ECE 110/120 Honors Lab Section : Smart Glove

Team members:

Andre He, ECE 110 NetID: songtao2

Jingwei Zhuo, ECE 110 NetID: jingwei2

Luting Chen, ECE 110 NetID: lchen108

Introduction

Statement of Proposal

We would like to introduce a different type of motion pattern lock, which would be a glove lock that could identify certain gestures as the correct password to unlock a system.

Background Research

Most locks for today require users either to keep a physical key with them or to memorize a serial of numbers/letters or a certain shape pattern. On the one hand, surveys have shown that in every five people, there are two whose passwords have been compromised in the past year. On the other hand, there are also researches showing that through subsequent practice using muscle memory, people will be able to reproduce movement patterns effortlessly and "subconsciously". Since the risk of using both physical keys and electronic passwords are fairly high, it would be reasonable to introduce a new kind of lock that only requires a much simpler and more basic memorization for users ---- muscle memory for basic single hand movements. 

Design Detail

Overview

Our project contains two main component: the glove part and the microcontroller part.

The smart-glove attaches a PCB board connecting multiple flex resistors between the upper layers of the glove. When the wearer curls each finger, the corresponding flex resistor changes its resistance and so does the current. So far, we would like to allow five movements of fingers to transmit five different signals, which are automatically sent from glove to Bluetooth module of microcontroller.

Microcontroller opens the lock only if the gestures (converted to signals) are in the right order. Logic gates and functions will be implemented to build the circuit with an Arduino/Raspberry board.

Block Diagram / Flow Chart

     

Materials

I.hardware

1) Flex Resistors

2) Bluetooth module

3) LEDs

4) Glove (pre-made/row material)

5) Raspberry Pi

II.Software

1) Quartus

2) Eagle

3) Reality Editor

4) HRQR

Possible Challenges

1) We need to design our PCB board, which is a signal converter and thus may need Bipolar Junction Transistor to construct amplifier circuit.

2) We need to learn about the theory between current and signal conversion.

3) We lack some basic programming experience about writing down algorithms and enable the microcontroller to read the codes to open the lock.

4) Extension Module: if our project is successfully finalized, we could use Reality Editor to monitor and remotely control the system(via Virtual Reality).

References

"Password Statistics: The Bad, the Worse and the Ugly (Infographic)", 2015.[Online] https://www.entrepreneur.com/article/246902  [26-Sep-2016].

Lee, D.T., & Schmidt, A.R. (2005). Motor Control and Learning: A Behavioural Emphasis. (4th ed). Windsor, ON: Human Kinetics

Weekly Update Log 1

Measured and recorded the resistance values of each flex resistor. However, there was a serious mistake that we connected flex sensor directly with the power supply and only measured the resistance and current values. We plan to finish all the measurements next period. 

Weekly Update Log 2

Continued measurements and plotted a MATLAB graph showing the IV characteristic of each flex resistor.

Flex resistor used: 30, 39, 27, 21, 4, 7.

We use degrees like 0, 0-45, 45-90 as a range of resistance values triggering commands from the smart glove. Users can pose gestures accordingly within the tolerance scope.

Weekly Update Log 3

There was a serious mistake that we connected flex sensor directly with the power supply and only measured the resistance and current values. Arduino can only measure voltage drop and we reconnected the circuit, making the flex resistor in series with a resistor. Andre used MATLAB to plot a IV characteristic diagram for different flex sensors.