University of Twente Student Theses
Generative Adversarial Networks of Missing Sensor Data Imputation for 3D Body Tracking
Song, X. (2020) Generative Adversarial Networks of Missing Sensor Data Imputation for 3D Body Tracking.
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| Abstract: | Human body motion tracking has important applications in many fields, not restricted to medical, biological science, virtual reality, sports and animation. While solving the problem of human motion tracking it is not always possible to obtain a large dataset without missing data or annotation. This creates challenges in developing algorithms that require such datasets. Moreover, reducing the number of sensors by generating data for these reduced sensors for motion capture can decrease the usage complexity. This thesis aims to design and evaluate efficient and precise machine learning models to impute the missing data for sensors used in body tracking solutions. Firstly, various traditional methods for data imputation and their shortcomings are introduced briefly. The characteristics of these methods that make them unsuitable for our tasks are then discussed. The human motion tracking datasets used in this thesis are obtained from sensors used in Xsens MVN Link inertial motion tracking system. Inspired by the traditional data imputation methods, we develop machine learning algorithms to deal with data imputation issues for human body motion tracking datasets. We first generate a model based on Hidden Markov Model (HMM) for data imputation in a time-series sensor signal. Further, an autoencoder based on convolutional and deconvolutional neural networks has been designed to impute the missing data in the motion tracking dataset. Finally, we investigate a Generative Adversarial Network (GAN) based method to solve the data imputation problem on the same dataset. The experiments are carried out with different lengths of missing data. The results of these three methods are evaluated and visualized. These algorithms are compared against two single data imputation methods: Mean Imputation and Zero Imputation. Dynamic Time Warping (DTW) and the Root Mean Square Error (RMSE) distance between the original dataset and the estimated imputed output are used for the evaluation of the three algorithms. The DTW measure shows that the proposed machine learning perform better than the two simpler single imputation methods. The DTW measure shows that proposed machine learning models produce better suited time series output as compared to Zero Imputation and Mean Imputation. HMM and autoencoder based models have better results on our datasets. Among the three algorithms, MisGAN based model achieves the best results. For the dataset with missing data of length 32 time frames, our MisGAN reduces the DTW value by 50:2% compared to Zero Imputation and reduces the DTW value by 50:4% compared to Mean Imputation. However, our models do not show obvious better performance than the two single imputation methods when evaluated using the RMSE measure. Through the analysis and visualization of these results, we consider that DTW is more suitable for analyzing the difference between time series data than RMSE. This research can be applied as solutions for data imputation for human motion tracking datasets, but further research needs to be conducted to make our models more suitable to human motion tracking datasets and to tune the parameters of models to improve the performance of them. |
| Item Type: | Essay (Master) |
| Faculty: | EEMCS: Electrical Engineering, Mathematics and Computer Science |
| Subject: | 54 computer science |
| Programme: | Computer Science MSc (60300) |
| Link to this item: | https://purl.utwente.nl/essays/83687 |
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