3D Motion Reconstruction from 2D Motion Data Using Multimodal Conditional Deep Belief Network

In this paper, we propose a deep generative model named Multimodal Conditional Deep Belief Network (MCDBN) for cross-modal learning of 3D motion data and their non-injective 2D projections on the image plane. This model has a three sectional structure, which learns conditional probability distribution of 3D motion data given 2D projections. Two distinct Conditional Deep Belief Networks (CDBNs), encode the real-valued spatiotemporal patterns of 2D and 3D motion time series captured from subjects’ movements into the compact representations. The third part includes a Multimodal Restricted Boltzmann Machines (MRBMs) which in the training process, learns the relationship between the compact representations of data modalities by variation information criteria. As a result, conditioned on a 2D motion data obtained from a video, MCDBN can regenerate 3D motion data in generation phase. We introduce Pearson correlation coefficient of ground truth and regenerated motion signals as a new evaluation metric in motion reconstruction problems. The model is trained with human motion capture data and the results show that the real and the regenerated signals are highly correlated which means the model can reproduce dynamical patterns of motion accurately

Planning for Complementarity: An Examination of the Roll and Opportunities of First-Tier and Second-Tier Cities Along the High-Speed Rail Network in California, Research Report 11-17

The coming of California High-Speed Rail (HSR) offers opportunities for positive urban transformations in both first-tier and second-tier cities. The research in this report explores the different but complementary roles that first-tier and second-tier cities along the HSR network can play in making California more sustainable and less dependent on fossil fuels while reducing mobile sources of greenhouse gas emissions and congestion at airports and on the state’s roadways. Drawing from case studies of cities in Northern and Southern California, the study develops recommendations for the planning, design, and programming of areas around California stations for the formation of transit-supportive density nodes

Prosthetic Control and Sensory Feedback for Upper Limb Amputees

Hand amputation could dramatically degrade the life quality of amputees. Many amputees use prostheses to restore part of the hand functions. Myoelectric prosthesis provides the most dexterous control. However, they are facing high rejection rate. One of the reasons is the lack of sensory feedback. There is a need for providing sensory feedback for myoelectric prosthesis users. It can improve object manipulation abilities, enhance the perceptual embodiment of myoelectric prostheses and help reduce phantom limb pain. This PhD work focuses on building bi-directional prostheses for upper limb amputees. In the introduction chapter, first, an overview of upper limb amputee demographics and upper limb prosthesis is given. Then the human somatosensory system is briefly introduced. The next part reviews invasive and non-invasive sensory feedback methods reported in the literature. The rest of the chapter describes the motivation of the project and the thesis organization. The first step to build a bi-directional prostheses is to investigate natural and robust multifunctional prosthetic control. Most of the commerical prostheses apply non-pattern recognition based myoelectric control methods, which offers only limited functionalities. In this thesis work, pattern recognition based prosthetic control employing three commonly used and representative machine learning algorithms is investigated. Three datasets involving different levels of upper arm movements are used for testing the algorithm effectiveness. The influence of time-domain features, window and increment sizes, algorithms, and post-processing techniques are analyzed and discussed. The next three chapters address different aspects of providing sensory feedback. The first focus of sensory feedback process is the automatic phantom map detection. Many amputees have referred sensation from their missing hand on their residual limbs (phantom maps). This skin area can serve as a target for providing amputees with non-invasive tactile sensory feedback. One of the challenges of providing sensory feedback on the phantom map is to define the accurate boundary of each phantom digit because the phantom map distribution varies from person to person. Automatic phantom map detection methods based on four decomposition support vector machine algorithms and three sampling methods are proposed. The accuracy and training/ classification time of each algorithm using a dense stimulation array and two coarse stimulation arrays are presented and compared. The next focus of the thesis is to develop non-invasive tactile display. The design and psychophysical testing results of three types of non-invasive tactile feedback arrays are presented: two with vibrotactile modality and one with multi modality. For vibrotactile, two types of miniaturized vibrators: eccentric rotating masses (ERMs) and linear resonant actuators (LRAs) were first tested on healthy subjects and their effectiveness was compared. Then the ERMs are integrated into a vibrotactile glove to assess the feasibility of providing sensory feedback for unilateral upper limb amputees on the contralateral hand. For multimodal stimulation, miniature multimodal actuators integrating servomotors and vibrators were designed. The actuator can be used to deliver both high-frequency vibration and low-frequency pressures simultaneously. By utilizing two modalities at the same time, the actuator stimulates different types of mechanoreceptors and thus h