@inproceedings{877e597ceb89451db42c551d95f3d560,
title = "SensIR: Detecting hand gestures with a wearable bracelet using infrared transmission and reflection",
abstract = "Gestures have become an important tool for natural interaction with computers and thus several wearables have been developed to detect hand gestures. However, many existing solutions are unsuitable for practical use due to low accuracy, high cost or poor ergonomics. We present SensIR, a bracelet that uses near-infrared sensing to infer hand gestures. The bracelet is composed of pairs of infrared emitters and receivers that are used to measure both the transmission and reflection of light through/off the wrist. SensIR improves the accuracy of existing infrared gesture sensing systems through the key idea of taking measurements with all possible combinations of emitters and receivers. Our study shows that SensIR is capable of detecting 12 discrete gestures with 93.3% accuracy. SensIR has several advantages compared to other systems such as high accuracy, low cost, robustness against bad skin coupling and thin form-factor.",
keywords = "Gesture recognition, Infrared, Wearables",
author = "Jess McIntosh and Asier Marzo and Mike Fraser",
year = "2017",
month = oct,
day = "20",
doi = "10.1145/3126594.3126604",
language = "English",
series = "UIST 2017 - Proceedings of the 30th Annual ACM Symposium on User Interface Software and Technology",
publisher = "Association for Computing Machinery",
pages = "593--597",
booktitle = "UIST 2017 - Proceedings of the 30th Annual ACM Symposium on User Interface Software and Technology",
address = "USA United States",
note = "30th Annual ACM Symposium on User Interface Software and Technology, UIST 2017 ; Conference date: 22-10-2017 Through 25-10-2017",
}