AIMC Topic: Gestures

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Nonparametric Feature Matching Based Conditional Random Fields for Gesture Recognition from Multi-Modal Video.

IEEE transactions on pattern analysis and machine intelligence
We present a new gesture recognition method that is based on the conditional random field (CRF) model using multiple feature matching. Our approach solves the labeling problem, determining gesture categories and their temporal ranges at the same time...

Optimal Modality Selection for Cooperative Human-Robot Task Completion.

IEEE transactions on cybernetics
Human-robot cooperation in complex environments must be fast, accurate, and resilient. This requires efficient communication channels where robots need to assimilate information using a plethora of verbal and nonverbal modalities such as hand gesture...

Unsupervised Trajectory Segmentation for Surgical Gesture Recognition in Robotic Training.

IEEE transactions on bio-medical engineering
Dexterity and procedural knowledge are two critical skills that surgeons need to master to perform accurate and safe surgical interventions. However, current training systems do not allow us to provide an in-depth analysis of surgical gestures to pre...

Real-time human pose estimation and gesture recognition from depth images using superpixels and SVM classifier.

Sensors (Basel, Switzerland)
In this paper, we present human pose estimation and gesture recognition algorithms that use only depth information. The proposed methods are designed to be operated with only a CPU (central processing unit), so that the algorithm can be operated on a...

A computational model of the short-cut rule for 2D shape decomposition.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
We propose a new 2D shape decomposition method based on the short-cut rule. The short-cut rule originates from cognition research, and states that the human visual system prefers to partition an object into parts using the shortest possible cuts. We ...

Automatic gesture recognition and evaluation in peg transfer tasks of laparoscopic surgery training.

Surgical endoscopy
BACKGROUND: Laparoscopic surgery training is gaining increasing importance. To release doctors from the burden of manually annotating videos, we proposed an automatic surgical gesture recognition model based on the Fundamentals of Laparoscopic Surger...

MVMD-TCCA: A method for gesture classification based on surface electromyographic signals.

Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology
Gesture recognition plays a fundamental role in enabling nonverbal communication and interaction, as well as assisting individuals with motor impairments in performing daily tasks. Surface electromyographic (sEMG) signals, which can effectively detec...

Using Gesture and Speech to Control Surgical Lighting Systems: Mixed Methods Study.

JMIR human factors
BACKGROUND: Surgical lighting systems (SLSs) provide optimal lighting conditions for operating room personnel. Current systems are mainly adjusted by hand; surgeons either accommodate the light themselves or communicate their requirements to an assis...

Quantifying Facial Gestures Using Deep Learning in a New World Monkey.

American journal of primatology
Facial gestures are a crucial component of primate multimodal communication. However, current methodologies for extracting facial data from video recordings are labor-intensive and prone to human subjectivity. Although automatic tools for this task a...

Real-Time sEMG Processing With Spiking Neural Networks on a Low-Power 5K-LUT FPGA.

IEEE transactions on biomedical circuits and systems
The accurate modeling of hand movement based on the analysis of surface electromyographic (sEMG) signals offers exciting opportunities for the development of complex prosthetic devices and human-machine interfaces, moving from discrete gesture recogn...