FPGA Implementation of Eye Movement Detection Algorithm

In this paper, we propose an simple and efficient VLSI hardware architecture is implemented for eye movement detection. For Eye movement detection reading activity Electrooculography (EOG) signal is considered. Here, for denoising the noisy EOG signal efficient FIR filter and for decomposition of denoised EOG signal an efficient Haar wavelet transform architecture is used respectively. The modified VLSI hardware architecture method detected the saccade (left movement of eye and right movement of eye) and blink efficiently. The hardware architecture of the eye movement detection algorithm functionality is verified by using Xilinx System Generator hardware co-simulation tool. The eye movement detection algorithm is implemented on the ZedBoard FPGA using Xilinx Vivado design suite.

A Noble Classification Framework for Data Glove Classification of a Large Number of Hand Movements

The recognition of hand movements is an important method for human-computer interaction (HCI) technology, and it is widely used in virtual reality and other HCI areas. While many valuable efforts have been made, efficient ways to capture over 20 types of hand movements with high accuracy by one data glove are still lacking. This paper addresses a new classification framework for 52 hand movements. This classification framework includes the following two parts: the movement detection algorithm and the movement classification algorithm. The fine K-nearest neighbor (Fine KNN) is the core of the movement detection algorithm. The movement classification algorithm is composed of downsampling in data preparation and a new deep learning network named the DBDF network. Bidirectional Long Short-Term Memory (BiLSTM) is the main part of the DBDF network. The results of experiments using the Ninapro DB1 dataset demonstrate that our work can classify more types of hand movements than related algorithms with a precision of 93.15%.

Eyeball Movement Detection To Control Smart Wheelchair Using Eye Aspect Ratio (EAR)

Many developed technology's with an aim of helping the disabilities. One of them is a wheelchair. It is the most common stuff that used for helping disabilities as a tool for mobilization. There are two types of wheelchair. The first is the manual wheelchair, operated by hand. The second is an electrical wheelchair, that operated by joystick or other electric device. This research proposed a mechanism to control the wheelchair by using an eye movement. It could be used especially for people with multiple disabilities (hand and foot defects), so they can take an advantage of their eyeballs as a tool to control wheelchair movement. There are five options for controlling the wheelchair (leftward, rightward, upward, downward and center). Leftward, rightward and center used for control direction of smart wheelchair. Furthermore, upward and downward of eye movements used to control the speed of smart wheelchair. Upward command used to increase the speed. Meanwhile, down-ward used to decrease the speed (stop). The proposed method used EAR (Eye Aspect Ratio), which divided into three regions based on sector area, for determining the directions of the eyeball movement. EAR is the value that represents the ratio between the upper eyelid and lower eyelid. The result obtained high accuracy