A Morse-code recognition system with LMS and matching algorithms for persons with disabilities

MOUTH-CONTROLLED TEXT INPUT DEVICE WITH SLIDING FUZZY ALGORITHM FOR INDIVIDUALS WITH SEVERE DISABILITIES

This study presents a novel mouth-controlled text input (McTin) device that enables users with severe disabilities to access the keyboard and mouse facilities of a standard personal computer via the input of suitable Morse codes processed by sliding window averaging and a fuzzy recognition algorithm. The device offers users the choice of four different modes of operation, namely keyboard-, mouse-, practice-, and remote-control mode. In the keyboard-mode, the user employs a simple mouth-controlled switch to input Morse codes, which the McTin device then translates into the corresponding keyboard character, symbol, or function. In the mouse-mode, the user is able to control the direction of the mouse movement and access the various mouse functions by inputting a maximum of four Morse code elements. The remote-control-mode gives the user the ability to control some of the functions of household appliances such as TV, air conditioner, fan, and lamp. Finally, the practice-mode employs a training environment within which the user may be trained to input Morse codes accurately and quickly via the mouth-controlled switch. Although this study presents the use of a mouth-controlled switch for the input of Morse codes, the form of the input device can be modified to suit the particular requirements of users with different degrees of physical disability. The proposed device has been tested successfully by two users with severe spinal cord injuries to generate text-based articles, send e-mails, draw pictures, and browse the Internet.

Enabling self-directed computer use for individuals with cerebral palsy: a systematic review of assistive devices and technologies

AIM

The purpose of this study was to systematically review published evidence on the development, use, and effectiveness of devices and technologies that enable or enhance self-directed computer access by individuals with cerebral palsy (CP).

METHODS

Nine electronic databases were searched using keywords 'computer', 'software', 'spastic', 'athetoid', and 'cerebral palsy'; the reference lists of articles thus identified were also searched. Thirty articles were selected for review, with 23 reports of development and usability testing of devices and seven evaluations of algorithms to increase computer recognition of input and cursor movements.

RESULTS

Twenty-four studies had fewer than 10 participants with CP, with a wide age range of 5 to 77 years. Computer task performance was usually tested, but only three groups sought participant feedback on ease and comfort of use. International standards exist to evaluate effectiveness of non-keyboard devices, but only one group undertook this testing. None of the study designs were higher than American Academy for Cerebral Palsy and Developmental Medicine level IV.

INTERPRETATION

Access solutions for individuals with CP are in the early stages of development. Future work should include assessment of end-user comfort, effort, and performance as well as design features. Engaging users and therapists when designing and evaluating technologies to enhance computer access may increase acceptance and improve performance.

Fuzzy support vector machines for adaptive Morse code recognition

Morse code is now being harnessed for use in rehabilitation applications of augmentative-alternative communication and assistive technology, facilitating mobility, environmental control and adapted worksite access. In this paper, Morse code is selected as a communication adaptive device for persons who suffer from muscle atrophy, cerebral palsy or other severe handicaps. A stable typing rate is strictly required for Morse code to be effective as a communication tool. Therefore, an adaptive automatic recognition method with a high recognition rate is needed. The proposed system uses both fuzzy support vector machines and the variable-degree variable-step-size least-mean-square algorithm to achieve these objectives. We apply fuzzy memberships to each point, and provide different contributions to the decision learning function for support vector machines. Statistical analyses demonstrated that the proposed method elicited a higher recognition rate than other algorithms in the literature.

A NOVEL APPROACH TO MANDARIN MORSE CODE RECOGNITION

Morse code, with its single switch operation, has been shown to be a valuable tool in assistive technology, augmentative and alternative communication, and rehabilitation for people with various neuromuscular diseases, such as amyotrophic lateral sclerosis, multiple sclerosis, and muscular dystrophy. A major hindrance is a stable typing rate, which is strictly required for the recognition of Morse code characters, especially for persons with severe disabilities. In this paper, a combination of two methods, support vector machines and the variable degree variable step least-mean-square algorithm was used to increase the prediction accuracy for Mandarin Morse code recognition. Experimental findings revealed that the proposed method results in a better recognition rate when compared to alternative methods from the literature.

Morse code application for wireless environmental control systems for severely disabled individuals

Some physically-disabled people with neuromuscular diseases such as amyotrophic lateral sclerosis, multiple sclerosis, muscular dystrophy, or other conditions that hinder their ability to write, type, and speak, require an assistive tool for purposes of augmentative and alternative communication in their daily lives. In this paper, we designed and implemented a wireless environmental control system using Morse code as an adapted access communication tool. The proposed system includes four parts: input-control module; recognition module; wireless-control module; and electronic-equipment-control module. The signals are transmitted using adopted radio frequencies, which permits long distance transmission without space limitation. Experimental results revealed that three participants with physical handicaps were able to gain access to electronic facilities after two months' practice with the new system.

UNSTABLE MORSE CODE RECOGNITION SYSTEM WITH EXPERT-GATING NEURAL NETWORK

The standard Morse code defines the tone ratio (dash/dot) and the silent ratio (dash-space/dotspace) as 3:1. Since human typing ratio can't keep this ratio precisely and the two ratios —tone ratio and silent ratio—are not equal, the Morse code can't be recognized automatically. The requirement of the standard ratio is difficult to satisfy even for an ordinary person. As for the unstable Morse code typing pattern, the auto-recognition algorithms in the literature are not good enough in applications. The disabled persons usually have difficulty in maintaining a stable typing speeds and typing ratios, we therefore adopted an Expert-Gating neural network model to implement in single chip and recognize online unstable Morse codes. Also, we used another method—a linear back propagation recalling algorithm, to implement in single chip and recognize unstable Morse codes. From three person tests: Test one is a cerebral palsy; Test two is a beginner: Test three is a skilled expert, we have the results: in the experiment of test one, we have 91.15% (use 6 characters average time series as thresholds) and 91.54% (learning 26 characters) online average recognition rate; test two have 95.77% and 96.15%, and test three have 98.46% and 99.23% respectively. As for linear back propagation recalling method online recognized rate, we have the results from test one: 92.31% online average recognition rate; test two: 96.15%; and test three 99.23% respectively. So, we concluded: The Expert-Gating neural network and the linear back propagation recalling algorithm have successfully overcome the difficulty of analyzing a severely online unstable Morse code time series and successfully implement in single chip to recognize online unstable Morse code.

The application of the neural network on Morse code recognition for users with physical impairments

Morse code is a simple, speedy and low cost means of communication composed of a series of dots, dashes and space intervals. Each tone element (either a dot, dash or space interval) is transmitted by sending a signal for a defined length of time. This poses a challenge as the automatic recognition of Morse code is dependent upon maintaining a stable typing rate. In this paper, a suitable adaptive automatic recognition method, combining the least-mean-square (LMS) algorithm with a neural network, was applied to this problem. The method presented in this paper is divided into five modules: space recognition, tone recognition, learning process, adaptive processing and character recognition. Statistical analyses demonstrated that the proposed method elicited a better recognition rate in comparison with other methods in the literature.

Unstable Morse Code recognition system with back propagation neural network for person with disabilities

A Morse code auto-recognition system is limited by stable typing speed and stable typing ratio from long to short intervals. For an unstable Morse code typing pattern, the auto-recognition algorithms in the literature are not good enough for applications. This paper adopted a neural network to recognize unstable Morse codes. From an experiment on a teenager with cerebral palsy, the neural network has an average recognition rate up to 93.2%. The recognition rate from an amputee aged 40, who used a prosthesis for typing, it is 97.2% on average. When we compare this to 99.2% for the recognition rate from a skilled expert, the result is quite promising. The neural network has successfully overcome the difficulty of analysing a severely unstable Morse code time series. Since the human typing speed is quite slow in comparison to signal processing by the computer, it also makes it possible to use a neural network for real-time signal recognition.

Unstable Morse code recognition with adaptive variable-ratio threshold prediction for physically disabled persons

With one or two switches, Morse code could provide an effective alternative communication channel for individuals with physical limitations. However, most of the physically disabled persons have difficulties in maintaining a stable typing of Morse code, and hence the automated recognition of unstable Morse code is becoming more on demand. In this study, an adaptive variable-ratio threshold prediction (AVRTP) algorithm is proposed to analyze the Morse code time series with variable unit time period and ratio. Two least-mean-square (LMS) predictors are applied to track the dot interval and the dot-dash difference concurrently, and then a predicted threshold based on a variable-ratio decision rule is used to distinguish between dots and dashes. The same method is also applied to identify character-spaces. By the adaptive prediction of variable-ratio threshold, AVRTP has successfully overcome the difficulty of analyzing severely unstable Morse code time series and outperformed the previously proposed adaptive unstable-speed prediction (AUSP) algorithm and LMS and matching (I,MS&M) algorithm. This study concludes with a computer simulation and a preliminary clinical evaluation that demonstrate AVRTP as an efficient and reliable method for unstable Morse code recognition.

Adaptive Morse code communication system for severely disabled individuals

Morse code with an easy-to-operate, single switch input system has been shown to be an excellent communication adaptive device. Because maintaining a stable typing rate is not easy for the disabled, the automatic recognition of Morse code is difficult. Therefore, a suitable adaptive automatic recognition method is needed. This paper presents the application of a Least-Mean-Square algorithm to adaptive Morse code recognition for persons with impaired hand coordination and dexterity. Four processes are involved in this adaptive Morse code recognition method: space recognition, tone recognition, adaptive processing, and character recognition. Statistical analyses demonstrated that the proposed method results in a better recognition rate for the participants tested in comparison to other methods from the literature.