The researchers developed a fingernail-sized device using 7 photo-reflective sensors to detect finger microgestures based on fingertip skin deformation. They implemented a random forest classifier to recognize 11 gestures with an average accuracy of 91.1% for the general model and 91.5% for the individual model. Future work will focus on addressing limitations like user dependence and developing a device that can be worn comfortably for real-world use.