American Sign Language Alphabet Research Articles

American Sign Language Alphabet Translator Android Application: Hand Shapes Into Text

American Sign Language is used by the deaf-mute community in expressing their thoughts. Many applications have been developed to assist the deaf-mute person, but most of the applications integrate on a computer or on desktop. These leads to create a mobile application that will recognize hand pose into text. American Sign Language Alphabet Translator Android Application: Hand Shapes into Text is a mobile application that translates American Sign Language Alphabet into text. These applications translate the ASL alphabet to a person who is not capable of understanding the language of a deaf-mute person. The application used the process of hand segmentation and feature extraction to get the information needed to extract the hand and data set that used to match the hand pose to display the equivalent letters. The study is successful in recognizing most of the alphabet handshape as long as it is well detected.

XKin: an open source framework for hand pose and gesture recognition using kinect

This work targets real-time recognition of both static hand-poses and dynamic hand-gestures in a unified open-source framework. The developed solution enables natural and intuitive hand-pose recognition of American Sign Language (ASL), extending the recognition to ambiguous letters not challenged by previous work. While hand-pose recognition exploits techniques working on depth information using texture-based descriptors, gesture recognition evaluates hand trajectories in the depth stream using angular features and hidden Markov models (HMM). Although classifiers come already trained on ASL alphabet and 16 uni-stroke dynamic gestures, users are able to extend these default sets by adding their personalized poses and gestures. The accuracy and robustness of the recognition system have been evaluated using a publicly available database and across many users. The XKin open project is available online (Pedersoli, XKin libraries. https://github.com/fpeder/XKin , 2013) under FreeBSD License for researchers in human---machine interaction.

Give Me a Sign: Decoding Complex Coordinated Hand Movements Using High-Field fMRI

Decoding movements from the human cortex has been a topic of great interest for controlling an artificial limb in non-human primates and severely paralyzed people. Here we investigate feasibility of decoding gestures from the sensorimotor cortex in humans, using 7 T fMRI. Twelve healthy volunteers performed four hand gestures from the American Sign Language Alphabet. These gestures were performed in a rapid event related design used to establish the classifier and a slow event-related design, used to test the classifier. Single trial patterns were classified using a pattern-correlation classifier. The four hand gestures could be classified with an average accuracy of 63 % (range 35–95 %), which was significantly above chance (25 %). The hand region was, as expected, the most active region, and the optimal volume for classification was on average about 200 voxels, although this varied considerably across individuals. Importantly, classification accuracy correlated significantly with consistency of gesture execution. The results of our study demonstrate that decoding gestures from the hand region of the sensorimotor cortex using 7 T fMRI can reach very high accuracy, provided that gestures are executed in a consistent manner. Our results further indicate that the neuronal representation of hand gestures is robust and highly reproducible. Given that the most active foci were located in the hand region, and that 7 T fMRI has been shown to agree with electrocorticography, our results suggest that this confined region could serve to decode sign language gestures for intracranial brain–computer interfacing using surface grids.

The Therapeutic Value of Adversity

The Therapeutic Value of Adversity

Hand Gesture Recognition using Neural Networks and Moment

Hand gesture recognition is a rich area of research and covers a wide scope of applications from human machine interaction (e.g in games) to Deaf people computer interface and from 3D animationto control of mechanical systems. In this work a new algorithm for hand gesture recognition is proposed and evaluated. This algorithm uses moment invariants for feature extraction and neural networks for classification. To evaluate the algorithm a subset from the ASL (American Sign Language) is used, this subset consists of the 26 American one-handed sign language alphabet as a training set for the system. The system then tested using images that are different from the training set in size,orientation and position and the results for these tests are presented and discussed.

Muscle synergies as a predictive framework for the EMG patterns of new hand postures

Synchronous muscle synergies have been suggested as a framework for dimensionality reduction in muscle coordination. Many studies have shown that synergies form a descriptive framework for a wide variety of tasks. We examined if a muscle synergy framework could accurately predict the EMG patterns associated with untrained static hand postures, in essence, if they formed a predictive framework. Hand and forearm muscle activities were recorded while subjects statically mimed 33 postures of the American Sign Language alphabet. Synergies were extracted from a subset of training postures using non-negative matrix factorization and used to predict the EMG patterns of the remaining postures. Across the subject population, as few as 11 postures could form an eight-dimensional synergy framework that allowed for at least 90% prediction of the EMG patterns of all 33 postures, including trial-to-trial variations. Synergies were quite robust despite using different postures in the training set, and also despite using a varied number of postures. Estimated synergies were categorized into those which were subject-specific and those which were general to the population. Population synergies were sparser than the subject-specific synergies, typically being dominated by a single muscle. Subject-specific synergies were more balanced in the coactivation of multiple muscles. We suggest as a result that global muscle coordination may be a combination of higher order control of robust subject-specific muscle synergies and lower order control of individuated muscles, and that this control paradigm may be useful in the control of EMG-based technologies, such as artificial limbs and functional electrical stimulation systems.

Fingerspell: Let Your Fingers Do the Talking

In this article we discuss an application that translates hand gestures of the American Sign Language (ASL) alphabet and converts them to text. The FingerSpell application addresses the communication barrier of the deaf and the hearing-impaired by eliminating the need for a third party with knowledge of the American Sign Language, allowing a user to use hand signs that will be translated to letters and words. Through the use of a data glove embedded with multiple input sensors, the application parses each hand and finger gesture signed by the user and detects unique cases which trigger application responses. Application responses come in both visual and auditory forms. The letter corresponding to the unique gesture will appear on the screen and play aloud using computer speech synthesis. The user may also sign an end of word gesture which cues the application to read the previous string of letters together, as one word. This allows a deaf person to communicate with another person without the need for the other person to understand sign language. In addition, this application can serve as a teaching tool for those learning how to sign. FingerSpell is a stepping stone to opening up lines of communication for the Deaf and hearing-impaired communities. Future research and advancements can extend the FingerSpell application to cover the full sign language (word signs) and alphabets of other sign languages.

The Emotional Effects of Writing About Stressful Experiences: An Exploration of Moderators

The purpose of this study was to examine the efficacy of incorporating mnemonic memory aids and having a subject teach another person a given task (peer tutoring) as a method of enhancing task acquisition and recall by the subject and to discuss the implications for occupational therapists who instruct clients in motor tasks such as therapeutic exercise programs. Sixty-seven college students were randomly assigned to one of three groups using different teaching methods for the purpose of learning a motor task, specifically the American Sign Language alphabet. Subjects who were taught using mnemonics and peer tutoring scored significantly better on post-testing two days following instruction than did the control groups. Use of these techniques did not increase direct teaching time by the instructor, nor did they incur additional costs. Thus, these techniques may be easily incorporated into client education to improve recall and performance.

Enhancing Motor Learning Through Peer Tutoring

The purpose of this study was to examine the efficacy of incorporating mnemonic memory aids and having a subject teach another person a given task (peer tutoring) as a method of enhancing task acquisition and recall by the subject and to discuss the implications for occupational therapists who instruct clients in motor tasks such as therapeutic exercise programs. Sixty-seven college students were randomly assigned to one of three groups using different teaching methods for the purpose of learning a motor task, specifically the American Sign Language alphabet. Subjects who were taught using mnemonics and peer tutoring scored significantly better on post-testing two days following instruction than did the control groups. Use of these techniques did not increase direct teaching time by the instructor, nor did they incur additional costs. Thus, these techniques may be easily incorporated into client education to improve recall and performance.