An automated method to extract three-dimensional position data using an infrared time-of-flight camera

Traditional motion capture systems can be prohibitive in healthcare settings from time, cost, space and user-expertise perspectives. Ideally, movement analysis technologies for healthcare should be low-cost, quick, simple and usable in small spaces. This study demonstrates a simple, low-cost and close-range time-of-flight depth-camera system, for automatic gait analysis. A method to automatically track three-dimensional position and orientation of retro-reflective marker-triads in real-time was developed. A marker-triad was applied to a participant (self-selected walking pace): thigh angle (wrt. global-vertical) was calculated. Trials were concurrently recorded using a motion capture system. Root-mean-square error was 2.5°, 1.3° and 2.2° for depth-camera distances of 0.8 m, 1.1 m and 1.4 m respectively. Results indicate that walking distances of 1.1 m are optimal for the current system. Further development and investigation into potential healthcare applications (e.g., low-cost, close-range gait analysis) is warranted

Optical Position Sensor Based on Digital Image Processing: Magnetic Field Mapping Improvement

Optical position measurement system for an automated magnetic field mapping apparatus based on fluxgate sensors is presented. For the exact position estimation of the sensor head, a simple smart camera was developed with respect to minimal hardware configuration and real-time execution of position measurement algorithm. The camera is observing the mapped scene and evaluates position of the sensor head using an active marker. The sensor head is designed as movable, what allows keeping the scene fixed and exactly referenced to the mapped magnetic field using coordinates obtained from image. With image sensor fixed 2.5 m above the plane and range ±130 mm around the lens optical axis (image center), the total position measurement error is less than 0.5 mm

Coded time of flight cameras: sparse deconvolution to address multipath interference and recover time profiles

Time of flight cameras produce real-time range maps at a relatively low cost using continuous wave amplitude modulation and demodulation. However, they are geared to measure range (or phase) for a single reflected bounce of light and suffer from systematic errors due to multipath interference. We re-purpose the conventional time of flight device for a new goal: to recover per-pixel sparse time profiles expressed as a sequence of impulses. With this modification, we show that we can not only address multipath interference but also enable new applications such as recovering depth of near-transparent surfaces, looking through diffusers and creating time-profile movies of sweeping light. Our key idea is to formulate the forward amplitude modulated light propagation as a convolution with custom codes, record samples by introducing a simple sequence of electronic time delays, and perform sparse deconvolution to recover sequences of Diracs that correspond to multipath returns. Applications to computer vision include ranging of near-transparent objects and subsurface imaging through diffusers. Our low cost prototype may lead to new insights regarding forward and inverse problems in light transport.United States. Defense Advanced Research Projects Agency (DARPA Young Faculty Award)Alfred P. Sloan Foundation (Fellowship)Massachusetts Institute of Technology. Media Laboratory. Camera Culture Grou

3D hand posture recognition using multicam

This paper presents the hand posture recognition in 3D using the MultiCam, a monocular 2D/3D camera developed by Center of Sensorsystems (ZESS). The :VlultiCam is a camera which is capable to provide high resolution of color data acquired from CMOS sensors and low resolution of distance (or range) data calculated based on timeof- flight (ToF) technology using Photonic Mixer Device (PMD) sensors. The availability of the distance data allows the hand posture to be recognized in z-axis direction without complex computational algorithms which also enables the program to work in real-time processing as well as eliminates the background effectively. The hand posture recognition will employ a simple but robust algorithm by checking the number of fingers detected around virtually created circle centered at the Center of Mass (CoM) of the hand and therefore classifies the class associated with a particular hand posture. At the end of this paper, the technique that uses intersection between the circle and fingers as the method to classify the hand posture which entails the MultiCam capability is proposed. This technique is able to solve the problem of orientation, size and distance invariants by utilizing the distance data