Post on 21-Apr-2022
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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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