Motion data gloves are frequently used input devices that interpret human hand gestures for applications such as virtual reality and human-computer interaction. However, commercial motion data gloves are too expensive for consumer use, and this has limited their popularity. This paper presents an inexpensive motion data glove to overcome this obstacle. To lower costs, we designed our glove to use single-channel video instead of expensive motion-sensing fibers or multi-channel video. Our visual motion data glove is composed of an inexpensive consumer glove with attached thin-bar-type optical indicators and a closed-form reconstruction algorithm that can overcome the common disadvantages of single-channel video approaches, i.e., occlusion and the need for inconvenient iterative reconstruction algorithms. Our low-cost visual motion data gloves are used to interpret human hand gestures, and the resulting performance is evaluated.