3,575 research outputs found
Deep Thermal Imaging: Proximate Material Type Recognition in the Wild through Deep Learning of Spatial Surface Temperature Patterns
We introduce Deep Thermal Imaging, a new approach for close-range automatic
recognition of materials to enhance the understanding of people and ubiquitous
technologies of their proximal environment. Our approach uses a low-cost mobile
thermal camera integrated into a smartphone to capture thermal textures. A deep
neural network classifies these textures into material types. This approach
works effectively without the need for ambient light sources or direct contact
with materials. Furthermore, the use of a deep learning network removes the
need to handcraft the set of features for different materials. We evaluated the
performance of the system by training it to recognise 32 material types in both
indoor and outdoor environments. Our approach produced recognition accuracies
above 98% in 14,860 images of 15 indoor materials and above 89% in 26,584
images of 17 outdoor materials. We conclude by discussing its potentials for
real-time use in HCI applications and future directions.Comment: Proceedings of the 2018 CHI Conference on Human Factors in Computing
System
Vision-based portuguese sign language recognition system
Vision-based hand gesture recognition is an area of active current research in computer vision and machine learning. Being a natural way of human interaction, it is an area where many researchers are working on, with the goal of making human computer interaction (HCI) easier and natural, without the need for any extra devices. So, the primary goal of gesture recognition research is to create systems, which can identify specific human gestures and use them, for example, to convey information. For that, vision-based hand gesture interfaces require fast and extremely robust hand detection, and gesture recognition in real time. Hand gestures are a powerful human communication modality with lots of potential applications and in this context we have sign language recognition, the communication method of deaf people. Sign lan- guages are not standard and universal and the grammars differ from country to coun- try. In this paper, a real-time system able to interpret the Portuguese Sign Language is presented and described. Experiments showed that the system was able to reliably recognize the vowels in real-time, with an accuracy of 99.4% with one dataset of fea- tures and an accuracy of 99.6% with a second dataset of features. Although the im- plemented solution was only trained to recognize the vowels, it is easily extended to recognize the rest of the alphabet, being a solid foundation for the development of any vision-based sign language recognition user interface system
Wheelchair control by head motion
Electric wheelchairs are designed to aid paraplegics. Unfortunately, these
can not be used by persons with higher degree of impairment, such as
quadriplegics, i.e. persons that, due to age or illness, can not move any of
the body parts, except of the head. Medical devices designed to help them are
very complicated, rare and expensive. In this paper a microcontroller system
that enables standard electric wheelchair control by head motion is
presented. The system comprises electronic and mechanic components. A novel
head motion recognition technique based on accelerometer data processing is
designed. The wheelchair joystick is controlled by the system’s mechanical
actuator. The system can be used with several different types of standard
electric wheelchairs. It is tested and verified through an experiment
performed within this paper
Assistive robotic device: evaluation of intelligent algorithms
Assistive robotic devices can be used to help people with upper body
disabilities gaining more autonomy in their daily life. Although basic motions
such as positioning and orienting an assistive robot gripper in space allow
performance of many tasks, it might be time consuming and tedious to perform
more complex tasks. To overcome these difficulties, improvements can be
implemented at different levels, such as mechanical design, control interfaces
and intelligent control algorithms. In order to guide the design of solutions,
it is important to assess the impact and potential of different innovations.
This paper thus presents the evaluation of three intelligent algorithms aiming
to improve the performance of the JACO robotic arm (Kinova Robotics). The
evaluated algorithms are 'preset position', 'fluidity filter' and 'drinking
mode'. The algorithm evaluation was performed with 14 motorized wheelchair's
users and showed a statistically significant improvement of the robot's
performance.Comment: 4 page
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