Hand Gesture Recognition Using Particle Swarm Movement

We present a gesture recognition method derived from particle swarm movement for free-air hand gesture recognition. Online gesture recognition remains a difficult problem due to uncertainty in vision-based gesture boundary detection methods. We suggest an automated process of segmenting meaningful gesture trajectories based on particle swarm movement. A subgesture detection and reasoning method is incorporated in the proposed recognizer to avoid premature gesture spotting. Evaluation of the proposed method shows promising recognition results: 97.6% on preisolated gestures, 94.9% on stream gestures with assistive boundary indicators, and 94.2% for blind gesture spotting on digit gesture vocabulary. The proposed recognizer requires fewer computation resources; thus it is a good candidate for real-time applications

Human gesture classification by brute-force machine learning for exergaming in physiotherapy

In this paper, a novel approach for human gesture classification on skeletal data is proposed for the application of exergaming in physiotherapy. Unlike existing methods, we propose to use a general classifier like Random Forests to recognize dynamic gestures. The temporal dimension is handled afterwards by majority voting in a sliding window over the consecutive predictions of the classifier. The gestures can have partially similar postures, such that the classifier will decide on the dissimilar postures. This brute-force classification strategy is permitted, because dynamic human gestures show sufficient dissimilar postures. Online continuous human gesture recognition can classify dynamic gestures in an early stage, which is a crucial advantage when controlling a game by automatic gesture recognition. Also, ground truth can be easily obtained, since all postures in a gesture get the same label, without any discretization into consecutive postures. This way, new gestures can be easily added, which is advantageous in adaptive game development. We evaluate our strategy by a leave-one-subject-out cross-validation on a self-captured stealth game gesture dataset and the publicly available Microsoft Research Cambridge-12 Kinect (MSRC-12) dataset. On the first dataset we achieve an excellent accuracy rate of 96.72%. Furthermore, we show that Random Forests perform better than Support Vector Machines. On the second dataset we achieve an accuracy rate of 98.37%, which is on average 3.57% better then existing methods

Sonification of probabilistic feedback through granular synthesis

We describe a method to improve user feedback, specifically the display of time-varying probabilistic information, through asynchronous granular synthesis. We have applied these techniques to challenging control problems as well as to the sonification of online probabilistic gesture recognition. We're using these displays in mobile, gestural interfaces where visual display is often impractical

Hidden-Markov-Models-Based Dynamic Hand Gesture Recognition

This paper is concerned with the recognition of dynamic hand gestures. A method based on Hidden Markov Models (HMMs) is presented for dynamic gesture trajectory modeling and recognition. Adaboost algorithm is used to detect the user's hand and a contour-based hand tracker is formed combining condensation and partitioned sampling. Cubic B-spline is adopted to approximately fit the trajectory points into a curve. Invariant curve moments as global features and orientation as local features are computed to represent the trajectory of hand gesture. The proposed method can achieve automatic hand gesture online recognition and can successfully reject atypical gestures. The experimental results show that the proposed algorithm can reach better recognition results than the traditional hand recognition method

Real-time Gesture Recognition Using RFID Technology

This paper presents a real-time gesture recognition technique based on RFID technology. Inexpensive and unintrusive passive RFID tags can be easily attached to or interweaved into user clothes. The tag readings in an RFID-enabled environment can then be used to recognize the user gestures in order to enable intuitive human-computer interaction. People can interact with large public displays without the need to carry a dedicated device, which can improve interactive advertisement in public places. In this paper, multiple hypotheses tracking is used to track the motion patterns of passive RFID tags. Despite the reading uncertainties inherent in passive RFID technology, the experiments show that the presented online gesture recognition technique has an accuracy of up to 96%

Sensor fusion using EMG and vision for hand gesture classification in mobile applications

The discrimination of human gestures using wear - able solutions is extremely important as a supporting technique for assisted living, healthcare of the elderly and neurorehabili - tation. This paper presents a mobile electromyography (EMG) analysis framework to be an auxiliary component in physiother - apy sessions or as a feedback for neuroprosthesis calibration. We implemented a framework that allows the integration of multi - sensors, EMG and visual information, to perform sensor fusion and to improve the accuracy of hand gesture recognition tasks. In par ticular, we used an event - based camera adapted to run on the limited computational resources of mobile phones. We introduced a new publicly available dataset of sensor fusion for hand gesture recognition recorded from 10 subjects and used it to train th e recognition models offline. We compare the online results of the hand gesture recognition using the fusion approach with the individual sensors with an improvement in the accuracy of 13% and 11% , for EMG and vision respectively, reaching 85