The Experts below are selected from a list of 15462 Experts worldwide ranked by ideXlab platform

Muhammad Ibn Ibrahimy - One of the best experts on this subject based on the ideXlab platform.

  • advances in electromyogram signal classification to improve the quality of life for the disabled and aged people
    Journal of Computer Science, 2010
    Co-Authors: M R Ahsan, Muhammad Ibn Ibrahimy, Othman Omran Khalifa
    Abstract:

    Problem statement: The social demands for the Quality Of Life (QOL) are increasing with the exponentially expanding silver generation. To i mprove the QOL of the disabled and elderly people, robotic researchers and biomedical engineers have b een trying to combine their techniques into the rehabilitation systems. Various biomedical signals (biosignals) acquired from a specialized tissue, or gan, or cell system like the nervous system are the driv ing force for the entire system. Examples of biosig nals include Electro-Encephalogram (EEG), Electrooculogram (EOG), Electroneurogram (ENG) and (EMG). Approach: Among the biosignals, the research on EMG signal processing and controlling is currently expanding in various directions. EMG signal based r esearch is ongoing for the development of simple, robust, user friendly, efficient interfacing device s/systems for the disabled. The advancement can be observed in the area of robotic devices, prosthesis limb, exoskeleton, wearable Computer, I/O for virt ual reality games and physical exercise equipments. An EMG signal based graphical controller or interfacin g system enables the physically disabled to use word processing programs, other personal Computer software and internet. Results: Depending on the application, the acquired and pro cessed signals need to be classified for interpreting into mechanical forc e or machine/Computer Command. Conclusion: This study focused on the advances and improvements on different methodologies used for EMG signal classification with their efficiency, flexibility a nd applications. This review will be beneficial to the EMG signal researchers as a reference and comparison st udy of EMG classifier. For the development of robust, flexible and efficient applications, this study ope ned a pathway to the researchers in performing futu re comparative studies between different EMG classific ation methods.

  • EMG Signal Classification for Human Computer Interaction : A Review
    European Journal of Scientific Reseacrh, 2009
    Co-Authors: Rakib Ahsan, Muhammad Ibn Ibrahimy
    Abstract:

    With the ever increasing role of Computerized machines in society, Human Computer Interaction (HCI) system has become an increasingly important part of our daily lives. HCI determines the eff ective utilization of the available information flow of the computing, communication, and display technol ogies. In recent years, there has been a tremendous interest in introduc ing intuitive interfaces that can recognize the user's body movements and translate them into machin e Commands. For the neural linkage with Computers, various biomedical signals (biosi gnals) can be used, which can be acquired from a specialized tissue, organ, or cell system like the nervous system. Examples include Electro-Encephalogram (EEG), Electroocul ogram (EOG), and El ectromyogram (EMG). Such approaches are extremely valuable to physically disabled persons. Many attempts have been made to use EMG signal from gesture for developing HCI. EMG signal processing and controller work is currently proceeding in various direction including the development of continuous EMG signal classifi cation for graphical controller, that enables the physically disabled to use word proce ssing programs and other personal Computer software, internet. It also enable manipulati on of robotic devices, prosthesis limb, I/O for virtual reality games, physical exercise equipm ents etc. Most of the developmental area is based on pattern recognition using neural networks. The EMG controller can be programmed to perform gesture recognition base d on signal analysis of groups of muscles action potential. This review paper is to di scuss the various methodologies and algorithms used for EMG signal classification for the purpose of interpreting the EMG signal into Computer Command.

Rakib Ahsan - One of the best experts on this subject based on the ideXlab platform.

  • EMG Signal Classification for Human Computer Interaction : A Review
    European Journal of Scientific Reseacrh, 2009
    Co-Authors: Rakib Ahsan, Muhammad Ibn Ibrahimy
    Abstract:

    With the ever increasing role of Computerized machines in society, Human Computer Interaction (HCI) system has become an increasingly important part of our daily lives. HCI determines the eff ective utilization of the available information flow of the computing, communication, and display technol ogies. In recent years, there has been a tremendous interest in introduc ing intuitive interfaces that can recognize the user's body movements and translate them into machin e Commands. For the neural linkage with Computers, various biomedical signals (biosi gnals) can be used, which can be acquired from a specialized tissue, organ, or cell system like the nervous system. Examples include Electro-Encephalogram (EEG), Electroocul ogram (EOG), and El ectromyogram (EMG). Such approaches are extremely valuable to physically disabled persons. Many attempts have been made to use EMG signal from gesture for developing HCI. EMG signal processing and controller work is currently proceeding in various direction including the development of continuous EMG signal classifi cation for graphical controller, that enables the physically disabled to use word proce ssing programs and other personal Computer software, internet. It also enable manipulati on of robotic devices, prosthesis limb, I/O for virtual reality games, physical exercise equipm ents etc. Most of the developmental area is based on pattern recognition using neural networks. The EMG controller can be programmed to perform gesture recognition base d on signal analysis of groups of muscles action potential. This review paper is to di scuss the various methodologies and algorithms used for EMG signal classification for the purpose of interpreting the EMG signal into Computer Command.

Othman Omran Khalifa - One of the best experts on this subject based on the ideXlab platform.

  • advances in electromyogram signal classification to improve the quality of life for the disabled and aged people
    Journal of Computer Science, 2010
    Co-Authors: M R Ahsan, Muhammad Ibn Ibrahimy, Othman Omran Khalifa
    Abstract:

    Problem statement: The social demands for the Quality Of Life (QOL) are increasing with the exponentially expanding silver generation. To i mprove the QOL of the disabled and elderly people, robotic researchers and biomedical engineers have b een trying to combine their techniques into the rehabilitation systems. Various biomedical signals (biosignals) acquired from a specialized tissue, or gan, or cell system like the nervous system are the driv ing force for the entire system. Examples of biosig nals include Electro-Encephalogram (EEG), Electrooculogram (EOG), Electroneurogram (ENG) and (EMG). Approach: Among the biosignals, the research on EMG signal processing and controlling is currently expanding in various directions. EMG signal based r esearch is ongoing for the development of simple, robust, user friendly, efficient interfacing device s/systems for the disabled. The advancement can be observed in the area of robotic devices, prosthesis limb, exoskeleton, wearable Computer, I/O for virt ual reality games and physical exercise equipments. An EMG signal based graphical controller or interfacin g system enables the physically disabled to use word processing programs, other personal Computer software and internet. Results: Depending on the application, the acquired and pro cessed signals need to be classified for interpreting into mechanical forc e or machine/Computer Command. Conclusion: This study focused on the advances and improvements on different methodologies used for EMG signal classification with their efficiency, flexibility a nd applications. This review will be beneficial to the EMG signal researchers as a reference and comparison st udy of EMG classifier. For the development of robust, flexible and efficient applications, this study ope ned a pathway to the researchers in performing futu re comparative studies between different EMG classific ation methods.

M R Ahsan - One of the best experts on this subject based on the ideXlab platform.

  • advances in electromyogram signal classification to improve the quality of life for the disabled and aged people
    Journal of Computer Science, 2010
    Co-Authors: M R Ahsan, Muhammad Ibn Ibrahimy, Othman Omran Khalifa
    Abstract:

    Problem statement: The social demands for the Quality Of Life (QOL) are increasing with the exponentially expanding silver generation. To i mprove the QOL of the disabled and elderly people, robotic researchers and biomedical engineers have b een trying to combine their techniques into the rehabilitation systems. Various biomedical signals (biosignals) acquired from a specialized tissue, or gan, or cell system like the nervous system are the driv ing force for the entire system. Examples of biosig nals include Electro-Encephalogram (EEG), Electrooculogram (EOG), Electroneurogram (ENG) and (EMG). Approach: Among the biosignals, the research on EMG signal processing and controlling is currently expanding in various directions. EMG signal based r esearch is ongoing for the development of simple, robust, user friendly, efficient interfacing device s/systems for the disabled. The advancement can be observed in the area of robotic devices, prosthesis limb, exoskeleton, wearable Computer, I/O for virt ual reality games and physical exercise equipments. An EMG signal based graphical controller or interfacin g system enables the physically disabled to use word processing programs, other personal Computer software and internet. Results: Depending on the application, the acquired and pro cessed signals need to be classified for interpreting into mechanical forc e or machine/Computer Command. Conclusion: This study focused on the advances and improvements on different methodologies used for EMG signal classification with their efficiency, flexibility a nd applications. This review will be beneficial to the EMG signal researchers as a reference and comparison st udy of EMG classifier. For the development of robust, flexible and efficient applications, this study ope ned a pathway to the researchers in performing futu re comparative studies between different EMG classific ation methods.

Michael Eddington - One of the best experts on this subject based on the ideXlab platform.

  • blockchain Computer data distribution
    2017
    Co-Authors: Adam Cecchetti, Michael Eddington
    Abstract:

    Blockchain distribution of Computer data is disclosed. Computer data can comprise Computer code, a Computer code segment, a Computer Command, or a block of Computer data, which can be employed by a device to patch software, change a device state, or synchronize data between devices. Blockchain distribution can provide benefits in a heterogeneous device environment, facilitate ad hoc device synchronization, and embody a distributed patch and communications network. Devices can receive a blockchain block from another device and, in some embodiments, enable other devices to access the block from the device. In some embodiments, devices can discard irrelevant blocks, however, an entire blockchain can be reconstructed where partial blockchains can be received from more than one device. Additionally, checkpoint blocks can enable devices to navigate the blockchain efficiently by skipping over known irrelevant blocks.