These days, the Internet of things (IoT) research is driving large-scale development and deployment of many innovative applications. IoT has indeed brought many smart applications to the doorstep of users. IoT has also made it possible to connect many sensors and control equipment. Here, the authors address an important application for physically challenged. The authors present a brain–computer interface (BCI) system to lock/unlock a wheelchair and control its movements using BCI. The approach presented here uses NeuroSky's MindWave Mobile, a single electrode electroencephalography (EEG) headset that can be connected to any Bluetooth-enabled system. The raw EEG data from the headset is processed on an Android mobile device to extract the electromyography (EMG) patterns that occur due to eye blinks and activity of muscles in the jaw. These patterns are used to control the movement of a wheelchair in all possible directions. A biometric security system is provided to lock and unlock the wheelchair by extracting the information about different brain waves from the raw EEG signal. In this system, only the user knows the password which is generated using brain waves and it can lock/unlock the wheelchair and control it. The proposed system was verified and evaluated using a prototype.

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Indoor lighting systems need to be designed to balance energy consumption and the visual comfort of occupants. Achieving this goal with multiple luminaires and sensors is, however, not a simple problem. Lighting control systems need to adjust the dimming levels of luminaires in real time based on occupancy conditions and external daylight changes. We propose a system based on visible light communication to monitor and control artificial lighting in a robust manner. In our system, luminaires modulate the emitted light to broadcast basic control information while fulfilling their main purpose of illumination. Based on the broadcasted information, luminaires use collocated light sensors to estimate the optical channel gains and daylight contributions. This information is then used in a control algorithm to determine the dimming levels of luminaires under specified illumination constraints. We provide an analytical framework, simulations, and an empirical evaluation of our approach in an office space. We compare our method with a state-of-art lighting control system that uses radio transceivers to communicate information, and thus, cannot monitor optical channels in real time. Our results show that while the radio-based system may underilluminate and even oscillate around the desired illumination due to large reflectance changes in the environment, our method provides stable illumination close to desired levels.
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