In a potential breakthrough in wearable sensing technology, researchers from Cornell and the University of Wisconsin, Madison, have designed a wrist-mounted device that continuously tracks the entire human hand in 3D.
The bracelet, called FingerTrak, can sense and translate into 3D the many positions of the human hand, including 20 finger joint positions, using three or four miniature, low-resolution thermal cameras that read contours on the wrist. The device could be used in sign language translation, virtual reality, mobile health, human-robot interaction and other areas, the researchers said.
“This was a major discovery by our team – that by looking at your wrist contours, the technology could reconstruct in 3D, with keen accuracy, where your fingers are,” said Cheng Zhang, assistant professor of information science and director of Cornell’s new , where FingerTrak was developed. “It’s the first system to reconstruct your full hand posture based on the contours of the wrist.”
Yin Li, assistant professor of biostatistics and medical informatics at the University of Wisconsin, Madison School of Medicine and Public Health, contributed to the software behind FingerTrak.
Their paper, “,” was published in June in the Proceedings of the Association for Computing Machinery on Interactive, Mobile, Wearable and Ubiquitous Technologies. It also will be presented at the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing, taking place virtually Sept. 12-16.
Past wrist-mounted cameras have been considered too bulky and obtrusive for everyday use, and most could reconstruct only a few discrete hand gestures.
FingerTrak’s breakthrough is a lightweight bracelet, allowing for free movement. Instead of using cameras to directly capture the position of the fingers, the focus of most prior research, FingerTrak uses a combination of thermal imaging and machine learning to virtually reconstruct the hand. The bracelet’s four miniature, thermal cameras – each about the size of a pea – snap multiple “silhouette” images to form an outline of the hand.
A deep neural network then stitches these silhouette images together and reconstructs the virtual hand in 3D. Through this method, Zhang and his fellow researchers were able to capture the entire hand pose, even when the hand is holding an object.
“The most novel technical finding in this work is discovering that the contours of the wrist are enough to accurately predict the entire hand posture,” Zhang said. “This finding allows the reposition of the sensing system to the wrist, which is more practical for usability.”
The technology has a wide range of possible uses, the researchers said, but Zhang said the most promising is its potential application in sign language translation.
“Current sign language translation technology requires the user to either wear a glove or have a camera in the environment, both of which are cumbersome,” he said. “This could really push the current technology into new areas.”
FingerTrak could also have an impact on health care applications, specifically in monitoring disorders that affect fine-motor skills, Li said.
“How we move our hands and fingers often tells about our health condition,” Li said. “A device like this might be used to better understand how the elderly use their hands in daily life, helping to detect early signs of diseases like Parkinson’s and Alzheimer’s.”
In addition to Zhang and Li, the FingerTrak team includes three collaborators who were visiting undergraduate students to Cornell’s SciFi Lab last fall: first author Fang Hu of Shanghai Jiao Tong University; Peng He of Hangzhou Dianzi University; and Songlin Xu of the University of Science and Technology of China.
Louis DiPietro is communications coordinator for the Department of Information Science.
