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Proceedings ArticleDOI: 10.1109/ICETACS.2013.6691428

Communication by gestures in personal emergency response system

23 Dec 2013-pp 230-235
Abstract: The Present systems on emergency response are primitive and not accurate in expressing the actual need of the person in risk. In a proper healthcare monitoring every communication between the patient and the care takers are critical and such response involves many implicit meanings. Even a slight misapprehension leads to adverse effects. A simple wearable system can precisely interpret the implicit communication to the care takers or to an automated support device. Simple and obvious hand movements can be used for the above purpose. The proposed system suggests a novel and swifter methodology simpler than the conventional sign language interpretations for such implicit communication. The exploratory experimental results show a well-distinguished realization of different hand movement activities using a wearable sensor medium and the interpretation results always show significant thresholds as well as faster recognition. more

Topics: Gesture (52%), Gesture recognition (52%), Wearable computer (50%)

Proceedings ArticleDOI: 10.1109/CTIT.2013.6749513
Ken Newman1Institutions (1)
01 Dec 2013-
Abstract: This paper demonstrates methods for creating a server application with the task of generating a plausible family tree based on the user The application does not try to be factual, merely plausible In creating this app a number of server based techniques are employed to solve the various problems 1 - Heuristics were defined to simulate plausible birth and death dates, and also the parent's age at the birth of the each generation 2 - Screen scraping techniques were employed to harvest names from name databases which did not offer an api 3 - Google places api was used to select a plausible birthplace for each ancestors 4 - Wikibots were used to retrieve a plausible occupation plausible for each ancestor The purpose of this study is to investigate how a variety of servers-based methods can be employed within a single application to provide a complex result which appears intelligent, has a certain level of semantic credibility, is fun, engaging for the user and appears entirely plausible more

Topics: Data scraping (53%), Heuristics (52%)

4 Citations

Proceedings ArticleDOI: 10.1109/CTIT.2017.8259558
S. Andrews1, S. KarthikInstitutions (1)
01 Oct 2017-
Abstract: The clinical emergency communication between a patient and medical agents is crucial and requires accurate interpretations. A novel approach using wearable devices come to address this issue in recent days. The accuracy of the system's output depends on the precise representation of two important things namely correct gestures and burst timings. The proposed burst detection algorithm calculates the intensity and latency of the burst occurrence in the signal generated during the gesture process. The finding of bursts helps to interpret the gesture signal and plays a vital role in identification and patterning the communication and response. This burst helps in preprocessing of the signals and selecting the important feature of the signals for further classification and interpretations. The more active channels that produce effective bursts are identified by the proposed algorithm and helps in hardware optimization. more

3 Citations

Proceedings ArticleDOI: 10.1109/CTIT.2013.6749512
01 Dec 2013-
Abstract: The implicit robotic communication is highly appreciated in the fields of security and defense The reason for such communication is to conceal the commands or its source to others to gain the whole benefits of the hidden robotic assistance to make it available in the case of emergency and critical situations To interpret and communicate most distinctive orders to the robotic system, a well defined and distinguishable gesture paradigm is necessary Moreover the conversion of such paradigms should also be precise and augmentative The Random Average Distribution helps to reduce the complexity of the SVD features and augment the clear classification The signals acquired from a pair of different gestures from five subjects with ten trials, were subjected to the proposed feature extraction method and were classified The results obtained were found better than the results obtained by the direct SVD and reduced in complexity more

Topics: Gesture recognition (54%), Gesture (53%), Feature extraction (50%)

3 Citations

Journal ArticleDOI: 10.1016/J.PATREC.2021.04.017
Julien Maitre1, Clément Rendu1, Kevin Bouchard1, Bruno Bouchard1  +1 moreInstitutions (1)
Abstract: In this paper, special attention is given to the hand. The literature provides solutions allowing the hand gestures recognition and/or object recognition for virtual reality, robotic applications, and so on. These solutions rely mainly on computer vision and data gloves. From this finding, we decided to develop our data glove prototype. The data glove is exploited to recognize common objects in the kitchen that the person can hold (e.g., hold a fork) while he/she performs basic daily activities such as drink a glass of water. The proposed approach is straightforward, cheap ( ∼ 260 $ in USD) and efficient ( ∼ 100%). Moreover, the designed data glove gives easy and direct access to the raw data provided by sensors. Besides, a comparison between classical machine learning algorithms (e.g., CART, Random Forest) and a deep neural network is given. Finally, the proposed prototype is described in a way that researchers can reproduce it for any applications involving the object recognition with the hand. more

Topics: Wired glove (66%), Fork (file system) (53%)

2 Citations


Journal ArticleDOI: 10.1016/J.ENGAPPAI.2011.06.015
Cemil Oz, Ming C. Leu1Institutions (1)
Abstract: An American Sign Language (ASL) recognition system is being developed using artificial neural networks (ANNs) to translate ASL words into English. The system uses a sensory glove called the Cyberglove(TM) and a Flock of Birds^(R) 3-D motion tracker to extract the gesture features. The data regarding finger joint angles obtained from strain gauges in the sensory glove define the hand shape, while the data from the tracker describe the trajectory of hand movements. The data from these devices are processed by a velocity network with noise reduction and feature extraction and by a word recognition network. Some global and local features are extracted for each ASL word. A neural network is used as a classifier of this feature vector. Our goal is to continuously recognize ASL signs using these devices in real time. We trained and tested the ANN model for 50 ASL words with a different number of samples for every word. The test results show that our feature vector extraction method and neural networks can be used successfully for isolated word recognition. This system is flexible and open for future extension. more

Topics: Word recognition (57%), Feature extraction (55%), Feature vector (54%) more

146 Citations

Journal ArticleDOI: 10.1016/J.JNEUMETH.2008.11.005
R. Gentner1, Joseph Classen1Institutions (1)
Abstract: Sensor gloves for measurements of finger movements are a promising tool for objective assessments of kinematic parameters and new rehabilitation strategies. Here, a novel low-cost sensor glove equipped with resistive bend sensors is described and evaluated. Resistive bend sensors were modified in order to optimize measurement accuracy (quantified as the stability of sensor signal after a fast and constant bending) and to increase sensor linearity, reducing calibration time from several minutes to only approximately 10s. Reliability analysis of the sensor glove in five subjects showed an intraclass correlation coefficient (ICC) of 0.93+/-0.05, a mean standard deviation of 1.59 degrees and an overall error of 4.96 degrees , comparable to previously evaluated sensor gloves. User acceptance and applicability, assessed by a user feedback questionnaire, was high. Thus, with minor modifications, resistive bend sensors are suitable for accurate assessments of human finger movements. The low material costs ( more

129 Citations

Journal ArticleDOI: 10.1109/TPAMI.2008.32
Nidal Kamel1, Shohel Sayeed2, G.A. Ellis1Institutions (2)
Abstract: Utilizing the multiple degrees of freedom offered by the data glove for each finger and the hand, a novel online signature verification system using the singular value decomposition (SVD) numerical tool for signature classification and verification is presented. The proposed technique is based on the Singular value decomposition in finding r singular vectors sensing the maximal energy of glove data matrix A, called principal subspace, so the effective dimensionality of A can be reduced. Having modeled the data glove signature through its r-principal subspace, signature authentication is performed by finding the angles between the different subspaces. A demonstration of the data glove is presented as an effective high- bandwidth data entry device for signature verification. This SVD-based signature verification technique is tested and its performance is shown to be able to produce equal error rate (EER) of less than 2.37 percent. more

Topics: Wired glove (61%), Matrix decomposition (51%), Subspace topology (50%)

72 Citations

Journal ArticleDOI: 10.1016/J.ENGAPPAI.2010.06.001
Abstract: Sign and gesture recognition offers a natural way for human-computer interaction. This paper presents a real time sign recognition architecture including both gesture and movement recognition. Among the different technologies available for sign recognition data gloves and accelerometers were chosen for the purposes of this research. Due to the real time nature of the problem, the proposed approach works in two different tiers, the segmentation tier and the classification tier. In the first stage the glove and accelerometer signals are processed for segmentation purposes, separating the different signs performed by the system user. In the second stage the values received from the segmentation tier are classified. In an effort to emphasize the real use of the architecture, this approach deals specially with problems like sensor noise and simplification of the training phase. more

Topics: Gesture recognition (66%), Sketch recognition (59%), Gesture (53%) more

38 Citations

Open accessJournal ArticleDOI: 10.1155/2011/724697
Abstract: A brain computer interface BCI enables direct communication between a brain and a computer translating brain activity into computer commands using preprocessing, feature extraction, and classification operations. Feature extraction is crucial, as it has a substantial effect on the classification accuracy and speed. While fractal dimension has been successfully used in various domains to characterize data exhibiting fractal properties, its usage in motor imagery-based BCI has been more recent. In this study, commonly used fractal dimension estimation methods to characterize time series Katz's method, Higuchi's method, rescaled range method, and Renyi's entropy were evaluated for feature extraction in motor imagery-based BCI by conducting offline analyses of a two class motor imagery dataset. Different classifiers fuzzy k-nearest neighbours FKNN, support vector machine, and linear discriminant analysis were tested in combination with these methods to determine the methodology with the best performance. This methodology was then modified by implementing the time-dependent fractal dimension TDFD, differential fractal dimension, and differential signals methods to determine if the results could be further improved. Katz's method with FKNN resulted in the highest classification accuracy of 85%, and further improvements by 3% were achieved by implementing the TDFD method. more

Topics: Fractal (58%), Fractal dimension (55%), Feature extraction (53%) more

32 Citations

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