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Séminaire avec Deok-Hwan Kim
04/25/2016 @ 14:00 - 17:00
Du 20 au 29 avril 2016, le LITIS recevra à l’UFR des Sciences et Techniques du Havre Deok-Hwan KIM en provenance de l’Université d’INHA en Corée du Sud.
Parmi ses thèmes de recherche, nous pouvons citer les suivants :
o Software Defined Storage & Networking and Open Source based Cloud Platform (OpenStack)
o IoT Development Platform (Arduino, Raspberry Pi, Intel Edison, Galileo, Samsung Artik)
o Big Data Processing (Data encoding/decoding, Compression, Data Chunking& Striping in Flash array)
Il présentera ses travaux lors d’un séminaire intitulé »Research on Wearable Mobile Healthcare Platform ».
Ce séminaire se tiendra lundi 25 avril à 14h dans la Salle de séminaire du LITIS – Le Havre.
Le résumé de sa présentation sera le suivant :
« The world of future healthcare is transforming the digital health landscape. Mobile networks are the enabler of digital healthcare while wearable technology is growing rapidly and is expected to be the next big wave in healthcare similar to that created by the smartphone revolution. The aim of this talk is to introduce our studies in the measurement and analysis of bio-signals and images in clinical medicine and the biological sciences. Emphasis is placed on contributions dealing with the practical, applications-led research on the use of methods and devices in clinical diagnosis, patient monitoring and management. First of all, we review human-computer interaction (HCI) technology that translates brain or bio-signals into simple commands that can control external devices or into messages with which one can communicate. For the neural linkage with computers, various biomedical signals(bio-signals) can be used, which can be acquired from a specialized tissue, organ, or cell system like the nervous system. Examples include Electro-Encephalogram (EEG), Electrooculogram (EOG), and Electromyogram (EMG). Such approaches are extremely valuable to physically disabled persons. Our attempts have been made to use EOG signal to measure the movement of the eye for estimating visual fatigue caused by 2D and 3D displays, develop EMG signal classification for manipulating robotic devices, prosthesis limb, which enables the rehabilitation of lower limb amputees by using powered artificial prosthesis, detect seizure to help diagnosis of epilepsy which requires a long-term electroencephalogram (EEG) monitoring. Most of the developmental area is based on pattern recognition using neural networks. The presentation will be continuously dedicated to recent developments and improvements of human identification based on the gait and Korean monophthong recognition based on facial surface EMG signal. The latter can be enhanced as silent speech recognition using muscle activity. Besides, virtual desktop infrastructure is used to allocate process, memory.and bio-signal tool boxes using GPGPU are used to analyze EOG, EEG, EMG signals. »