Use that Pinky on the ‘A’ Key: A Finger-Key Identification Module for a Touch Typing Trainer

Typing tests list out words for the user to repeat. These tests measure metrics that quantify the user’s ability to type. However, conventional typing metrics do not measure correct finger placement. This aspect of typing may affect the user’s health. It is also a crucial aspect of keyboard typing education. As such, a method to identify which finger is used to press which key is beneficial. This paper introduces a new technique to achieve this by developing a finger-key identification module that utilizes computer vision algorithms to detect the keys in a keyboard, and a ready-made machine learning solution to track fingers while typing. This module was successful in finger-key identification with an accuracy of 99.58% in a dataset of 942 keypresses. It also had an average finger-key identification time of 0.083 s, which allows it to perform real-time finger-key identification for typing speeds up to 143.040 words per minute, well beyond the median typing speed of the population. Two metrics for correct finger placement were defined and were implemented in a proof-of-concept trainer: (1) finger placement accuracy and (2) historical finger placement accuracy.