Hu proposed a method that recognizes finger movements by detecting wrist muscles for human–computer interaction (HCI). Considering human habits and aesthetics, the sensor is placed on the back of the wrist. We first designed a polyvinylidene fluoride (PVDF) piezoelectric thin-film sensor unit with a planar elastic substrate. By studying the effects of the hardness and thickness of the substrate, we designed the sensor unit to have a Shore-A hardness of 23 and a thickness of 2 mm. Then we constructed a 4 × 2 sensor matrix with a size of 25 × 15 mm2. To build a finger movement dataset, we collected wrist dorsal movement signals when the fingers moved using the sensor matrix. Then we used a four-layer back-propagation (BP) neural network to recognize the finger movements. We experimentally demonstrated that even on the dorsal wrist side, finger movements could be recognized. The recognition rate of the general model using mixed personal data was 79%. In comparison, the recognition rate of the individual model using personal data was 94%.