser as it scans across the scene. This is all done by the sensor.” Very energy efficient “We don’t need new image-processing algorithms, and we don’t need extra processing to eliminate the noise, because we don’t collect the noise. “We have a way of choosing the light rays we want to capture and only those rays,” says Srinivasa Narasimhan, associate professor of robotics at Carnegie Mellon University. Researchers who developed the new imaging system created a mathematical model that allows the camera and its light source to work together efficiently, eliminating extraneous light, or “noise,” that would otherwise wash out the signals needed to detect a scene’s contours. The key is to gather only the bits of light the camera actually needs. One of the important application of this system in assistive robotics is to generate the trajectory of a lower limb exoskeleton device.A new imaging technology could address a major drawback of depth-sensing cameras, such as Microsoft’s Kinect controller: the inability to work in bright light, especially sunlight. Based on the experimental results the accuracy and correlation of various speed of motion for the Kinect v1 sensor is evaluated.
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The results obtained are verified by taking into account the angle data of five prominent lower limb sections during joint movement. Finally, the accuracy of the Kinect camera at different speed of range is estimated by making a study over a group of ten human subjects with normal gait pattern in a treadmill setup. It provides a comparative study between the Kinect’s extracted gait data and a twin axis Goniometer data. Further the moving average filter is applied to smooth the dataset to obtain a better motion. Using the raw data from Kinect camera a motion file is generated for simulating the musculoskeletal model of the OpenSim software. This paper proposes a technique to record the joint coordinates of a lower extremity of a human being using a Microsoft Kinect camera which is quite reliable with respect to a wearable sensor like Goniometer. With the increasing advancement of Gait analysis with marker-less skeletal tracking system, the Microsoft Kinect camera is becoming increasingly popular.