Reynald Hoskinson

Integration of MEMS Inertial and Pressure Sensors for Vertical Trajectory Determination

Integration of a low-cost global positioning system (GPS) with a microelectromechanical system-based inertial measurement unit (MEMS-IMU) is a widely used method that takes advantage of the individual superiority of each system to get a more accurate and robust navigation performance. However, because of poor observations as well as multipath effects, the GPS has low accuracy in the vertical direction. As a result, the navigation accuracy even in an integrated GPS/MEMS-IMU system is more challenged in the vertical direction than the horizontal direction. To overcome this problem, this paper investigates the integration of a MEMS barometric pressure sensor with the MEMS-IMU for vertical position/velocity tracking without the GPS that has applications in sports. A cascaded two-step Kalman filter consisting of separate orientation and position/velocity subsystems is proposed for this integration. Slow human movements in addition to more rapid sport activities such as vertical and step-down jumps can be tracked using the proposed algorithm. The height-tracking performance is benchmarked against a reference camera-based motion-tracking system and an error analysis is performed. The experimental results show that the vertical trajectory tracking error is less than 28.1 cm. For the determination of jump vertical height/drop, the proposed algorithm has an error of less than 5.8 cm.

Light reallocation for high contrast projection using an analog micromirror array

We demonstrate for the first time a proof of concept projector with a secondary array of individually controllable, analog micromirrors added to improve the contrast and peak brightness of conventional projectors. The micromirrors reallocate the light of the projector lamp from the dark parts towards the light parts of the image, before it reaches the primary image modulator. Each element of the analog micromirror array can be tipped/tilted to divert portions of the light from the lamp in two dimensions. By directing these mirrors on an image-dependent basis, we can increase both the peak intensity of the projected image as well as its contrast. In this paper, we describe and analyze the optical design for projectors using this light reallocation approach. We also discuss software algorithms to compute the best light reallocation pattern for a given input image, using the constraints of real hardware. We perform extensive simulations of this process to evaluate image quality and performance characteristics of this process. Finally, we present a first proof-of-concept implementation of this approach using a prototype analog micromirror device.

A cascaded two-step Kalman filter for estimation of human body segment orientation using MEMS-IMU.

Orientation of human body segments is an important quantity in many biomechanical analyses. To get robust and drift-free 3-D orientation, raw data from miniature body worn MEMS-based inertial measurement units (IMU) should be blended in a Kalman filter. Aiming at less computational cost, this work presents a novel cascaded two-step Kalman filter orientation estimation algorithm. Tilt angles are estimated in the first step of the proposed cascaded Kalman filter. The estimated tilt angles are passed to the second step of the filter for yaw angle calculation. The orientation results are benchmarked against the ones from a highly accurate tactical grade IMU. Experimental results reveal that the proposed algorithm provides robust orientation estimation in both kinematically and magnetically disturbed conditions.

High-dynamic range image projection using an auxiliary MEMS mirror array

We introduce a new concept to improve the contrast and peak brightness of conventional data projectors. Our method provides a non-homogenous light source by dynamically directing fractions of the light from the projector lamp before it reaches the display mechanism. This will supply more light to the areas that need it most, at the expense of the darker parts of the image. In effect, this method will produce a low resolution version of the image onto the image-forming element. To manipulate the light in this manner, we propose using an intermediate array of microelectromechanical system (MEMS) mirrors. By directing the light away from the dark parts earlier in the display chain, the amount of light that needs to be blocked will be reduced, thus decreasing the black level of the final image. Moreover, the ability to dynamically allocate more light to the bright parts of the image will allow for peak brightness higher than the average maximum brightness of display.

Fitness activity classification by using multiclass support vector machines on head-worn sensors

Fitness activity classification on wearable devices can provide activity-specific information and generate more accurate performance metrics. Recently, optical head-mounted displays (OHMD) like Google Glass, Sony SmartEyeglass and Recon Jet have emerged. This paper presents a novel method to classify fitness activities using head-worn accelerometer, barometric pressure sensor and GPS, with comparisons to other common mounting locations on the body. Using multiclass SVM on head-worn sensors, we obtained an average F-score of 96.66% for classifying standing, walking, running, ascending/descending stairs and cycling. The best sensor location combinations were found to be on the ankle plus another upper body location. Using three or more sensors did not show a notable improvement over the best two-sensor combinations.

Synthetic soundscapes with natural grains

We present a technique to facilitate the creation of constantly changing, randomized audio streams from samples of source material. A core motivation is to make it easier to quickly create soundscapes for virtual environments and other scenarios where long streams of audio are used. While mostly in the background, these streams are vital for the creation of mood and realism in these types of applications. Our approach is to extract the component parts of sampled audio signals, and use them to resynthesize a continuous audio stream of indeterminate length. An automatic segmentation algorithm involving wavelets is used to split the input signal into syllable-like audio segments that we call natural grains. For each grain, a table of similarity between it and all the other grains is constructed. The grains are then output in a continuous stream, with the next grain being chosen from among those other grains which best follow from it. Using this sampling-resynthesis technique, we can construct an infinite number of variations on the original signal with a minimum amount of interaction. An interface for the manipulation and playback of several of these streams is provided to facilitate building complex audio environments, and is made available for online experimentation at www.cs.ubc.ca/labs/lci/naturalgrains/.

Echology: an interactive spatial sound and video artwork

We present a novel way of manipulating a spatial soundscape, one that encourages collaboration and exploration. Through a table-top display surrounded by speakers and lights, participants are invited to engage in peaceful play with Beluga whales shown through a live web camera feed from the Vancouver Aquarium in Canada. Eight softly glowing buttons and a simple interface encourage collaboration with others who are also enjoying the swirling Beluga sounds overhead.

Pedestrian Dead Reckoning With Smartglasses and Smartwatch

Wearable miniature inertial sensors have been widely used for pedestrian dead reckoning (PDR). Typical low-cost PDR systems use sensors attached to either the human trunk or feet. The recent emergence of smartglasses and smart watches provides an opportunity to use both types of wearable devices in position tracking. This paper proposes a novel method of utilizing both a smartwatch and smartglasses for PDR. The general idea is to use the relative angle between arm swing direction and head direction to detect any head-turn motion that would otherwise skew the position dead reckoning propagation. A complete PDR solution that includes step detection, step length estimation, head-rotation detection, and dead reckoning using a smartwatch and smartglasses that are currently available in the market is presented. Using the smartglasses, step detection with an error rate less than 0.4% and a cumulative distance error of less than 2.4% on 800 m walks and runs is achieved. In the dead reckoning field experiments, the proposed algorithm produces result that closely track the actual path when plotted on Google Maps, outperforming solutions that only use the smartwatch or smartglasses alone.

Automatic jump detection in skiing/snowboarding using head-mounted MEMS inertial and pressure sensors

With advancing technology in the miniature microelectromechanical systems (MEMS) sensors, wearable devices are becoming increasingly popular, facilitating convenient activity detection. One particular application is in sports performance monitoring. This article presents a novel real-time jump detection algorithm in skiing and snowboarding using a microelectromechanical systems–based inertial measurement unit, which is integrated with a barometric pressure sensor. The key performance variables of the jump can be extracted and evaluated for training and/or entertainment purposes. In contrast to the existing jump detection algorithms based on acceleration signals, the proposed algorithm uses vertical velocity and air time in addition to acceleration in the vertical direction. The experimental results show that by incorporating the velocity and air time into the detection algorithm, the sensitivity and specificity increase dramatically to 92% and 93%, respectively.

Compact near-eye display system using a superlens-based microlens array magnifier.

This paper reports a new approach to making a very compact near-eye display (NED) using only two layers of microlens arrays (MLA) working in conjunction as a magnifying lens (MLA magnifier). The purpose of the MLA magnifier is to generate a virtual image of a display, positioned within several centimeters from the eye, at optical infinity to minimize the optical disparity between the surrounding scenery and the image on the display. Our MLA magnifier is about 2 mm thick with a system focal length of 5 mm and a total thickness of around 7 mm (excluding the thickness of the display) in non-folded optics configuration, which is much more compact in comparison to other popular NEDs such as Google Glass or Recon Instruments Snow goggles having folded optics.