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Abstract：
As higher spatiotemporal resolution tactile sensing systems are being developed for prosthetics, wearables, and other biomedical applications, they demand faster sampling rates and generate larger data streams. Sparsifying transformations can alleviate these requirements by enabling compressive sampling and efficient data storage through compression. However, research on the best sparsifying transforms for tactile interactions is lagging. In this work we construct a library of orthogonal and biorthogonal wavelet transforms as sparsifying transforms for tactile interactions and compare their tradeoffs in compression and sparsity. We tested the sparsifying transforms on a publicly available high-density tactile object grasping dataset (548 sensor tactile glove, grasping 26 objects). In addition, we investigated which dimension wavelet transform-1D, 2D, or 3D-would best compress these tactile interactions. Our results show that wavelet transforms are highly efficient at compressing tactile data and can lead to very sparse and compact tactile representations. Additionally, our results show that 1D transforms achieve the sparsest representations, followed by 3D, and lastly 2D. Overall, the best wavelet for coarse approximation is Symlets 4 evaluated temporally which can sparsify to 0.5% sparsity and compress 10-bit tactile data to an average of 0.04 bits per pixel. Future studies can leverage the results of this paper to assist in the compressive sampling of large tactile arrays and free up computational resources for real-time processing on computationally constrained mobile platforms like neuroprosthetics.
摘要：
随着更高的时空分辨率触觉传感系统正在开发用于假肢，可穿戴设备,和其他生物医学应用，它们需要更快的采样率并生成更大的数据流。稀疏的转换可以通过压缩实现压缩采样和高效的数据存储来缓解这些要求。然而,关于触觉交互的最佳稀疏化转换的研究还很滞后。在这项工作中，我们构建了一个正交和双正交小波变换库，作为触觉交互的稀疏变换，并比较了它们在压缩和稀疏性方面的权衡。我们在公开可用的高密度触觉对象抓取数据集（548传感器触觉手套，抓住26个对象)。此外,我们研究了小波变换-1D，2D,或3D-最好压缩这些触觉交互。我们的结果表明，小波变换在压缩触觉数据方面非常有效，并且可以导致非常稀疏和紧凑的触觉表示。此外,我们的结果表明，一维变换实现了最稀疏的表示，其次是3D,最后是2D。总的来说,用于粗略逼近的最佳小波是在时间上评估的Symlet4，它可以稀疏到0.5％的稀疏性，并将10位触觉数据压缩到平均每像素0.04位。未来的研究可以利用本文的结果来帮助大型触觉阵列的压缩采样，并释放计算资源，以便在计算受限的移动平台（如神经假体）上进行实时处理。
