IRJET- Virtual Fitness Trainer with Spontaneous Feedback using a Line of Motion Sensing Input Device Kinect Xbox 360

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 05 Issue: 10 | Oct 2018 www.irjet.net p-ISSN: 2395-0072
© 2018, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 1627
Virtual Fitness Trainer with Spontaneous Feedback using a line
of motion sensing input device Kinect Xbox 360
Mr. VRUSHABH JADHAV1, Mr. GAURAV JAIN2, Mr. ONKAR KHANDEKAR3,
Prof. Ms. SMITA BANSOD4
1Vrushabh Jadhav, Information technology Dept. of Shah and Anchor Kutchhi Eng. College, Mumbai, Maharashtra
2Gaurav Jain, Information technology Dept. of Shah and Anchor Kutchhi Eng. College, Mumbai, Maharashtra
3Onkar Khandekar, Information technology Dept. of Shah and Anchor Kutchhi Eng. College, Mumbai, Maharashtra
4 Prof. Smita Bansod, Information technology Dept. of Shah and Anchor Kutchhi Eng. College, Mumbai, Maharashtra
----------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - Exercise plays an important role inourdaytoday
life as it helps people remain in shape, fit and to prevent
from many disease. Regular physical activities such as
weight training and cardio exercises are part of everyday
modern life. If performed correctly, it contributes to the
health of a person. Exercises helps to prevent obesity and
stimulate the immunesystem.Manypeoplepracticephysical
exercises without an assistance of an expert in home. This
paper aims to present a software that offers virtual trainer
with real-time feedback and the assessment score to
different exercise postures presented by an animated 3D
character using Kinect sensor. This tool allows people to
observe the correct execution of each exercise. Recognition
of the exercise has been performed using Random Forest
(RF) classifier. The computer must first understand what a
user is doing before it can respond. This has always been an
active research field in computer vision, but it has proven
formidably difficult with video cameras. With help of Kinect
sensor, the computer directly sense the third dimension,
making the task much easier
Key Words: Kinect xbox 360, Depth cameras, Skeletal
Tracking, RMT, Random Forest Algorithm.
1. INTRODUCTION
1.1 Overview
The importance of exercising has been a common sense
among people for a long time, but how to make them
especially those white collars actively involved into those
tiring practices is a big problem. Our daily life is more
convenient than ever before because of the greatprogressof
science and technology. However, the body building or
fitness are always ignored in our dailylife.People needgoing
to fitness center or finding personal trainer to provide plans
and instructions of their own fitness. Nowadays, people are
becoming less active physically due to the advancement of
convenient technology [1]. Lack of adequate physical
exercise leads to unfitness and causes various illness. In
everyday routine, exercise playsanimportant roleasithelps
people remain in shape and fit.
1.2 Health Problems
Research findings have revealed that physical activity can
also boost self-esteem, mood, sleep quality and energy, as
well as reducing the risk of stress, depression, dementia and
Alzheimers disease.[2] In order to get fit, people need to go
fitness centers However, due to unavailability of adequate
fitness guides at affordable cost people tends to ignore
health
1.3 Available Fitness Products
Current fitness guide products include book, video, mobile
Apps etc., as shown in Figure a, most of which do not
provide real time fitness guide or even do not support any
interaction with users[1]. Typically, these products have
several drawbacks. First, the whole purposes or main
function of the products are too simple and dull, they only
serves to give advice or some tips on the exercises without
pertinence. Second, although some products aim at record
the time or miles of how user walks and gives some
values on how much calories has been consumed. Still, the
accuracy is a problem
1.4 Virtual Personal Trainer
In this project, we propose a virtual trainer with real-time
feedback and the assessment score through different
exercise postures using Kinect sensor. Recognition of the
exercise has been performed using Random Forest (RF)
classifier [1]. The authors have utilized 20 joints of the 3D
skeleton as features.
The Virtual Personal Trainer we build can provide real time
visually action guide and action assessment during the
fitness time of users using random forest algorithm
2. Proposed Methodology
Input data captures the motion of people using line motion
sensing input device (Kinect xbox 360). In line motion input
device, Input Data can be the color image, depth image,
skeleton data and audio data.[1] In this paper, we only use
skeleton data. Skeleton data is 3D skeleton of the human
posture that consists of twenty 3D points hasbeenextracted
using the sensor’s Application ProgrammingInterface(API).
3. Methodology and Implementation
The method proposes to use skeleton tracking feature of
Kinect sensor. Our system consists of Kinect sensor,
connected to a System via adapter that help to connect xbox

360 with Laptop or pc. To get data from the sensor, we use
C#, Kinect SDK and Visual studio software.
3.1 Tracking personal Skeleton
Purpose of this paper is recognition of action approach,
With help of depth maps captured by Kinect sensor and are
processed by a skeleton-tracking algorithm.[12][1] The
Kinect xbox 360 skeleton-tracking module help for
detecting the performing action andtrackinga setofjointsof
his/her body. The Kinect for Windows SDK provides uswith
a set of APIs that allow easy access to the skeletonjoints.The
SDK supports the tracking of up to 20 joint points. Each and
every joint position is identified by its name (head,
shoulders, elbows, wrists, arms, spine, hips, knees, ankles,
and so on), and the skeleton-tracking state is determined by
either Tracked, Not Tracked, or Position Only. The SDK uses
multiple channels to detect the skeleton.Thedefaultchannel
tracks all 20 skeletal joint positions with the Tracked, Not
Tracked, tracking mode. The followingdiagramrepresentsa
complete human skeleton facing the Kinect sensor, shaped
with 20 joint points that can be tracked by the Kinect[12][4]
3.2 Random Forest
Random forest algorithm can use both for classification and
the regression kind of problems. Randomforestalgorithmis
a supervised classification algorithm. As the name suggest,
this algorithm creates the forest with a number of trees. In
general, the more trees in the forest the more robust the
forest looks like. In the same way in the random forest
classifier, the higher the number of trees in the forest gives
the high accuracy results.
As shown in below figure from each decision tree based on
conditions value will be selected which will be thenpassed
to majority voting system where classification and
regression tasks will be performed to get accuracy result as
final class
Random forest combine simplicity of Decision tree with
flexibility resulting in vast improvement accuracy. While
creating a dataset we will consider 2 variable of each type
Specifying certain conditions and traversingthroughtree or
dataset checking condition at each and every step and
traverse the tree till last whererandomforestwill provideus
the predicted result.
3.3 Dataset Description
In this work, we will selected 5 basic exercises that should
ideally be performed by everyone in daily life. We will
enrolled 3 volunteers which are Fitness trainers for the
dataset preparation. [1][4]And also referred internet to
Know the steps involved in exercise for data set preparation
Rest of the participants are students volunteers we have

used data who will perform exercise so as to evaluate the
performance of the system. [1]
3.4 Project flow
Flow Chart
The Kinect sensor first takes input from its depth camera
and infrared camera. The exercise that a user performs in
front of Kinect sensor is considered as input. Then, the
system uses Random Forest classifier to identify the correct
exercise by mapping it with its Virtual trainer data set.
Exercises that user performsaremappedwithvirtual trainer
data using Classifier algorithm. If user is performing
exercises in wrong manner, it tells user and user can re-
perform exercise
3.5 Feedback Mechanism
This paper solution was developedtomake physical therapy
more engaging, efficient, and successful by using the Line
motion input device (Kinect Xbox 360) sensor and software
development kit (SDK), which helps patient to measure
patient progress by comparing it with Virtual trainer.
Patients can perform therapy at home. This can tell if
patients are doing exercises in proper manner and whether
patients perform exercises with accuracy[1]. Virtual trainer
provide to real-time feedback to patients for exercise .Our
helps motivate patients to do physical therapy and the data
set we gather demonstrate what form of therapy is most
effective, what types of patients react better to what type of
therapy, and how to best deliver that therapy. [4]
4. Experimental Results
We performed our project on Visual studio 2017, 64 bit. The
input line motion device ie. Kinect Xbox 360 used in this
project, is Microsoft Kinect, 30 FPS and16KHzusedforXbox
360 gaming console. The time required to process an image
is around 0.173 sec. We performed this project from a
distance 1.2 to 3 meters from the input line motion device.
The x-axis of the absolute maximum representstheposition
of the obstacle (left, right, middle), and the y-axis of the
absolute maximum represents the distance between the
object and user.
We have used Kinect Xbox 360 model to perform this
project ,in which it can only track two human bodies at a
times. This technology is helpful to detect20Jointsofhuman
body. Instead of Kinect 360, Kinect V2 can also be used
which can track six human bodies at a time .Kinect V2 is
based on time-of-flight technology. A time-of-flight camera
emits light signals and then measures howlongittakesthem
to return. With such measurements, the camera is able to
differentiate light reflecting from objects in a room and the
surrounding environment. That provides an accurate depth
estimation that enables the shape of those objects to be
computed.
5. Related Work
5.1. Sign Language Word Recognition using Kinect
Currently, it is said that there are about 360 million people
with hearing disability people in the world close this fileand
download the Microsoft Word, Letter file. Some hearing
disability people use sign language as a communicate way
with others Many hearingdisabilitypeopleusesignlanguage
as a communicate way with others. y. In order to solve these
problems, sign language recognition (SLR) aimed at
supporting the communicationbetweenthemareaddressed
[2].we employed two datasets, one is the 100 Japanese sign
language words dataset named J100wordsdataset,collected
by ourselves, and the other is a public available dataset
called ChaLearn dataset.

5.2 Blind Navigation System for VisuallyImpairedUsing
Microsoft Kinect Camera
Obstacle avoidance and navigation are major problems for
blind people. They require help to pass safely .In this work
an obstacle avoidance system for blind people using Kinect
depth camera. This assistive technology recognizes the
medium in front of the user using Kinect depth camera. The
system receives the depth images from the Kinect camera
and processes it using a windowing-based mean or average
method to recognize obstacles in the scanned environment.
When the system recognizes an obstacle, it sends a voice
feedback to the user through earphones. The testing is done
with blindfolded persons. It shows that this device could
successfully guide them to bypass obstacles safely
6. CONCLUSIONS
Our purpose of this paper is to provide smart gym in which
users can perform exercises under Virtual personal trainer
in anywhere. With help of this Paper, we are reducing
travelling time of users to travel to the gym to perform basic
routine exercises. This Paper is applicableto all kindofusers
i.e. beginners to experts. Here we are eliminating the
possibility of injuries during workout that may be caused
due to incorrect form exercise.
In this paper, we did not get the highest accuracy. The future
task is to improve the method to obtain high recognition
accuracy with help of Proposed Method. The number of
tracked bodies and joints in our proposed method is less.
This is not a sufficient number of tracked bodies and joints
assuming actual use. Increasing the number of tracked
bodies and joints is also a future tasks.
ACKNOWLEDGMENT
We would like to take this opportunity and express our
sincere gratitude towards Prof. Smita Bansod ma’am for her
guidance when required. We appreciate her valuable
suggestions and support. We are also very grateful to Prof.
Swati Nadkarni), Head of Information Technology
Engineering Department, Shah & anchor kutchhi
engineering college, Mumbai for her tremendous support
and guidance.
REFERENCES
[1] Pradeep Kumar, Raj Kumar Saini, Mahendra Yadava,
Partha Pratim Roy, Debi Prosad Dogra, Raman
Balasubramanian. Virtual Trainer with Real-Time Feedback
using Kinect Sensor, IEEE Sensors Journal, 2018.
[2]P. Kumar, H. Gauba, P. P. Roy, and D. P. Dogra. Coupled
hmm based multi-sensor data fusion for sign language
recognition. Pattern Recognition Letters, 2016.
[3] P. Kumar, H. Gauba, P. P. Roy, and D. P. Dogra. A
multimodal framework for sensor based sign language
recognition. Neurocomputing, 2017.
[4] P. Kumar, R. Saini, P. P. Roy, and D. P. Dogra. A position
and rotation invariant framework for sign language
recognition (slr) using kinect. Multimedia Tools and
Applications, pages 1–24, 2017. [19] S. Obdr ˇ zˇalek, G.
Kurillo, F. Ofli, R. Bajcsy, E. Seto, H. Jimison, and ´ M. Pavel.
Accuracy and robustness of kinect pose estimation in the
context of coaching of elderly population. In Engineering in
medicine and biology society (EMBC), 2017 annual
international conference of the IEEE, pages 1188–1193.
IEEE, 2017.
[5] K. Vamsikrishna, D. P. Dogra, and M. S. Desarkar.
Computer-vision assisted palm rehabilitation with
supervised learning. IEEE Transactions on Biomedical
Engineering, 63(5):991–1001, 2016.
[6] E. Velloso, A. Bulling, H. Gellersen, W. Ugulino, and H.
Fuks. Qualitative activity recognition of weight lifting
exercises. In Proceedings of the 4th Augmented Human
International Conference, pages 116–123. ACM, 2016.
[7] A. Verikas, A. Gelzinis, and M. Bacauskiene. Mining data
with random forests: A survey and results of new tests.
Pattern Recognition, 44(2):330–349, 2018.
[8] M. G. Wilson, G. M. Ellison, and N. T. Cable. Basic science
behind the cardiovascular benefits of exercise. British
journal of sports medicine, 50(2):93–99, 2016.
[9] M. Yadava, P. Kumar, R. Saini, P. P. Roy, and D. P. Dogra.
Analysis of EEG signals and its application to
neuromarketing. Multimedia Tools and Applications, 2017.
[10] Survey of Motion Tracking Methods Based on Inertial
Sensors: A Focus on Upper Limb Human Motion Alessandro
Filippeschi 1,*, Norbert Schmitz 2, Markus Miezal 3,Gabriele
Bleser 2,3, Emanuele Ruffaldi 1 and Didier Stricker 2
[11]Microsoft research/Teaching Kinect for Windows to
Read Your Hands/direction in the evolution of Kinect for
Windows.TechFest 2013
[12]http://www.rroij.com/open-access/survey-on-
skeleton-gesture-recognitionprovided-by-
kinect.php?aid=42694.

IRJET- Virtual Fitness Trainer with Spontaneous Feedback using a Line of Motion Sensing Input Device Kinect Xbox 360

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