(A) gesture classifier tree algorithm for motion gesture sensor

다국어 초록 (Multilingual Abstract)

Recently, portable handheld devices such as smart phones and tablets have become the hottest category in the electronics industry. With the recent advancements in distribution and usage of handheld devices, the range of services offered has broadened from the conventional mobile communication. With the increase in usage and complexity of handheld devices comes a need for a high user interface. The most commonly used user interface system for the execution of various complicated functions is the touchscreen. The touchscreen is the most accurate user interface system in use, presenting excellence both in simplicity and design. However, because of the versatility of handheld devices in its ability to be used in various environments, the touchscreen has shown some limitations in its use. For example, this system has constraints when wearing gloves or getting wet or dirt hands. To solve these problems, gesture sensors, which are similar in concept with the air mouse where no direct contact with the device is necessary, have been introduced.
Currently, there are three types of gesture sensors depending on its input source. First is the motion-based system which uses a gyroscope and an accelerator sensor. Although the motion-based system can recognize a variety of different gestures, it has the disadvantage where the user must always be holding on to the device. Next is the vision-based system which uses a camera. This system has the advantage of being able to recognize various motions as well as 3D gestures, but the camera must be on during the whole time it is active, thus having high power consumption. In order reduce active power during use of gesture sensors, proximity-based system was introduced Proximity-based systems use IR LED and IR receivers to detect simple gestures and reduces power consumption own to one-tenth that of vision-based systems. Conventional proximity-based systems have the disadvantage of requiring a high form factor since a set distance between the IR LED and IR receiver is needed in order to detect gestures.
In this thesis, in order to overcome the disadvantage of the high form factor found in conventional proximity-based gesture sensors, two IR receivers embedded on a single chip and an IR LED was used.
The goal of this thesis is to use the proposed algorithm to solve the problem associated with bringing the two IR receivers close to each other and to implement a gesture sensor capable of recognizing three different gestures of right/left/click from a distance of 10cm and above. The proposed gesture sensor was implemented on a FPGA board using Verilog HDL. Evaluation results show that the system is able to recognize many gestures with 95% accuracy in real time.

목차 (Table of Contents)

• Abstract i
• List of Figures vi
• List of Table vii
• 1. Introduction 1
• 1.1 Motivation 1
• 1.2 Organization 4
• 2. Fundamentals of gesture sensors 5
• 2.1 The basic principle of proximity-based gesture sensor 5
• 2.1.1. The basic principle of the IR sensor 5
• 2.1.2. The basic concept of gesture recognition 7
• 2.2 Gesture sensors with optical barriers 9
• 2.2.1. Time margin and gray zone 9
• 2.2.2. Gesture sensors with optical barrier 10
• 2.3 Conventional proximity-based gesture sensors 11
• 2.4 Motion Gesture Sensor using a single LED 12
• 3. Proximity sensing algorithm 15
• 3.1 Basic concept of proximity sensing algorithm 15
• 3.2 Proximity sensing algorithm 17
• 3.3 Tolerance in proximity algorithm 19
• 4. Gesture Recognition Algorithm 21
• 4.1 Maximum cross correlation 21
• 4.2 Velocity of object and time delay 23
• 4.3 Signal difference method 25
• 4.4 Click function 28
• 4.5 Proposed gesture classifier tree 29
• 5. Implementation of MGS processor 31
• 5.1 Architecture 31
• 5.2 I2C protocol 32
• 5.3 Low-Pass FIR filter 33
• 5.3.1. Moving Average Filter 33
• 5.3.2. Simulation results 35
• 5.4 The proximity detector 36
• 5.4.1. State machine 36
• 5.4.2. Simulation result 37
• 5.5 The motion detector 38
• 5.5.1. State machine 38
• 5.5.2. Simulation result 40
• 6. Experimental results 42
• 6.1 Parameter Settings 42
• 6.1.1. Sampling rate and time delay 43
• 6.1.2. tolerance in proximity algorithm 44
• 6.1.3. Threshold in proximity algorithm and filter taps 44
• 6.2 Experimental environment 46
• 6.3 Test results 48
• 6.3.1. User to device 48
• 6.3.2. Speed of gesture and system accuracy 50
• 7. Conclusion 52