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 Table of Contents  
ORIGINAL ARTICLE
Year : 2022  |  Volume : 17  |  Issue : 1  |  Page : 30-33

A research protocol of an observational study on efficacy of microsoft kinect azure in evaluation of static posture in normal healthy population


Department of Community Health Physiotherapy, Ravi Nair Physiotherapy College, Datta Meghe Institute of Medical Sciences, Wardha, Maharashtra, India

Date of Submission23-Apr-2021
Date of Decision18-Sep-2021
Date of Acceptance15-Jan-2022
Date of Web Publication25-Jul-2022

Correspondence Address:
Dr. Waqar Naqvi
Department of Community Health Physiotherapy, Ravi Nair Physiotherapy College, Datta Meghe Institute of Medical Sciences, Sawangi (Meghe), Wardha, Maharashtra
India
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/jdmimsu.jdmimsu_176_21

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  Abstract 


Background: Recognition of human pose is very significant in studies involving human-computer interactions (HCIs). Microsoft's Kinect II or Microsoft's Kinect azure sensor in three-dimensional motion capturing systems shows a growing interest in vision-based HCI as they are low-cost. In this research, we introduced and ruled out the efficacy of Microsoft Kinect Azure in the evaluation of static coronal Posture in normal healthy population. Methodology: The research has been structured as an observational study. The total of 132 participants will be taken from AVBRH, Sawangi Meghe for study as per inclusion and exclusion criteria. With intervention the period of the study will be 6 months. It holds single period, concurrent validity evaluation comparing normal posture derived from the Kinect system. Discussion: This study protocol aims to evaluate the validity of the evaluation of normal human posture using Microsoft Kinect Azure. The study's expected outcome will concert on the evaluation of coronal Posture using Microsoft Kinect Azure in normal healthy population.

Keywords: Coronal posture, healthy individuals, human posture evaluation, Kinect Azure, projector


How to cite this article:
Nurai T, Naqvi W. A research protocol of an observational study on efficacy of microsoft kinect azure in evaluation of static posture in normal healthy population. J Datta Meghe Inst Med Sci Univ 2022;17:30-3

How to cite this URL:
Nurai T, Naqvi W. A research protocol of an observational study on efficacy of microsoft kinect azure in evaluation of static posture in normal healthy population. J Datta Meghe Inst Med Sci Univ [serial online] 2022 [cited 2022 Aug 16];17:30-3. Available from: http://www.journaldmims.com/text.asp?2022/17/1/30/352215




  Introduction Top


From a cybernetic perspective, our body system could be described as a network of structure and function-based subsystems with motor equiform within the principle of equilibrium, energy economy, and contentment: Thus, the optimal posture is something that provides full motor movement performance, and in the absence of stress with maximal energy economy. The current research work is focused on the requirement of defining a real objective method of evaluating the postural variables, affordable, and of easy use.[1]

Human-computer interaction (HCI) based on vision does not involve direct physical contact between users and apps. In this area, conventional approaches were generally based on regular sensors such as the RGB camera, which are not only computationally expensive but also easily affected by variations in brightness and background clutters.[2] Low-cost sensors such as Microsoft's Kinect II or Microsoft's Kinect azure sensor in three-dimensional (3D) motion capturing systems showing a growing interest in vision-based HCI as an alternative to more expensive devices[2],[3] [Figure 1].
Figure 1: Microsoft Azure

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Microsoft Kinect®, a gameplay device associated with Xbox console, has been the tool used throughout this analysis. Designed by Microsoft within field of sport, through the Motion Capture Program, the kinect sensor® has fascinated millions of customers for years, capturing movements through cameras and instantaneous or delayed replay. The Kinect® seems to be an RGB lens, fitted through an infrared feature that captures 3D pictures.[1]

Obtaining 3D body joint details is important for understanding the position of the human body however there seem to be several possible applications under which motion capture (MOCAP) systems can be used effectively. Two general methods being: marker-based methods where active (light-emitting) or passive (reflective) markers could be used, and marker-less approach. Few camera based professional motion-capture systems, like the vicon or even qualisys, have passive markers evident in infrared (IR) lenses, whereas marker-less processes do not need additional equipment in addition to cameras. For example, the CMU1's OpenPose algorithm analyses two-dimensional clips for joint approximation, but the Azure Kinect is using RGB and IR lenses to construct a 3D value of the image.

Recently, work on the recognition of human activity has been documented on systems showing strong overall success in recognition. Azure Machine can be used to construct up models of machine learning algorithms. Recognition output is based on: An activity set, data collection quality, feature extraction process, and learning algorithm.[4] The machine will calculate the distance of objects within the environment. The data from the sensor can be used in software applications using a software development kit. The sensor provides information on the location of the recognized user joints in the frame in addition to the depth and color details.[3]

Advancement has been achieved this far in introducing new frameworks for understanding human posture through Kinect. Suma et al. suggested a versatile action with articulated skeletal toolkit called flexible action and articulated skeleton toolkit (FAAST), that comprises 27 predetermined human roles, such as lean left, left arm up, left foot up, and so on. When acquiring device skeleton data, the FAAST toolbox can execute virtual events such as mouse cursor control and keypad inputs, and can thus be used to control virtual reality apps. Kang and some colleagues proposed a new way of controlling 3D application by collecting user commands through remote information, and also user position data through joints. Furthermore, Thanh et al. designed a system where robot can study human postures relying on the Semaphore method. Command terms were sent out letter by letter utilizing sign language throughout their approach. The area of physical posture recognition has been revamped by modern algorithms utilizing Kinect. While Kinect's depth images consists complete three-dimensional posture detail, body recognition takes a lot of effort to extract away unnecessary items that is not even component of human body. With implementation of NITE and also the FAAST, designed by Suma et al., depending on articulated skeleton sites, some simple pre-specified human actions such as swipe, loop, jump, and hop may be recognized. These works build a solid basis for posture recognition research that usees data from Kinect skeletons. Such aforementioned methods, with Kinect's support, have vastly improved existing human posture image recognition. However, certain drawbacks hamper their success in general circumstances in terms of productivity and applicability. First, all of these strategies have quite constricted lexicon posture, and could only handle basic postures that are far from sufficient in advanced tasks. Second, such methods are mainly based on computer programmer-defined postures, which doubtlessly lessen an HCI system's versatility and applicability. Moreover, user-defined parameters are needed in these processes that may be subjective and cumbersome in real-world applications. To tackle these questions, we suggest a newer method of recognizing human posture through adopting machine learning technique. Without empirical criteria this technique can instantly recognize a certain user-defined pose often with superior efficiency.[1]

Human posture recognition can also be seen as a sort of sub-field for recognition of motion as a pose is a “static motion.” The Kinect system features an infrared imaging scanner coupled via a monochrome CMOS sensor collecting information from images. This machine has an RGB lens, and even a microphone with several arrays. The Kinect system therefore gives us the opportunity to simultaneously record the image of the color and the description of the observable scene in detail. A skeleton monitoring feature was recently implemented in the latest version of Kinect SDK. This method is intended to store the joints as points compared to the apparatus itself. The knowledge of conjoints is contained in images. The locations of the various points are determined and extracted to each frame[5] [Figure 2].
Figure 2: Joints point

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We have three key details for each Joint. A joint has a certain discrete value for index. The first info is the Joints index. The 2nd knowledge is where each joint will be located in coordinates x, y, and z. Those 3 coordinates are measured in meters. The Depth Sensor's body axis are the axes x, y, and z. It is a right-hand coordination device that positions the array of sensors at the beginning where the positive z axis extends in the way the series of sensors points in. A positive y dimension moves upward, and thus the positive x dimension falls to the left (for the sensor array)[5] [Figure 3].
Figure 3: Three coordinates for joint position

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Aim and objective

This research aims to evaluate the efficacy of Microsoft Kinect Azure in the evaluation of static Posture in normal healthy population.

Objective

The objective is to evaluate standing frontal posture in normal individuals by Microsoft Kinect Azure.


  Methodology Top


This study will be conducted in the Department of Community Health Physiotherapy at Ravi Nair Physiotherapy College, Sawangi (Meghe), Wardha, India, with the approval of Datta Meghe Institute of Medical Sciences, Institutional Ethics Committee, Deemed to be University.

Material required

  • Consent form
  • Projector
  • Microsoft Kinect azure.


Procedure

Obtain informed consent, approach participants, and screen for eligibility (N = 132)



Baseline attention bias assessment (N = 132)



Orientation to application of Kinect Azure for posture evaluation



Administration and assessment of posture by Kinect Azure



Statistical analysis

We will collect data of 132 participants (both male and female). They will be instructed to stand normally in such a way that frontal plane of posture comes under sensor coverage. The sensor will record and interpret the data with the markings of normal posture landmarks. The Kinect sensor is mounted at each experiment to captures the subject image. The distances are compatible with the recommendation for achieving the highest data quality.

Furthermore, the age, weight, height, sex, and profession of the individuals (e.g., undergraduate, businessman, dietician, or software developer) will be recorded as additional information for better annotation of the collected data. These data are useful in better understanding the results that need to be derived and in drawing conclusions from the analysis.

Outcome measures

  • Normal posture landmarks.


Inclusion criteria

  • Normal healthy individuals with age between 7 and 65 years
  • Individuals without preexisting physical deformity
  • Both male and female
  • Subjects willing to participate.


Exclusion criteria

  • Subject with musculoskeletal disorder/deformity
  • Neurological problem
  • Subject with abnormal posture.


Study design

Observational type.

Study setting

Ravi Nair Physiotherapy College, Sawangi (M).

Sample size

One hundred and thirty-two participants.

Study duration

Six months.

Sampling technique

simple random.


  Expected Results Top


The study's expected outcome will concert on the evaluation of normal posture in a healthy individual. After accomplishment of the study, result will be calculated by systemic data analysis by randomized control trial.


  Discussion Top


This study protocol aims to evaluate the validity of evaluation of Normal human posture using Microsoft Kinect Azure. The study's expected outcome will concert on the evaluation of coronal Posture using Microsoft Kinect Azure in normal healthy population. This research will help to decide the how efficiently Kinect is used in evaluating frontal human posture in the age group of 7–65 years.

Ethics and dissemination

The approval of the committee on institutional ethics must be obtained prior to the start of the study. Patients must be treated with respect first. Upon meeting the requirements of inclusion and exclusion criteria, the patients are taken for review.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
  References Top

1.
Jeong J, Wang Y, Shah M. A Low-Cost Motion Capture System using Synchronized Azure Kinect Systems.1.  Back to cited text no. 1
    
2.
Zhang Z, Liu Y, Li A, Wang M. A novel method for user-defined human posture recognition using Kinect. In: 2014 7th International Congress on Image and Signal Processing. 2014. p. 736-40.  Back to cited text no. 2
    
3.
Diego-Mas JA, Alcaide-Marzal J. Using Kinect™ sensor in observational methods for assessing postures at work. Appl Ergon 2014;45:976-85.  Back to cited text no. 3
    
4.
Čurić M, Kevrić J. Posture activity prediction using Microsoft Azure. In: Hadžikadić M, Avdaković S, editors. Advanced Technologies, Systems, and Applications II. Cham: Springer International Publishing; 2018. p. 299-306.  Back to cited text no. 4
    
5.
Le TL, Nguyen MQ, Nguyen TT. Human posture recognition using human skeleton provided by Kinect. In: 2013 International Conference on Computing, Management and Telecommunications (ComManTel). 2013. p. 340-5.  Back to cited text no. 5
    


    Figures

  [Figure 1], [Figure 2], [Figure 3]



 

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Abstract
Introduction
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