User:Harsh patel6595/sandbox

= Product Design: SmartCane for Blind = This is an article for the project that was made at IIT Madras during a course.

Problem Statement and Concept
Design and develop an electronic travel aid that fits on the top fold of the white cane. It will serve as an enhancement to the white cane in detecting obstacles that are hanging and above the knee level. It will have a module that will assist the user to read the sign boards that he encounters.

Motivation
A white cane is one of the most common mobility aids for the visually impaired. However, it does not help users with visual impairments find obstacles at head- or knee-level, or at distances greater than 1 m. To overcome these difficulties, smart canes with vibration alerts and an extended obstacle detection range have been introduced. However, several usability problems mean that users with visual impairments rarely adopt a smart cane. The goal of this project was to understand the potential for using a smart cane, along with developing design guidelines for improved smart canes.

Performance Parameters
1.    Detection Range

2.    Vibration Intensity

3.    Audio output Intensity

4.    Image quality from the camera

Target Customers and Market
India is home to world’s largest number of blind people. Of the 37 million people across the globe who are blind, over 15 million are from India.

India needs at least 2.5 lakhs donated eyes every year but manages to collect only 25000 out of which 30% cannot be used. As most of the people in India affected by blindness cannot afford surgeries and expensive assistive devices, there is a need in market for a cheap and reliable assistive device.

Human Factor Analysis for selecting Handle Type
Using the specification mentioned in the fig:'Free Body Diagram of a Cane on ground with normal force' we can find 𝑅ℎ and 𝜏ℎ.

Considering Normal force (N) provided by the ground is 10% of the device weight, coefficent of friction between cane and ground is 0.4 and α = 45

Rh = 0.136N – (0.136*0.1)N

Rh = 𝟎.𝟏𝟐𝟐𝟒𝐍;

τh = (0.0136∗0.4)∗1∗Sin(45)+(0.136∗1∗cos(45))/2−0.0136∗1∗cos(45)

τh = 𝟎.𝟎𝟒𝟐𝟑𝟏𝟑𝑵𝒎;

 Configuration 1:  Considering d = 7cm Fthumb can be calculated and hence Fff can be calculated as τh is provided by fingers

τh = Fff∗d

0.042313=𝐹𝑓𝑓∗0.7

𝑭𝒇𝒇=𝟎.𝟎𝟔𝟎𝟒𝟒𝟕𝑵

 Configuration 2:  Taking average distance between forearm (center) and finger as d (18 cm) and considering μh to be 0.7

τh = Fholdingforce∗d

𝜏ℎ = 𝜇ℎ∗𝐹𝑓𝑓∗𝑑

0.042313 = 0.7∗𝐹𝑓𝑓∗1.8

𝑭𝒇𝒇=𝟎.𝟎𝟑𝟑𝟓𝟖𝑵

Therefore from the above comparison Finger force (𝐹𝑓𝑓) required in Configuration 2 is less (hence less fatigue) which is almost half of the first hence we choose the second configuration.

Specifications

 * 1) Ultrasound sensor: HC-SR04 sensor: Detection range of up to 4.5 meters, High precision of 0.3 cm, Sensor angle of 35.6 degrees.
 * 2) Image Processing Module: HD ready camera for high resolution image capture coupled with Pytesser package.
 * 3) Controller: Raspberry Pi 3 with Audio jack for audio output
 * 4) Vibration Module: A small-sized motor with eccentric mass on the shaft that can be controlled by the controller.
 * 5) Audio Module: Using espeak Text-to-Speech library to convert the text read from the sign board into audio signal which can be heard through the earphones that can be attached to audio jack on the Raspberry Pi 3.

Circuit Diagram of Vibration Module
We have used
 * 1) relay : JZC-11F-05VDC – 1Z
 * 2) motor : 3v dc motor

Circuit Diagram of Ultrasound Module
We have used Ultrasound sensor: HC -SR04