国際生活機能分類(International Classification of Functioning, Disability and Health: ICF)にみた福祉・保健・医療の専門職協働における連携に関する貢献と課題

Deep Learning for EEG motor imagery classification based on multi-layer CNNs feature fusion

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Medical Instrumentation Application And Design

The Disabled State

Indoor localization has been an active research field for decades. Location determination is normally accomplished using the Global Positioning System (GPS) for outdoor environment. For indoor environment GPS does not work due to the lack of the sight line to satellite. Several technologies are deployed to construct an efficient indoor positioning system. A compromise between cost, accuracy and time deployment should be taken into account while choosing the best system. Most indoor localization approaches adopt WLAN as the basic schema of localizing due to many advantages. This paper gives an overview of indoor localization technologies, methods and performance metrics, and makes benchmark of existing solutions of Wi-Fi fingerprinting technique with detailed presentation of performances and limitations related to most important recent advances.

Survey on indoor localization system and recent advances of WIFI fingerprinting technique

The EEG frequency bands are brain rhythms that indicate the activity level of the brain. This paper investigates the effects of the sub-band frequency on the classification of motor imagery of hand movements. Ten sub-bands of 10Hz width between 0 to 100 Hz are chosen. Band power features of the sub-bands are classified using a neural classifier. Motor imagery signals recorded from the C3 and C4 channels for four tasks are used in the analysis. Classification rates of 89.23% 94.47% were achieved for sub-band frequencies ranging from 21Hz to 40 Hz for motor imagery signals. Results show that apart from mu and beta, low gamma frequencies are also better suited for motor imagery classification

An Analysis of the Effect of EEG Frequency Bands on the Classification of Motor Imagery Signals

An auditory loss is one of the most common disabilities present in newborns and infants in the world. A conventional hearing screening test's applicability is limited as it requires a feedback response from the subject under test. To overcome such problems, the primary focus of this study is to develop an intelligent hearing ability level assessment system using auditory evoked potential signals (AEP). AEP signal is a non-invasive tool that can reflect the stimulated interactions with neurons along the stations of the auditory pathway. The AEP responses of fourteen normal hearing subjects to auditory stimuli (20 dB, 30 dB, 40 dB, 50 dB and 60 dB) were derived from electroencephalogram (EEG) recordings. Higuchi's fractal method is applied to extract the fractal features from the recorded AEP signals. The extracted fractal features were then associated to different hearing perception levels of the subjects. Feed-forward and feedback neural networks are employed to distinguish the different hearing perception levels. The performance of the proposed intelligent hearing ability level assessment found to exceed 85% accuracy. This study indicates that AEP responses to the auditory stimuli to the normal hearing persons can predict the higher order auditory stimuli followed by the lower order auditory stimuli and consequently the state of auditory development of subjects

A machine learning approach for distinguishing hearing perception level using auditory evoked potentials

Electrooculography based bio signals have been used and applied as a control signal in several Human Computer Interactions. EOG is a technique of recording cornealretinal potential associated with eye movement. An HCI captures and decodes EOG signals and transforms human eye movement into actions. This paper proposes algorithms for identifying eleven eye movement signals acquired from twenty subjects using static and dynamic networks. Convolution technique is used to extract the features. These features are trained and tested with two neural networks, namely time delay neural network and feed forward neural network. The results obtained are compared with Singular Value Decomposition features for same networks. Classification accuracies varied from 90.99% and 90.10% for convolution features and 90.88% and 89.92% for SVD features using time delay neural network and feed forward neural network respectively. From the results it is observed that Convolution features using Time Delay Neural Network has better classification rates in comparison with SVD features.

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Idendifying Eye Movements using Neural Networks for Human Computer Interaction

Sign language recognition is one of the most promising sub-fields in gesture recognition research. A sign language is a language which, instead of acoustically conveyed sound patterns, uses visually transmitted sign patterns. Sign languages are commonly developed for hearing impaired communities, which can include interpreters, friends and families of hearing impaired people as well as people who are hard of hearing themselves. Developing a sign language recognition system will help the hearing impaired to communicate more fluently with the normal people. A simple sign language recognition system employing skin color segmentation and Neural Network has been developed. A simple segmentation process is carried out to separate the right and left hand regions from the image frame and in the preprocessing stage a simple vertical interleaving method has been proposed to reduce the size of the image. The 2D moment invariant features of the right and left hand interleaved image is obtained as features. Using the interleaved 2D-moment Invariant features, a simple neural network model has been developed. The system has been implemented and tested for its validity. Experimental results show that the system has a recognition rate of 92.58%.

A phoneme based sign language recognition system using 2D moment invariant interleaving feature and Neural Network

Nowadays voice disorders are increasing dramatically due to the modern way of life. Most of the voice disorders cause changes in the voice signal. Acoustic analysis on the speech signal could be a useful tool for diagnosing voice disorders. This paper applies Mel-scaled wavelet packet transform (Mel-scaled WPT) based features to perform accurate diagnosis of voice disorders. A Functional Link Neural Network (FLNN) is developed to test the usefulness of the suggested features. Two simple modifica- tions are newly proposed in the FLNN architecture to improve the classification accuracy. In the first ar- chitecture, a hidden layer is newly introduced in a FLNN and trained by Back Propagation (BP) proce- dure. In the second architecture, the Integral and Derivative controller concepts are introduced to the neurons in the hidden layer and the network is trained by BP procedure. The performance is compared with conventional neural network model. The results prove that the proposed FLNN gives very promising classification accuracy and suggested features can be employed clinically to diagnose the voice disorders.

Paulraj M P

Papers

An Analysis of the Effect of EEG Frequency Bands on the Classification of Motor Imagery Signals

A machine learning approach for distinguishing hearing perception level using auditory evoked potentials

Idendifying Eye Movements using Neural Networks for Human Computer Interaction

A phoneme based sign language recognition system using 2D moment invariant interleaving feature and Neural Network

Diagnosis of Voice Disorders using Mel Scaled WPT and Functional Link Neural Network