Slepyan Ariel, Zakariaie Michael, Tran Trac, Thakor Nitish
Electrical and Computer Engineering Department, The Johns Hopkins University, Baltimore, MD 21218, USA.
Biomedical Engineering Department, The Johns Hopkins University, Baltimore, MD 21218, USA.
Sensors (Basel). 2024 Jun 29;24(13):4243. doi: 10.3390/s24134243.
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.
随着用于假肢、可穿戴设备及其他生物医学应用的具有更高时空分辨率的触觉传感系统不断发展，它们需要更快的采样率并生成更大的数据流。稀疏变换可通过实现压缩采样和通过压缩进行高效数据存储来缓解这些需求。然而，关于用于触觉交互的最佳稀疏变换的研究仍滞后。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，并比较它们在压缩和稀疏性方面的权衡。我们在一个公开可用的高密度触觉物体抓取数据集（548个传感器触觉手套，抓取26个物体）上测试了这些稀疏变换。此外，我们研究了哪种维度的小波变换——一维、二维或三维——能最好地压缩这些触觉交互。我们的结果表明，小波变换在压缩触觉数据方面非常高效，并且可以产生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，其次是三维，最后是二维。总体而言，用于粗略近似的最佳小波是在时间上评估的Symlets 4，它可以稀疏到0.5%的稀疏度，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协助大型触觉阵列的压缩采样，并为像神经假肢这样计算资源受限的移动平台上的实时处理释放计算资源。
