Sparsity and redundancy reduction have been shown to be useful in machine learning, but empirical evaluation has been performed primarily on classification tasks using datasets of natural images and sounds. Similarly, the performance of unsupervised feature learning followed by supervised fine-tuning has primarily focused on classification tasks. In comparison, relatively little work has investigated the use of sparse codes for representing human movements and poses, or for using these codes in regression tasks with movement data. This paper defines a basic coding and regression architecture for evaluating the impact of sparsity when coding human pose information, and tests the performance of several coding methods within this framework for the task of mapping from a kinematic (joint angle) modality to a dynamic (joint torque) one. In addition, we evaluate the performance of unified loss functions defined on the same class of models. We show that, while sparse codes are useful for effective mappings between modalities, their primary benefit for this task seems to be in admitting overcomplete codebooks. We make use of the proposed architecture to examine in detail the sources of error for each stage in the model under various coding strategies. Furthermore, we show that using a unified loss function that passes gradient information between stages of the coding and regression architecture provides substantial reductions in overall error.

Wilderness search and rescue (WiSAR) is a challenging problem because of the large areas and often rough terrain that must be searched. Using mini-UAVs to deliver aerial video to searchers has potential to support WiSAR efforts, but a number of technology and human factors problems must be overcome to make this practical. At the source of many of these problems is a desire to manage the UAV using as few people as possible, so that more people can be used in ground-based search efforts. This paper uses observations from two informal studies and one formal experiment to identify what human operators may be unaware of as a function of autonomy and information display. Results suggest that progress is being made on designing autonomy and information displays that may make it possible for a single human to simultaneously manage the UAV and its camera in WiSAR, but that adaptable displays that support systematic navigation are probably needed.