Body motion detection and tracking using a Kinect sensor: Review

2017

The objective of this dissertation research is to use Kinect sensor, a motion sensing input device, to develop an integrated software system that can be used for tracking non-compliant activity postures of consented health-care workers for assisting the workers’ compliance to best practices, allowing individualized gestures for privacy-aware user registration, movement recognition using rule-based algorithm, real-time feedback, and exercises data collection. The research work also includes developing a graphical user interface and data visualization program for illustrating statistical information for administrator, as well as utilizing cloud based database system used for

International Journal of Pattern Recognition and Artificial Intelligence

Microsoft Kinect, a low-cost motion sensing device, enables users to interact with computers or game consoles naturally through gestures and spoken commands without any other peripheral equipment. As such, it has commanded intense interests in research and development on the Kinect technology. In this article, we present a comprehensive survey on Kinect applications, and the latest research and development on motion recognition using data captured by the Kinect sensor. On the applications front, we review the applications of the Kinect technology in a variety of areas, including healthcare, education and performing arts, robotics, sign language recognition, retail services, workplace safety training, as well as 3D reconstructions. On the technology front, we provide an overview of the main features of both versions of the Kinect sensor together with the depth sensing technologies used, and review literatures on human motion recognition techniques used in Kinect applications. We provide a classification of motion recognition techniques to highlight the different approaches used in human motion recognition. Furthermore, we compile a list of publicly available Kinect datasets. These datasets are valuable resources for researchers to investigate better methods for human motion recognition and lower-level computer vision tasks such as segmentation, object detection, and human pose estimation.

International Journal of Sports Science, 2015

The purpose of this study was to provide evidence of reliability and validity for the use of a Microsoft Kinect system to measure displacement in human movement analysis. Three dimensional (3D) video motion systems are commonly used to analyze human movement kinematics of body joints and segments for many diverse applications related to gait analysis, rehabilitation, sports performance, medical robotics, and biofeedback. These systems, however, have certain drawbacks pertaining to the use of markers, calibration time, number of cameras, and high cost. Microsoft Kinect systems create 3D images and are low cost, portable, not markers required, and easy to set up. They lack, however, evidence of reliability and validity for human movement kinematics analysis. Twenty-six participants were recruited for this study. Peak Motus version 9 and Microsoft Kinect system with customized skeleton software were used to collect data from each subject sitting on a platform moving horizontally at the...

Indonesian Journal of Electrical Engineering and Computer Science

Due to the low cost and wide availability of the Kinect sensor, researchers and experts in the field of anthropometry, sizing and clothing fiting are leveraging on its inbuilt 3D camera to develop systems for automated body measurement. This study focuses on the evaluation of the Microsoft Kinect (V1) sensor to determine its suitability for automated body measurement. The study was conducted by data collection of various body dimensions of test subjects using a measuring tape as a reference. Furthermore, a statistical approach known as the measurement system analysis was used to investigate the sensor's capability to produce accurate, reliable and consistent body measurements. The results obtained shows indicates that there exists very little variation when the measurement is repeated. Also, the instrument is relatively stable, with minimal bias which can be corrected by calibration. The outcome of the study proves the effectiveness of the Microsoft Kinect sensor as a means of c...

In this work, we attempt to tackle the problem of skeletal tracking of a human body using the Microsoft Kinect sensor. We use cues from the RGB and depth streams from the sensor to fit a stick skeleton model to the human upper body. A variety of Computer Vision techniques are used with a bottom up approach to estimate the candidate head and upper body postitions using haar-cascade detectorsa and hand positions using skin segmentation data. The data is finally integrated with the Extended Distance Transform skeletonisation algorithm to obtain a fairly accurate estimate of the the skeleton parameters. The results presented show that this method can be extended to perform in real time.

In this paper, we have proposed a system to keep track of human body movements in real time mode. The Kinect sensors are used to capture Depth and Audio streams. The system is designed by integration of two modules namely Kinect Module and Augmented Reality module. The kinect module performs Voice Recognition and captures depth images that are used by Augmented Reality module for computing the distance parameters. Augmented Reality module also captures real-time image data streams from high resolution camera. The system generates 3D module that is superimposed on real time data.

Iraqi Journal for Electrical and Electronic Engineering, 2021

In this paper, a new method is proposed for people tracking using the human skeleton provided by the Kinect sensor, Our method is based on skeleton data, which includes the coordinate value of each joint in the human body. For data classification, the Support Vector Machine (SVM) and Random Forest techniques are used. To achieve this goal, 14 classes of movements are defined, using the Kinect Sensor to extract data containing 46 features and then using them to train the classification models. The system was tested on 12 subjects, each of whom performed 14 movements in each experiment. Experiment results show that the best average accuracy is 90.2 % for the SVM model and 99 % for the Random forest model. From the experiments, we concluded that the best distance between the Kinect sensor and the human body is one meter.

2015

Human posture recognition is gaining increasing attention in the field of computer vision as well as image processing, due to its promising applications in the areas of personal health care, environmental awareness, human-computer-interaction and surveillance systems. Human posture recognition in video sequences is a highly challenging task which is part of the more general problem of video sequence interpretation. In this paper a non-contact view-based approach for measurement of velocity and acceleration of human movement for analysis of body dynamics using kinect camera is introduced. In the first step, twenty body-joint points of skeletal structure of the human body are extracted. Then, Array of joint points is stored. Finally Using obtained Array of joint points calculates the velocity.

IOSR Journal of Computer Engineering, 2017

In this work we implemented a system to obtain human body parameter measurement without physically contacting the user. This implementation is contained the methods of obtaining 3D measurements using Kinect v2 depth sensor. The developed system at the initial stage is capable of detecting and obtaining personalized body parameters such as height, shoulder length, neck to hip length, hip to leg length and arm length by incorporating the necessary skeleton joints and front perimeter at chest, stomach and waist by incorporating the necessary 3D pixels. According to the results, the measurement on height and arm length of the person are relatively in good agreement with the actual values since the error is less than 5% and measurement has been taken in centimeters. Maximum 12% of an error incorporated of calculating front perimeter at chest. Experimental results obtained from the developed system are in acceptable range for dressing purpose and ultimately helpful for designing a real time 3D virtual dressing room.

2013 IEEE 4th International Conference on Cognitive Infocommunications (CogInfoCom), 2013

Non-verbal communications such as kinesthetics, or body language and posture are important codes used to establish and maintain interpersonal relationships. They can also be utilized for safe and efficient human robot interactions. A correct interpretation of the human activity through the analysis of certain spatio-temporal and dynamic parameters represent an outstanding benefit for the quality of human machine communication in general. This paper presents an effective markerless motion capture system provided by a mobile robot for sensing human activity, in non-invasive fashion. We present a physical model based method exploiting the embedded Kinect. Its performances are evaluated first comparing the results to those obtained with a precise 3D motion capture marker based system and to data obtained from a dynamic posturography platform. Then an experiment in real life conditions is performed to assess the system sensitivity to some gait disturbances.