No abstract

No abstract

Electroencephalography (EEG) based smart home control system is one of the major applications of Brain Computer Interface (BCI) that allows disabled people to maximize their capabilities at home. A Brain Computer Interface (BCI) is a device that enables severely disabled people to communicate and interact with their environments using their brain waves. In this project, the scope includes Graphical User Interface (GUI) acts as a control and monitoring system for home appliances which using BCI as an input. Hence, NeuroSky MindWave headset is used to detect EEG signal from brain. Furthermore, a prototype model is developed using Raspberry Pi 3 Model B+, 4 channels 5V relay module, light bulb and fan. The raw data signal from brain wave is being extracted to operate the home appliances. Besides, the results agree well with the command signal used during the experiment. Lastly, the developed system can be easily implemented in smart homes and has high potential to be used in smart automation.

No abstract

Abstract-FES induced movement control is a significantly challenging area due to complexity and non-linearity of musculoskeletal system. The goal of this study is to design a cycle-to-cycle control of FES-induced swinging motion. In this approach only the quadriceps muscle is stimulated by controlling the amount of stimulation pulsewidth. This time dependent behaviour is successfully compensated for using a cycle-to cycle fuzzy controller, which computes the amount of knee extension stimulation on the basis of the achieved flexion angle in previous cycles.The capability of fuzzy control in automatic generation of stimulation burst duration is assessed in computer simulations using a musculo-skeletal model. This paper presents the development of a fuzzy logic control scheme based on discretetime cycle to cycle control strategies without predefined trajectory. The results show the effectiveness of the approach in controlling FES-induced swinging motion