바디 제스처 인식을 위한 기초적 신체 모델 인코딩과 선택적 / 비동시적 입력을 갖는 병렬 상태 기계

Ultigesture: A wristband-based platform for continuous gesture control in healthcare

In recent years, wearable sensor-based gesture recognition is proliferating in the field of healthcare. It could be used to enable remote control of medical devices, contactless navigation of X-ray display and Magnetic Resonance Imaging (MRI), and largely enhance patients' daily living capabilities. However, even though a few commercial or prototype devices are available for wearable gesture recognition, none of them provides a combination of (1) fully open API for various healthcare application development, (2) appropriate form factor for comfortable daily wear, and (3) affordable cost for large scale adoption. In addition, the existing gesture recognition algorithms are mainly designed for discrete gestures. Accurate recognition of continuous gestures is still a significant challenge, which prevents the wide usage of existing wearable gesture recognition technology. In this paper, we present Gemote, a smart wristband-based hardware/software platform for gesture recognition and remote control. Due to its affordability, small size, and comfortable profile, Gemote is an attractive option for mass consumption. Gemote provides full open API access for third party research and application development. In addition, it employs a novel continuous gesture segmentation and recognition algorithm, which accurately and automatically separates hand movements into segments, and merges adjacent segments if needed, so that each gesture only exists in one segment. Experiments with human subjects show that the recognition accuracy is 99.4% when users perform gestures discretely, and 94.6% when users perform gestures continuously.

MuViH: Multi-View Hand gesture dataset and recognition pipeline for human–robot interaction in a collaborative robotic finishing platform

Human Behavior and Hand Gesture Classification for Smart Human-robot Interaction

A low-power and real-time hidden Markov model (HMM) accelerator is proposed for gesture user interfaces on wearable smart devices. HMM algorithm is widely used for sequence recognitions such as speech recognition and gesture recognition due to its best-in-class recognition accuracy. However, the HMM algorithm incorporates high computational complexity and requires massive memory bandwidth for sequence matches. There have been studies on hardware acceleration of the HMM algorithm to resolve these issues, but they were focused on the speech recognition and did not incorporate the motion orientation capability required for the gesture recognition case. In this paper, we propose an HMM accelerator incorporating the motion orientation block for gesture recognitions on wearable devices. Binary search is exploited in the motion orientation to avoid the division and arctangent associated with the orientation and reduce its arithmetic complexity. In addition, gesture models are clustered in the gesture database to save the memory bandwidth by reducing memory transactions. Moreover, logarithmic arithmetic is used in Viterbi decoding in the HMM for more reduction in its complexity. Thanks to these schemes, this work achieves 25.6% power reduction compared with a plain hardware implementation of the gesture recognizing HMM.

Hand gesture recognition based on dynamic Bayesian network framework

In this paper, we propose an automatic system that recognizes both isolated and continuous gestures for Arabic numbers (0-9) in real-time based on hidden Markov model (HMM). To handle isolated gestures, HMM using ergodic, left-right (LR) and left-right banded (LRB) topologies with different number of states ranging from 3 to 10 is applied. Orientation dynamic features are obtained from spatio-temporal trajectories and then quantized to generate its codewords. The continuous gestures are recognized by our novel idea of zero-codeword detection with static velocity motion. Therefore, the LRB topology in conjunction with forward algorithm presents the best performance and achieves average rate recognition 98.94% and 95.7% for isolated and continuous gestures, respectively.

Safety of children in cars: A review of biomechanical aspects and human body models

CG-Recognizer: A biosignal-based continuous gesture recognition system

We model a recognition problem for connected hand gestures to find an optimal path through a hidden Markov model (HMM) directed acyclic graph. To determine this optimal path, an online graph search method is proposed that decodes the observed gesture pattern and evaluates the optimal graph node at each time frame of the continuously deepening HMM graph. The temporal characteristic of gesture recognition is subsequently handled by introducing a rejection threshold time that acts as a depth-wise sliding window for pruning unnecessary graph nodes. The functional depth of the graph is defined by this depth rejection threshold. Experimental comparison of our algorithm with other HMM-based search algorithms demonstrates the effectiveness and robustness of our method.

Human gesture recognition is an area, which has been studied thoroughly in recent years,and close to100% recognition rates in restricted environments have been achieved, often either with single separated gestures in the input stream, or with computationally intensive systems. The results are unfortunately not as striking, when it comes to a continuous stream of gestures. In this paper we introduce a hierarchical system for gesture recognition for use in a gaming setting, with a continuous stream of data. Layer 1 is based on Nearest Neighbor Search and layer 2 uses Hidden Markov Models. The system uses features that are computed from Microsoft Kinect skeletons. We propose a new set of features, the relative angles of the limbs from Kinect's axes to use in NNS. The new features show a 10 percent point increase in precision when compared with features from previously published results. We also propose a way of attributing recognised gestures with a force attribute, for use in gaming. The recognition rate in layer 1 is 68.2%, with an even higher rate for simple gestures. Layer 2 reduces the noise and has aaverage recognition rate of 85.1%. When some simple constraints are added we reach a precision of 90.5% with a recall of 91.4%.

Hand gesture recognition for human-computer interaction has become very popular in recent times. The main problem of this technology is how the system can recognize the presence of a gesture in a streaming video. In this paper, we propose a model that can recognize hand motion gesture in avideo stream using Support Vector Machine and Hidden-Markov model. Support Vector Machine has the advantage of generalizing classification. On the other hand, Hidden Markov Model is a statistical model that is capable of modeling Spatial-temporal time series. This system is divided into two main processes. First, this system recognizes hand posture using SMV (static gesture recognition) and generates sequence observation which is used for the second process later. The second process is recognizing dynamic hand gesture with the sequence observation from the static gesture. The implementation shows that static hand gesture recognition achieves average accuracy at 91% using testing dataset. Meanwhile,dynamically isolated hand gesture recognition gets average accuracy at 89% using testing dataset. We also have tested the system with continuous dynamic gesture using video stream. The system can recognize the gesture very well with accuracy of 83%. This achievement shows that the model can be used in human computer-interaction with specific supports such as Vector Machine and Hidden Markov Model parameter.

In order to incorporate naturalness in the design of human computer interfaces (HCI), it is desirable to develop recognition techniques capable of handling continuous natural gesture and speech inputs. Though many different researchers have reported high recognition rates for gesture recognition using hidden Markov models (HMM), the gestures used are mostly pre-defined and are bound with syntactical and grammatical constraints. But natural gestures do not string together in syntactical bindings. Moreover, strict classification of natural gestures is not feasible. We have examined hand gestures made in a very natural domain, that of a weather person narrating in front of a weather map. The gestures made by the weather person are embedded in a narration. This provides us with abundant data from an uncontrolled environment to study the interaction between speech and gesture in the context of a display. We hypothesize that this domain is very similar to that of a natural human-computer interface. We present an HMM architecture for continuous gesture recognition framework and keyword spotting. To explore the relation between gesture and speech, we conducted a statistical co-occurrence analysis of different gestures with a selected set of spoken keywords. We then demonstrate how this co-occurrence analysis can be exploited to improve the performance of continuous gesture recognition.

In this paper, we develop a system for automated human body tracking and modeling based on a monocular camera. In this system, ten body parts including head, torso, arms and legs are extracted to build a 2D human body model. One way to decompose human silhouette into different parts is to generate cuts between the negative minimum curvature (NMC) points. However, due to the self-occlusion problem and left-right ambiguity, each individual body part cannot be successfully identified in every frame. Therefore, in addition to utilizing the NMC points, we design a forward and backward tracking mechanism to identify the location of head in each frame. The torso angle and size are determined by integrating multiple-frame information with the modified solution of Poisson equation. Hands and feet can then be identified correctly based on a modified star skeleton approach along with the nearest-neighbor tracking mechanism. The rest of joint points can also be located by making use of the notion “connectivity”. In the experiments, we analyze the performance of the proposed human body modeling mechanism. We also demonstrate a behavior analysis application by employing the proposed method. The experiment results verify the robustness of the proposed approach and the feasibility of the employing the proposed approach to the action recognition application.

With the constant development of smart devices, Gesture recognition is applied on more and more fields. Gesture recognition devices currently on the market are inconvenient and expensive. A gesture recognition method based on Kinect is proposed in this paper. The camera of Kinect is used to get gesture images and then a hidden Markov model is established to recognize dynamic gesture so that the operation is simple. The assessment, decoding algorithms and training issues of hidden Markov model in gesture recognition is focused in this paper. Viterbi algorithm and Baum-Welch algorithm are used to solve the problems above. After simulation experiments, it is discovered that the gesture recognition algorithm based on Kinect proposed in this paper has a high accuracy rate for a variety of gestures, simultaneously the naturalness of human-computer interaction has been greatly improved.