Kamarulafizam Ismail

Human facial neural activities and gesture recognition for machine-interfacing applications.

The authors present a new method of recognizing different human facial gestures through their neural activities and muscle movements, which can be used in machine-interfacing applications. Human–machine interface (HMI) technology utilizes human neural activities as input controllers for the machine. Recently, much work has been done on the specific application of facial electromyography (EMG)-based HMI, which have used limited and fixed numbers of facial gestures. In this work, a multipurpose interface is suggested that can support 2–11 control commands that can be applied to various HMI systems. The significance of this work is finding the most accurate facial gestures for any application with a maximum of eleven control commands. Eleven facial gesture EMGs are recorded from ten volunteers. Detected EMGs are passed through a band-pass filter and root mean square features are extracted. Various combinations of gestures with a different number of gestures in each group are made from the existing facial gestures. Finally, all combinations are trained and classified by a Fuzzy c-means classifier. In conclusion, combinations with the highest recognition accuracy in each group are chosen. An average accuracy >90% of chosen combinations proved their ability to be used as command controllers.

Design and development of standalone DSP prototype for QT intercal processing and monitoring

This paper describes the development of stand alone DSP hardware for QT interval monitoring and assessment. The QT interval has been known to be an important indicator prior to Myocardial Infarction (MI) and it is important to observe and monitor any changes in the period of the QT interval. The system consists of three units. The first unit includes floating point digital signal processor, TMS320VC33 which is selected for the development of signal processing with memory circuit. The following unit includes the development of signal monitoring displays circuit with universal serial bus (USB) interface. The third unit includes the development of analog front end (AFE) circuit and merged with signal conditioning circuit. This paper discusses the principle of system design and the system has the advantages to maximize utilization by allowing modification, reconfiguration, portable, cost effective and run in stand-alone operation. As well as eligibility of on board pre-program algorithm for QT analysis which existing ECG monitoring equipment are lacking. The system had been successfully tested with algorithm for QT analysis and it is validated with healthy subjects data files from PTB diagnostic ECG data base.