Smart BraceletWith handshake recognition for information exchange

International Conference Engineering Technologies and Computer Science: Innovation & Application (EnT-2021), August 18-19, Moscow, Russia

Kayalvizhi JayavelAssistant Professor in Department

of Information Technology,SRM Institute of Science and

Technology, India

Didacienne MukanyiligiraHead of Masters studies, African Center of Excellence in Internet of Things (ACEIoT), Kigali, Rwanda

Oluwatobi Oyinlola Graduate Student, African Center of

Excellence in Internet of Things (ACEIoT), Kigali, Rwanda

The Problem We Solve

The current situation

Nowadays, the handshake gesture is

common around the world when meeting

and greeting people, maybe you just met,

or you have seen each other for a long

time.

Explain

Exchange of business card is the order of

the day all over the world, but then after

the event we all go back to our social

media to begin to look up the person and

Secure transmission of periodic date and

alerts

Solution

A smart bracelet that should be able to

detect a handshake gesture which will be

used to exchange contact details in other

connect digitally

Introduction

The ancient Greeks introduced handshake over 2,500 years ago

as a symbol of peace.

Background and Motivation

The gesture patterns of two people, who shake hands of each other, will be matched

by using an algorithm

Problem Statement

There are inconveniences in sharing and storing them

for future usage due to various reasons such as time constraints, human

errors and large crowd size

Goals

Design a bracelet using SoC with BLE functionalities

Develop an algorithm to detect, match hand gesture

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Methodology (cont’d)

Hardware design

•Circuit, PCB and

battery analysis

Firmware

•Programmed with C

•Used nRF52832

•Gyroscope and

Accelerometer

Algorithm

•Pattern recognition

• Handshake

detection and

matching algorithm

Analysis

•Data gathered are

analyzed

Research Objectives

Research Objectives

Objective #1

Design and build a hand gesture detection system

Objective #2

Develop an algorithm to detect, match and distinguish

hand gestures

Objective #3

Design and build a flexible wearable bracelet with

System-on-chip (SoC)

Objective #4

Develop a mobile App for contact management

Hypothesis

Hypothesis#1

An accelerometer will be used for motion

detection of hands in terms of orientation,

intensity and frequency.

01

Hypothesis#2

Handshake detection will be about 94%

accurate with vector feature than the existing

solutions.

02

Hypothesis#3

The detection will be executed based on the

intensity, frequency, and orientation of the

pattern recognition.

03

Hypothesis#4

The handshake matching computation can be

performed base on the analysis of the

intensity, orientation, and frequency of the

handshake gesture

04

Handshake recognition Algorithm

Extraction analysis

The analysis of data is based on distinguishing all the handshakes from the users which were done using window function signal processing to study all the trained data. MATLAB was used to compute all the three handshake features

The gesture is represented with dots x, y and z-axes. The box shows the area when the handshake was satisfied

Interpretation of Accelerometer Sensor Data

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85

58

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13

61

63

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17

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42

71

29

83

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35

23

79

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64

33

46

04

87

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85

95

62

26

49

67

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03

73

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57

78

48

11

83

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65

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29

19

94

69

73

10

00

X Y Z

High intensity

Low intensity

Captured Data

Investigate “handshake matching”

The four handshake samples

Investigate “handshake matching”

Algorithm #1

We introduce a mirror effect which happens when two people face each

other

Result #1

When analyzing the data it shows that

there is no real differenced for

mismatching and matching of the

bracelet. The is no significant use of

the move of x-axis because it moves

back and fort

Algorithm #2

Based on the previous observation,

algorithm 1 can be modified to reduce

the noise by removing the x-axis.

Result #2

We noticed that the approach does

not consider that the two axes could

give specific information when

combined

Hardware Design

Circuit Design

PCB Design

Battery life

Final hardware design

Mobile Application

An algorithm was formed and implemented; it can pick the best bracelet among all the broadcasted data. Using the cross correlation, we

evaluated a new algorithm.

For the handshake detection, a new tool was developed mainly to give a detailed computation of the frequency, intensity and orientation of the

gesture. And we have predictively received an accuracy of 97%.

Conclusions

Suggestions

Suggestion #1

Add Wakanda greeting to the bracelet, which requires another

new algorithm (Already included it)

Suggestion #2

Voting

Suggestion #3

Add nFC for payment processing

References

References

Joseph Vedhagiri, G.P., Wang, X.Z.,., 2020. Comparative

Study of Machine Learning Algorithms to Classify Hand

Gestures from Deployable and Breathable Kirigami-Based

Electrical Impedance Bracelet., 4(3), p.47.

Sagayam, K.M. and Hemanth, D.J., 2018. ABC algorithm

based optimization of 1-D hidden Markov model for

hand gesture recognition applications. 99, pp.313-323.

J. Wu, G. Pan, D. Zhang, G. Qi, and S. Li, “Gesture

recognition with a 3-d accelerometer,” in Ubiquitous

intelligence and computing. Springer, 2009, pp. 25–38.

De Smedt, Q., Wannous, H. and Vandeborre, J.P., 2016.

Skeleton-based dynamic hand gesture recognition. In

Proceedings of the IEEE Conference on Computer Vision

and Pattern Recognition Workshops (pp. 1-9).

Wu, X.Y., 2019. A hand gesture recognition algorithm

based on DC-CNN. Multimedia Tools and Applications,

pp.1-13.

Kim, Y. and Toomajian, B., 2016. Hand gesture recognition

using micro-Doppler signatures with convolutional neural

network. IEEE Access, 4, pp.7125-7130.

Geer, D., 2004. Will gesture recognition technology point

the way?. Computer, 37(10), pp.20-23.

D. Biswas, D. Corda, G. Baldus, Et al “Recognition of

elementary arm movements using orientation of a tri-axial

accelerometer located near the wrist,” Physiological

measurement, vol. 35, no. 9, p. 1751, 2014

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QUESTIONS