scholarly journals Early gesture recognition method with an accelerometer

2015 ◽  
Vol 11 (3) ◽  
pp. 270-287 ◽  
Author(s):  
Ryo Izuta ◽  
Kazuya Murao ◽  
Tsutomu Terada ◽  
Masahiko Tsukamoto
Keyword(s):  
Gesture Recognition ◽  
Early Stage ◽  
The Other ◽  
Training Data ◽  
Recognition Method ◽  
Content Type ◽  
Recognition Process ◽  
Recognition Result ◽  
Interface Usability ◽  
Proper Length

Purpose – This paper aims to propose a gesture recognition method at an early stage. An accelerometer is installed in most current mobile phones, such as iPhones, Android-powered devices and video game controllers for the Wii or PS3, which enables easy and intuitive operations. Therefore, many gesture-based user interfaces that use accelerometers are expected to appear in the future. Gesture recognition systems with an accelerometer generally have to construct models with user’s gesture data before use and recognize unknown gestures by comparing them with the models. Because the recognition process generally starts after the gesture has finished, the output of the recognition result and feedback delay, which may cause users to retry gestures, degrades the interface usability. Design/methodology/approach – The simplest way to achieve early recognition is to start it at a fixed time after a gesture starts. However, the degree of accuracy would decrease if a gesture in an early stage was similar to the others. Moreover, the timing of a recognition has to be capped by the length of the shortest gesture, which may be too early for longer gestures. On the other hand, retreated recognition timing will exceed the length of the shorter gestures. In addition, a proper length of training data has to be found, as the full length of training data does not fit the input data until halfway. To recognize gestures in an early stage, proper recognition timing and a proper length of training data have to be decided. This paper proposes a gesture recognition method used in the early stages that sequentially calculates the distance between the input and training data. The proposed method outputs the recognition result when one candidate has a stronger likelihood of recognition than the other candidates so that similar incorrect gestures are not output. Findings – The proposed method was experimentally evaluated on 27 kinds of gestures and it was confirmed that the recognition process finished 1,000 msec before the end of the gestures on average without deteriorating the level of accuracy. Gestures were recognized in an early stage of motion, which would lead to an improvement in the interface usability and a reduction in the number of incorrect operations such as retried gestures. Moreover, a gesture-based photo viewer was implemented as a useful application of our proposed method, the proposed early gesture recognition system was used in a live unscripted performance and its effectiveness is ensured. Originality/value – Gesture recognition methods with accelerometers generally learn a given user’s gesture data before using the system, then recognizes any unknown gestures by comparing them with the training data. The recognition process starts after a gesture has finished, and therefore, any interaction or feedback depending on the recognition result is delayed. For example, an image on a smartphone screen rotates a few seconds after the device has been tilted, which may cause the user to retry tilting the smartphone even if the first one was correctly recognized. Although many studies on gesture recognition using accelerometers have been done, to the best of the authors’ knowledge, none of these studies has taken the potential delays in output into consideration.

Combinations of eigenspace and GMM with Kinect sensor-extracted action gesture features for person identity recognition

2016 ◽  
Vol 33 (8) ◽  
pp. 2489-2503 ◽  
Author(s):  
Ing-Jr Ding ◽  
Zong-Gui Wu
Keyword(s):  
Gesture Recognition ◽  
Recognition Accuracy ◽  
Optimal Performance ◽  
Kinect Sensor ◽  
Recognition Method ◽  
Feature Design ◽  
Content Type ◽  
Active User ◽  
Individual Classifier ◽  
User Recognition

Purpose The Kinect sensor released by Microsoft is well-known for its effectiveness on human gesture recognition. Gesture recognition by Kinect has been proved to be an efficient command operation and provides an additional human-computer interface in addition to the traditional speech recognition. For Kinect gesture recognition in the application of gesture command operations, recognition of the active user making the gesture command to Kinect will be an extremely crucial problem. The purpose of this paper is to propose a recognition method for recognizing the person identity of an active user using combined eigenspace and Gaussian mixture model (GMM) with Kinect-extracted action gesture features. Design/methodology/approach Several Kinect-derived gesture features will be explored for determining the effective pattern features in the active user recognition task. In this work, a separate Kinect-derived feature design for eigenspace recognition and GMM classification is presented for achieving the optimal performance of each individual classifier. In addition to Kinect-extracted feature designs for active user recognition, this study will further develop a combined recognition method, called combined eigenspace-GMM, which properly hybridizes the decision information of both the eigenspace and the GMM for making a more reliable user recognition result. Findings Active user recognition using an effective combination of eigenspace and GMM with well-developed active gesture features in Kinect-based active user recognition will have an outstanding performance on the recognition accuracy. The presented Kinect-based user recognition system using the presented approach will further have the competitive benefits of recognition on both gesture commands and providing users of gesture commands. Originality/value A hybridized scheme of eigenspace and GMM performs better than eigenspace-alone or GMM-alone on recognition accuracy of active user recognition; a separate Kinect-derived feature design for eigenspace recognition and GMM classification is presented for achieving the optimal performance of the individual classifier; combined eigenspace-GMM active user recognition belonging to model-based active user recognition design has a fine extension on increasing the recognition rate by adjusting recognition models.

scholarly journals Traffic Police Gesture Recognition Based on Gesture Skeleton Extractor and Multichannel Dilated Graph Convolution Network

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 551
Author(s):  
Xin Xiong ◽  
Haoyuan Wu ◽  
Weidong Min ◽  
Jianqiang Xu ◽  
Qiyan Fu ◽  
...  
Keyword(s):  
Gesture Recognition ◽  
Background Noise ◽  
State Of The Art ◽  
The Other ◽  
Recognition Method ◽  
Accuracy Rate ◽  
Traffic Police ◽  
Automatic Driving ◽  
Rgb Images ◽  
Public Dataset

Traffic police gesture recognition is important in automatic driving. Most existing traffic police gesture recognition methods extract pixel-level features from RGB images which are uninterpretable because of a lack of gesture skeleton features and may result in inaccurate recognition due to background noise. Existing deep learning methods are not suitable for handling gesture skeleton features because they ignore the inevitable connection between skeleton joint coordinate information and gestures. To alleviate the aforementioned issues, a traffic police gesture recognition method based on a gesture skeleton extractor (GSE) and a multichannel dilated graph convolution network (MD-GCN) is proposed. To extract discriminative and interpretable gesture skeleton coordinate information, a GSE is proposed to extract skeleton coordinate information and remove redundant skeleton joints and bones. In the gesture discrimination stage, GSE-based features are introduced into the proposed MD-GCN. The MD-GCN constructs a graph convolution with a multichannel dilated to enlarge the receptive field, which extracts body topological and spatiotemporal action features from skeleton coordinates. Comparison experiments with state-of-the-art methods were conducted on a public dataset. The results show that the proposed method achieves an accuracy rate of 98.95%, which is the best and at least 6% higher than that of the other methods.

scholarly journals Apparel-based deep learning system design for apparel style recommendation

2019 ◽  
Vol 31 (3) ◽  
pp. 376-389 ◽  
Author(s):  
Congying Guan ◽  
Shengfeng Qin ◽  
Yang Long
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Recommendation System ◽  
Learning Technologies ◽  
The Body ◽  
Learning System ◽  
The Other ◽  
Training Data ◽  
Support Vector ◽  
Content Type

Purpose The big challenge in apparel recommendation system research is not the exploration of machine learning technologies in fashion, but to really understand clothes, fashion and people, and know what to learn. The purpose of this paper is to explore an advanced apparel style learning and recommendation system that can recognise deep design-associated features of clothes and learn the connotative meanings conveyed by these features relating to style and the body so that it can make recommendations as a skilled human expert. Design/methodology/approach This study first proposes a type of new clothes style training data. Second, it designs three intelligent apparel-learning models based on newly proposed training data including ATTRIBUTE, MEANING and the raw image data, and compares the models’ performances in order to identify the best learning model. For deep learning, two models are introduced to train the prediction model, one is a convolutional neural network joint with the baseline classifier support vector machine and the other is with a newly proposed classifier later kernel fusion. Findings The results show that the most accurate model (with average prediction rate of 88.1 per cent) is the third model that is designed with two steps, one is to predict apparel ATTRIBUTEs through the apparel images, and the other is to further predict apparel MEANINGs based on predicted ATTRIBUTEs. The results indicate that adding the proposed ATTRIBUTE data that captures the deep features of clothes design does improve the model performances (e.g. from 73.5 per cent, Model B to 86 per cent, Model C), and the new concept of apparel recommendation based on style meanings is technically applicable. Originality/value The apparel data and the design of three training models are originally introduced in this study. The proposed methodology can evaluate the pros and cons of different clothes feature extraction approaches through either images or design attributes and balance different machine learning technologies between the latest CNN and traditional SVM.

Hand gesture recognition using low-budget data glove and cluster-trained probabilistic neural network

2014 ◽  
Vol 34 (1) ◽  
pp. 94-105 ◽  
Author(s):  
Ognjan Luzanin ◽  
Miroslav Plancak
Keyword(s):  
Gesture Recognition ◽  
Probabilistic Neural Network ◽  
Recognition System ◽  
Hand Gesture Recognition ◽  
Training Data ◽  
Sensor Data ◽  
Hand Gesture ◽  
Data Set ◽  
Content Type ◽  
Data Glove

Purpose – Main purpose is to present methodology which allows efficient hand gesture recognition using low-budget, 5-sensor data glove. To allow widespread use of low-budget data gloves in engineering virtual reality (VR) applications, gesture dictionaries must be enhanced with more ergonomic and symbolically meaningful hand gestures, while providing high gesture recognition rates when used by different seen and unseen users. Design/methodology/approach – The simple boundary-value gesture recognition methodology was replaced by a probabilistic neural network (PNN)-based gesture recognition system able to process simple and complex static gestures. In order to overcome problems inherent to PNN – primarily, slow execution with large training data sets – the proposed gesture recognition system uses clustering ensemble to reduce the training data set without significant deterioration of the quality of training. The reduction of training data set is efficiently performed using three types of clustering algorithms, yielding small number of input vectors that represent the original population very well. Findings – The proposed methodology is capable of providing efficient recognition of simple and complex static gestures and was also successfully tested with gestures of an unseen user, i.e. person who took no part in the training phase. Practical implications – The hand gesture recognition system based on the proposed methodology enables the use of affordable data gloves with a small number of sensors in VR engineering applications which require complex static gestures, including assembly and maintenance simulations. Originality/value – According to literature, there are no similar solutions that allow efficient recognition of simple and complex static hand gestures, based on a 5-sensor data glove.

Cognitive Functioning and Other Discriminatory Variables in Anorexic Patients

2005 ◽  
Vol 27 (1) ◽  
pp. 191-210 ◽  
Author(s):  
Carmen García-Alba
Keyword(s):  
Cognitive Functioning ◽  
Research Study ◽  
Early Stage ◽  
Developmental Period ◽  
The Other ◽  
Model Synthesis ◽  
Consistent Function ◽  
Current Article ◽  
Rorschach Variables ◽  
Underlying Pathology

This study is part of a larger research study (doctoral dissertation), in which a comparative study with adolescent samples is done: 50 anorexic restricting patients (ANP), 50 patients diagnosed with depression (DP) and 50 non patients (NP). The proposed objective is two-fold: 1) To try to clarify the existing relationship between Anorexia (AN) and Depression (D), investigated from diverse disciplines but without conclusive results. 2) To detect in the ANP personality different traits from those of other groups, which should, if possible, allow to detect them at an early stage for an adequate prognosis. The current article presents the Rorschach findings in relation to the cognitive functioning of the ANP. In them, the following has been detected: (1) An information processing similar to that of the other groups, even with a more complete (L ≤ .99), more complex (DQ+↑) and better discriminated (Zd↑) grasp of the stimulus; (2) Mediating processes very similar to those of the other groups, sharing with them the perceptive maladjustments (X–%↑) and an excessive individualism (Xu%↑); (3) A clearly differentiating ideation disorder. Definitely, the ANP use predominantly ideation (M↑), but their thought, usually well-adjusted (MQo↑), presents eventual operations of delusional type (MQnone↑). Above that, their thinking is marked by a great passivity (Mp↑), which makes them more vulnerable to accept ideas without criticizing them and it results in a very inefficient thinking, which spins around these concepts without finding solutions, entering into a sort of ruminating which is completely unproductive. The differences toward the obsessive pathology are established. The discriminant analysis conducted with all the Rorschach variables that resulted as significant throughout the research, provides quite a consistent function which discriminates the ANP: MQnone↑, Mp↑, FD↓, Ma↑, MQo↑, AdjD↑, Sum H↑, (H)↑. Based on this we can understand that these adolescents, being in a developmental period of big changes and disorientations in relation with their own image, confronted with life events, and possibly starting off with some biologic vulnerability: (1) Due to the alterations of their ideation, accept without criticism (Mp) irrational ideas dominating in our culture, in which slimness appears as the only model, synthesis of intelligence, beauty and success; remaining captured in this type of mental activity (MQnone), which they cannot escape nor criticize (Mp), despite they reason adequately on other topics (MQo); (2) Their alterations of self-perception [(H)] make them hide themselves in a fantasized image, which is the axis of their interests and the only thing that really matters to them; (3) The resources they have to decide on behaviors and to finish these deliberately (AdjD), and their scarce tendency to the introspection (FD) lead to their decision of not eating, based on distorted and passively accepted thinking, which has great power and thus, so difficult to modify. Finally, based on the Rorschach data obtained, the hypothesis of a personality disorder as underlying pathology is pointed out.

The Application of UX Research in New Energy Vehicle Innovation

2019 ◽  
Vol 1 (1) ◽  
Author(s):  
Menghan TAO ◽  
Ning XIAO ◽  
Xingfu ZHAO ◽  
Wenbin LIU
Keyword(s):  
Early Stage ◽  
Rapid Expansion ◽  
The Other ◽  
Innovative Product ◽  
Stage Of Development ◽  
New Energy ◽  
New Energy Vehicle ◽  
The One ◽  
New Energy Vehicles ◽  
Degree Of Innovation

New energy vehicles(NEV) as a new thing for sustainable development, in China, on the one hand has faced the rapid expansion of the market; the other hand, for the new NEV users, the current NEVs cannot keep up with the degree of innovation. This paper demonstrates the reasons for the existence of this systematic challenge, and puts forward the method of UX research which is different from the traditional petrol vehicles research in the early stage of development, which studies from the user's essence level, to form the innovative product programs which meet the needs of users and being real attractive.

Posterior approach for cervical intramedullary arteriovenous malformation with diffuse-type nidus

1999 ◽  
Vol 91 (1) ◽  
pp. 105-111 ◽  
Author(s):  
Kenji Ohata ◽  
Toshihiro Takami ◽  
Alaa El-Naggar ◽  
Michiharu Morino ◽  
Akimasa Nishio ◽  
...  
Keyword(s):  
Spinal Cord ◽  
Poor Prognosis ◽  
Posterior Approach ◽  
The Other ◽  
Anterior Spinal Artery ◽  
Neural Element ◽  
Feeding Artery ◽  
Diffuse Type ◽  
Content Type ◽  
Fine Print

✓ The treatment of spinal intramedullary arteriovenous malformations (AVMs) with a diffuse-type nidus that contains a neural element poses different challenges compared with a glomus-type nidus. The surgical elimination of such lesions involves the risk of spinal cord ischemia that results from coagulation of the feeding artery that, at the same time, supplies cord parenchyma. However, based on evaluation of the risks involved in performing embolization, together with the frequent occurrence of reperfusion, which necessitates frequent reembolization, the authors consider surgery to be a one-stage solution to a disease that otherwise has a very poor prognosis. Magnetic resonance (MR) imaging revealed diffuse-type intramedullary AVMs in the cervical spinal cords of three patients who subsequently underwent surgery via the posterior approach. The AVM was supplied by the anterior spinal artery in one case and by both the anterior and posterior spinal arteries in the other two cases. In all three cases, a posterior median myelotomy was performed up to the vicinity of the anterior median fissure that divided the spinal cord together with the nidus, and the feeding artery was coagulated and severed at its origin from the anterior spinal artery. In the two cases in which the posterior spinal artery fed the AVM, the feeding artery was coagulated on the dorsal surface of the spinal cord. Neurological outcome improved in one patient and deteriorated slightly to mildly in the other two patients. Postoperative angiography demonstrated complete disappearance of the AVM in all cases. Because of the extremely poor prognosis of patients with spinal intramedullary AVMs, this surgical technique for the treatment of diffuse-type AVMs provides acceptable operative outcome. Surgical intervention should be considered when managing a patient with a diffuse-type intramedullary AVM in the cervical spinal cord.

Feature Fusion Based Hand Gesture Recognition Method for Automotive Interfaces

2020 ◽  
Vol 29 (6) ◽  
pp. 1153-1164
Author(s):  
Qianyi Xu ◽  
Guihe Qin ◽  
Minghui Sun ◽  
Jie Yan ◽  
Huiming Jiang ◽  
...  
Keyword(s):  
Gesture Recognition ◽  
Feature Fusion ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Recognition Method

Islamic hedging for pilgrimage funds: case of Indonesia

2019 ◽  
Vol 11 (3) ◽  
pp. 328-341
Author(s):  
Rifki Ismal ◽  
Nurul Izzati Septiana
Keyword(s):  
Exchange Rate ◽  
The Other ◽  
Exchange Rate Risk ◽  
Content Type ◽  
Other Hand ◽  
Rate Risk ◽  
Risk Identify ◽  
Hajj Season ◽  
The Right ◽  
Very High

Purpose The demand for Saudi Arabian real (SAR) is very high in the pilgrimage (hajj) season while the authority, unfortunately, does not hedge the hajj funds. As such, the hajj funds are potentially exposed to exchange rate risk, which can impact the value of hajj funds and generate extra cost to the pilgrims. The purpose of this paper is to conduct simulations of Islamic hedging for pilgrimage funds to: mitigate and minimize exchange rate risk, identify and recommend the ideal time, amount and tenors of Islamic hedging for hajj funds, estimate cost saving by pursuing Islamic hedging and propose technical and general recommendations for the authority. Design/methodology/approach Forward transaction mechanism is adopted to compute Islamic forward between SAR and Rupiah (Indonesian currency) or IDR. Findings – based on simulations, the paper finds that: the longer the Islamic hedging tenors, the better is the result of Islamic hedging, the decreasing of IDR/USD is the right time to hedge the hajj funds and, on the other hand, the IDR/SAR appreciation is not the right time to hedge the hajj funds. Findings Based on simulations, the paper finds that: the longer the Islamic hedging tenors, the better is the result of Islamic hedging, the decreasing of IDR/USD is the right time to hedge the hajj funds and, on the other hand, the IDR/SAR appreciation is not the right time to hedge the hajj funds. Research limitations/implications The research suggests the authority to (and not to) hedge the hajj fund, depending on economic conditions and market indicators. Even though the assessment is for the Indonesian case, other countries maintaining hajj funds might also learn from this paper. Originality/value To the best of author’s knowledge, this is the first paper in Indonesia that attempts to simulate the optimal hedging of hajj funds.

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