GESTURE TECHNOLOGY USING ACCELEROMETER ASSIGNMENT SOLUTION

INTRODUCTION

Gesture recognition is a topic in computer science and language technology with the goal of interpreting human gestures via mathematical algorithms. Gestures can originate from any bodily motion or state but commonly originate from the face or hand. Current focuses in the field include emotion recognition from the face and hand gesture recognition. Many approaches have been made using cameras and computer vision algorithms to interpret sign language. However, the identification and recognition of posture, gait, proxemics, and human behaviours is also the subject of gesture recognition techniques.

Gesture recognition can be seen as a way for computers to begin to understand human body language, thus building a richer bridge between machines and humans than primitive text user interfaces or even GUIs (graphical user interfaces), which still limit the majority of input to keyboard and mouse.

Gesture recognition enables humans to interface with the machine (HMI) and interact naturally without any mechanical devices. Using the concept of gesture recognition, it is possible to point a finger at the computer screen so that the cursor will move accordingly. This could potentially make conventional input devices such as mouse, keyboards and even touch-screens redundant. 

Accelerometer sensors measure the acceleration experienced by the sensor and anything to which the sensor is directly attached. Accelerometer sensors have many applications. The most commercial application is impact sensors for triggering airbag deployment in automobiles: when the acceleration exceeds 30 to 50 g’s, an accident is assumed and the airbags deploy.

The goal of this thesis is to measure the three-dimensional acceleration of human hand motion with adequate accuracy and precision, the necessary bandwidth for normal human motion, and the amplitude range required for the highest normal accelerations. At the same time, the physical presence of the sensor should not alter the hand motion. The application of measuring something sensitive to external mass like the human hand14 requires the accelerometer sensor to be extremely small and lightweight. 

Although this technology is still in its infancy, applications are beginning to appear. Gesture recognition can be conducted with techniques from computer vision and image processing.

ABSTRACT

Sensors allow detection, analysis, and recording of physical phenomenon that are difficult to otherwise measure by converting the phenomenon into a more convenient signal. Sensors convert physical measurements such as displacement, velocity, acceleration, force, pressure, chemical concentration, or flow into electrical signals. The value of the original physical parameter can be back-calculated from the appropriate characteristics of the electrical signal (amplitude, frequency, pulse-width, etc.). Electrical outputs are very convenient because there are well known methods (and often commercially available off-the-shelf solutions) for filtering and acquiring electrical signals for real-time or subsequent analysis. Sensor size is often important, and small sensors are desirable for many reasons including easier use, a higher sensor density, and lower material cost. 

A revolution in micro fabricated sensors occurred with the application of semiconductor fabrication technology to sensor construction. By etching and depositing electrically conductive and nonconductive layers on silicon wafers, the sensor is created with the electrical sensing elements already built into the sensor. The products created using these techniques are called microelectromechanical systems or MEMS. Other examples of MEMS are the application elements of inkjet printers1. The entire MEMS sensor is fabricated on a small section of a single silicon wafer or a stack of wafers bonded together. Reducing the area of the sensor layout both decreases the area of the sensor and increases the number of sensors produced on each wafer. The silicon dies are then packaged in chip carriers for use.

Many types of inertial sensors have been fabricated as MEMS. The original MEMS sensors were pressure sensors using piezoresistive sensing elements, while current MEMS sensors include accelerometers (measuring either linear or angular acceleration), shear stress sensors, chemical concentration sensors, and gyroscopes. This project uses single-axis MEMS linear accelerometer sensors fabricated at MIT to create a three-dimensional accelerometer sensor system suitable for measuring the acceleration of human hand motion.

MOTIVATION

Nowadays the skills of communication are vital to the society. Whether in industrial environments or plain social human activities there is a constant need of good communication skills. In a work environment there may be a need to communicate to a machine in a remote, secure, practical or non-intrusive manner. Such requirements are achieved when it is possible to represent the normal human activity by a virtual representation. When related to human-to-human activities, communication can become complicate when one of the interlocutors does not know the other’s language. This is the case when two persons try to communicate and one is hearing impaired and knows sign language, while the other is not and does not know such language. Being possible to intermediate the communication between this two persons allows a great social achievement with an immeasurable value to those who carry that limitation. Science and Industry are constantly evolving and today it is possible to create a system capable of facilitating communications, as mentioned above, and yet be portable, reliable, self-powered and very simple to use.

It is well-documented that the increasing older population is becoming one of the major challenges for developed countries. The population will continue to grow older in the future, whilst lower birth rates will result in fewer younger people to care for the elderly. It is widely accepted that we need to address this issue through a range of initiatives, including researching the potential for technology to extend independent living. Such initiatives may also support younger people, including those with disabilities.