Triboelectric nanogenerators (TENGs) offer a convenient means to convert mechanical energy from human movement into electricity, exhibiting the application prospects in human behavior monitoring. Nevertheless, the present methods to improve the device monitoring effect are limited to the design of a triboelectric material level (control of electron gain and loss ability). As compared with reported work, we improve the monitoring effect of TENG-based tactile sensors by optimizing the structure of the electrode/triboelectric material interface by means of a multiple strains mechanism. Cu@Ni double-clad waste woven fabrics are used as electrodes, which are characterized by a structure with a large number of pores formed between the fibers, greatly increasing the specific surface area of the electrode and generating dynamic strain under differentiated stress fields because of their different elastic modulus. To be exact, the resin layer undergoes elastic deformation under 0.64-4.47 kPa external stress and a new deformation generates at the electrode/triboelectric material interface induced by Cu@Ni double-clad woven fabrics slip under 4.47-63.84 kPa external stress, resulting in the accumulation of triboelectric charges on the PDMS surface. The establishment of multiple strains in triboelectric material further facilitates the generation of distinct triboelectric signal waveforms that are easily distinguishable by its amplitude and peak form. Besides, combined with deep machine learning and the triboelectric effect, in an open setting, the identification accuracy of five distinct behaviors approaches 100%. This provides a new pathway for enhancing identification accuracy of a TENG-based tactile sensor.