Dynamic Projection Mapping for Robust Sphere Posture Tracking Using Uniform/Biased Circumferential Markers

Dynamic projection mapping for a moving object according to its position and shape is fundamental for augmented reality to resemble changes on a target surface. For instance, augmenting the human arm surface via dynamic projection mapping can enhance applications in fashion, user interfaces, prototyping, education, medical assistance, and other fields. For such applications, however, conventional methods neglect skin deformation and have a high latency between motion and projection, causing noticeable misalignment between the target arm surface and projected images. These problems degrade the user experience and limit the development of more applications. We propose a system for high-speed dynamic projection mapping onto a rapidly moving human arm with realistic skin deformation. With the developed system, the user does not perceive any misalignment between the arm surface and projected images. First, we combine a state-of-the-art parametric deformable surface model with efficient regression-based accuracy compensation to represent skin deformation. Through compensation, we modify the texture coordinates to achieve fast and accurate image generation for projection mapping based on joint tracking. Second, we develop a high-speed system that provides a latency between motion and projection below 10 ms, which is generally imperceptible by human vision. Compared with conventional methods, the proposed system provides more realistic experiences and increases the applicability of dynamic projection mapping.

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Pervasive Lying Posture Tracking

Sensors ◽

10.3390/s20205953 ◽

2020 ◽

Vol 20 (20) ◽

pp. 5953 ◽

Cited By ~ 1

Author(s):

Parastoo Alinia ◽

Ali Samadani ◽

Mladen Milosevic ◽

Hassan Ghasemzadeh ◽

Saman Parvaneh

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Computational Models ◽

Learning Algorithms ◽

Pressure Sensors ◽

Machine Learning Algorithms ◽

Sensor System ◽

Accurate Detection ◽

Research Questions ◽

Posture Tracking

Automated lying-posture tracking is important in preventing bed-related disorders, such as pressure injuries, sleep apnea, and lower-back pain. Prior research studied in-bed lying posture tracking using sensors of different modalities (e.g., accelerometer and pressure sensors). However, there remain significant gaps in research regarding how to design efficient in-bed lying posture tracking systems. These gaps can be articulated through several research questions, as follows. First, can we design a single-sensor, pervasive, and inexpensive system that can accurately detect lying postures? Second, what computational models are most effective in the accurate detection of lying postures? Finally, what physical configuration of the sensor system is most effective for lying posture tracking? To answer these important research questions, in this article we propose a comprehensive approach for designing a sensor system that uses a single accelerometer along with machine learning algorithms for in-bed lying posture classification. We design two categories of machine learning algorithms based on deep learning and traditional classification with handcrafted features to detect lying postures. We also investigate what wearing sites are the most effective in the accurate detection of lying postures. We extensively evaluate the performance of the proposed algorithms on nine different body locations and four human lying postures using two datasets. Our results show that a system with a single accelerometer can be used with either deep learning or traditional classifiers to accurately detect lying postures. The best models in our approach achieve an F1 score that ranges from 95.2% to 97.8% with a coefficient of variation from 0.03 to 0.05. The results also identify the thighs and chest as the most salient body sites for lying posture tracking. Our findings in this article suggest that, because accelerometers are ubiquitous and inexpensive sensors, they can be a viable source of information for pervasive monitoring of in-bed postures.

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