As the field of human-computer interaction continues to evolve, there is a growing need for new methods of gesture recognition that can be used in a variety of applications, from gaming and entertainment to healthcare and robotics. While traditional methods of gesture recognition rely on cameras or other optical sensors, these systems can be limited by factors such as lighting conditions and occlusions.

Dataset Files

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[1] Sarah Seifi, Tobias Sukianto, Cecilia Carbonelli, "60 GHz FMCW Radar Gesture Dataset", IEEE Dataport, 2024. [Online]. Available: http://dx.doi.org/10.21227/s12w-cc46. Accessed: Jul. 22, 2024.
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doi = {10.21227/s12w-cc46},
url = {http://dx.doi.org/10.21227/s12w-cc46},
author = {Sarah Seifi; Tobias Sukianto; Cecilia Carbonelli },
publisher = {IEEE Dataport},
title = {60 GHz FMCW Radar Gesture Dataset},
year = {2024} }
TY - DATA
T1 - 60 GHz FMCW Radar Gesture Dataset
AU - Sarah Seifi; Tobias Sukianto; Cecilia Carbonelli
PY - 2024
PB - IEEE Dataport
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ER -
Sarah Seifi, Tobias Sukianto, Cecilia Carbonelli. (2024). 60 GHz FMCW Radar Gesture Dataset. IEEE Dataport. http://dx.doi.org/10.21227/s12w-cc46
Sarah Seifi, Tobias Sukianto, Cecilia Carbonelli, 2024. 60 GHz FMCW Radar Gesture Dataset. Available at: http://dx.doi.org/10.21227/s12w-cc46.
Sarah Seifi, Tobias Sukianto, Cecilia Carbonelli. (2024). "60 GHz FMCW Radar Gesture Dataset." Web.
1. Sarah Seifi, Tobias Sukianto, Cecilia Carbonelli. 60 GHz FMCW Radar Gesture Dataset [Internet]. IEEE Dataport; 2024. Available from : http://dx.doi.org/10.21227/s12w-cc46
Sarah Seifi, Tobias Sukianto, Cecilia Carbonelli. "60 GHz FMCW Radar Gesture Dataset." doi: 10.21227/s12w-cc46