PERBAIKAN INTERPOLASI GERAKKAN MODEL SKELETON 3D DARI DATASET HASIL KAMERA KINECT

AbstractThe Kinect camera's dataset can be used for training and testing of human movement recognition using a deep learning approach, in addition to tracking and estimating the position and position of the human body. Improvement of human movement which is covered by other body parts is still a challenge. The research objective: to design a repair application to move the 3D skeleton model from the Kinect camera dataset using the 3D interpolation histogram smoothing approach on the human body. The study was in the form of a simulation of a skeleton model movement improvement in the context of developing a key-frame based animation application through Kinect camera recording. The prototype development method goes through the cycle stages of analysis, design, implementation. The initial analysis stage selects the kinect camera dataset by examining the file format and data structure. Furthermore, the development for data improvement through the 3D interpolation refinement approach. Movement improvement in terms of measurement of interpolated resultant errors from each time t0, t1, t2 and t3 histogram using the RMSE method and visual observation. showed significant results close to normal movement.Keywords : dataset, kinect camera, interpolation, skeleton modelDataset hasil kamera Kinect dapat digunakan untuk pelatihan dan pengujian pengenalan gerakkan manusia dengan pendekatan deep learning, selain tracking dan estimasi letak dan posisi tubuh manusia. Perbaikan gerakkan manusia yang tertutup bagian tubuh lainnya masih menjadi tantangan. Tujuan penelitian: membuat rancang bangun aplikasi perbaikan gerakkan model skeleton 3D dari dataset hasil kamera Kinect menggunakan pendekatan penghalusan histogram interpolasi 3D pada bagian tubuh manusia. Kajian berupa simulasi perbaikan gerakkan model skeleton dalam rangka pengembangan aplikasi animasi berbasis key-frame melalui perekaman kamera Kinect. Metode pengembangan prototitpe melalui tahapan siklus analisis, disain, implementasi. Tahap analisis awal memilih dataset hasil kamera kinect dengan mengkaji format file dan struktur data. Kemudian mengekstraksi beberapa kategori gerakkan kedalam kedalam format file untuk eksperimen. Melalui disain awal pengembangan program dilakukan penyesuaian skala angka untuk menghasilkan histogram yang dapat memperlihatkan bagian kesalahan gerakkan secara signikan menggunakan Root Mean Squared Error (RMSE). Selanjutnya pengembangan untuk perbaikan data melalui pendekatan penghalusan interpolasi 3D. Perbaikan gerakkan ditinjau dari pengukuran kesalahan resultante interpolasi dari setiap watu t0, t1, t2 dan t3 histogram dengan metode RMSE dan pengamatan secara visual. menunjukkan hasil cukup signifikan mendekati gerakkan yang normal.Kata Kunci : dataset, kamera kinect, interpolasi, model skeleto

PERBAIKAN INTERPOLASI GERAKKAN MODEL SKELETON 3D DARI DATASET HASIL KAMERA KINECT

AbstractThe Kinect camera's dataset can be used for training and testing of human movement recognition using a deep learning approach, in addition to tracking and estimating the position and position of the human body. Improvement of human movement which is covered by other body parts is still a challenge. The research objective: to design a repair application to move the 3D skeleton model from the Kinect camera dataset using the 3D interpolation histogram smoothing approach on the human body. The study was in the form of a simulation of a skeleton model movement improvement in the context of developing a key-frame based animation application through Kinect camera recording. The prototype development method goes through the cycle stages of analysis, design, implementation. The initial analysis stage selects the kinect camera dataset by examining the file format and data structure. Furthermore, the development for data improvement through the 3D interpolation refinement approach. Movement improvement in terms of measurement of interpolated resultant errors from each time t0, t1, t2 and t3 histogram using the RMSE method and visual observation. showed significant results close to normal movement.Keywords : dataset, kinect camera, interpolation, skeleton modelDataset hasil kamera Kinect dapat digunakan untuk pelatihan dan pengujian pengenalan gerakkan manusia dengan pendekatan deep learning, selain tracking dan estimasi letak dan posisi tubuh manusia. Perbaikan gerakkan manusia yang tertutup bagian tubuh lainnya masih menjadi tantangan. Tujuan penelitian: membuat rancang bangun aplikasi perbaikan gerakkan model skeleton 3D dari dataset hasil kamera Kinect menggunakan pendekatan penghalusan histogram interpolasi 3D pada bagian tubuh manusia. Kajian berupa simulasi perbaikan gerakkan model skeleton dalam rangka pengembangan aplikasi animasi berbasis key-frame melalui perekaman kamera Kinect. Metode pengembangan prototitpe melalui tahapan siklus analisis, disain, implementasi. Tahap analisis awal memilih dataset hasil kamera kinect dengan mengkaji format file dan struktur data. Kemudian mengekstraksi beberapa kategori gerakkan kedalam kedalam format file untuk eksperimen. Melalui disain awal pengembangan program dilakukan penyesuaian skala angka untuk menghasilkan histogram yang dapat memperlihatkan bagian kesalahan gerakkan secara signikan menggunakan Root Mean Squared Error (RMSE). Selanjutnya pengembangan untuk perbaikan data melalui pendekatan penghalusan interpolasi 3D. Perbaikan gerakkan ditinjau dari pengukuran kesalahan resultante interpolasi dari setiap watu t0, t1, t2 dan t3 histogram dengan metode RMSE dan pengamatan secara visual. menunjukkan hasil cukup signifikan mendekati gerakkan yang normal.Kata Kunci : dataset, kamera kinect, interpolasi, model skeleto

Skeleton-aided Articulated Motion Generation

This work make the first attempt to generate articulated human motion sequence from a single image. On the one hand, we utilize paired inputs including human skeleton information as motion embedding and a single human image as appearance reference, to generate novel motion frames, based on the conditional GAN infrastructure. On the other hand, a triplet loss is employed to pursue appearance-smoothness between consecutive frames. As the proposed framework is capable of jointly exploiting the image appearance space and articulated/kinematic motion space, it generates realistic articulated motion sequence, in contrast to most previous video generation methods which yield blurred motion effects. We test our model on two human action datasets including KTH and Human3.6M, and the proposed framework generates very promising results on both datasets.Comment: ACM MM 201

On the use of natural user interfaces in physical rehabilitation: a web-based application for patients with hip prosthesis

This study aims to develop a telemedicine platform for self-motor rehabilitation and remote monitoring by health professionals, in order to enhance recovery in patients after hip replacement. The implementation of such a technology is justified by medical (improvement of the recovery process by the possibility to perform rehabilitation exercises more frequently), economic (reduction of the number of medical appointments and the time patients spend at the hospital), mobility (diminution of the transportation to and from the hospital) and ethics (healthcare democratization and increased empowerment of the patient) purposes. The Kinect camera is used as a Natural User Interface to capture the physical exercises performed at home by the patients. The quality of the movement is evaluated in real-time by an assessment module implemented according to a Hidden-Markov Model approach. The results show a high accuracy in the evaluation of the movements (92% of correct classification). Finally, the usability of the platform is tested through the System Usability Scale (SUS). The overall SUS score is 81 out of 100, which suggests a good usability of the Web application. Further work will focus on the development of additional functionalities and an evaluation of the impact of the platform on the recovery process

Rotation Correction Method Using Depth-Value Symmetry of Human Skeletal Joints for Single RGB-D Camera System

Most red-green-blue and depth (RGB-D) motion-recognition technologies employ both depth and RGB cameras to recognize a user\u27s body. However, motion-recognition solutions using a single RGB-D camera struggle with rotation recognition depending on the device-user distance and field-of-view. This paper proposes a near-real-time rotational-coordinate-correction method that rectifies a depth error unique Microsoft Kinect by using the symmetry of the depth coordinates of the human body. The proposed method is most effective within 2 m, a key range in which the unique depth error of Kinect occurs, and is anticipated to be utilized in applications requiring low cost and fast installation. It could also be useful in areas such as media art that involve unspecified users because it does not require a learning phase. Experimental results indicate that the proposed method has an accuracy of 85.38%, which is approximately 12% higher than that of the reference installation method

Using a Deep Learning Model on Images to Obtain a 2D Laser People Detector for a Mobile Robot

Recent improvements in deep learning techniques applied to images allow the detection of people with a high success rate. However, other types of sensors, such as laser rangefinders, are still useful due to their wide field of vision and their ability to operate in different environments and lighting conditions. In this work we use an interesting computational intelligence technique such as the deep learning method to detect people in images taken by a mobile robot. The masks of the people in the images are used to automatically label a set of samples formed by 2D laser range data that will allow us to detect the legs of people present in the scene. The samples are geometric characteristics of the clusters built from the laser data. The machine learning algorithms are used to learn a classifier that is capable of detecting people from only 2D laser range data. Our people detector is compared to a state-of-the-art classifier. Our proposal achieves a higher value of F1 in the test set using an unbalanced dataset. To improve accuracy, the final classifier has been generated from a balanced training set. This final classifier has also been evaluated using a test set in which we have obtained very high accuracy values in each class. The contribution of this work is 2-fold. On the one hand, our proposal performs an automatic labeling of the samples so that the dataset can be collected under real operating conditions. On the other hand, the robot can detect people in a wider field of view than if we only used a camera, and in this way can help build more robust behaviors.This work has been supported by the Spanish Government TIN2016- 76515-R Grant, supported with Feder funds

A fast and accurate motion descriptor for human action recognition applications

International audienc

Telerehabilitation platform for hip surgery recovery

CEPRA XI-2017-15.The enhancement of ubiquitous and pervasive computing brings new perspectives in terms of medical rehabilitations. In that sense, the present study proposes a Web-based platform to promote the reeducation of patients after hip replacement surgery. This project focuses on two fundamental aspects in the development of a suitable telerehabilitation application, which are: (i) being based on an affordable technology and (ii) providing the patients with a real-time assessment of the correctness of their movements. A comparative test shows that the movement's evaluation carried out by therapists is consistent with the output of the automatic assessment module. Improvements of the algorithm are discussed, in order to increase the accuracy and depth of the analysis.authorsversionpublishe