Flexible Self‐Powered Keypad with Low Crosstalk for Neuropsychological Assessment and Intelligent Systems

Abstract Flexible human‐machine interfaces (HMIs) encounter several significant challenges, including intricate architectures, reliance on external power, and crosstalk. This study develops a flexible self‐powered Keypad based on triboelectric nanogenerators (TENGs) and introduces an effective strategy to greatly reduce internal crosstalk based on separate cavity structure. A theoretical model is established to clarify the relationship among finger‐tapping force, device's mechanical strain, and TENG output. Comparative experiments demonstrate that the crosstalk ratios in the upper, right, and upper‐right units adjacent to the central unit are substantially reduced to 24%, 29%, and 15%, respectively. The flexible Keypad, as an HMI, exhibits superior response time (34 ms), outstanding durability (30 000 continuous tapping), and linear feedback in response to finger‐tapping force (1–10 N). In Finger Tapping Test, the self‐powered Keypad accurately detects subtle variations in finger‐tapping patterns, both between individuals and across different fingers of the same individual, offering valuable reference for neuropsychological assessments. By integrating machine learning, the self‐powered Keypad achieves high accuracy in individual identification (100%) and handwritten digit recognition (97%). A mixed‐reality gaming control system based on the flexible self‐powered Keypad is developed, enabling real‐time precise control of game characters in virtual environment, thereby broadening the application prospects of self‐powered HMIs.