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The main advantage of wearable devices lies in enabling them to be tracked without external infrastructure. However, unlike vision (cameras), there is a dearth of large-scale training data to develop robust ML models for wearable devices. SIHeDA-Net (Sensor-Image Heterogeneous Domain Adaptation) uses training data from public images of American Sign Language (ASL) that can be used for inferences on sensors even with errors by bridging the domain gaps through latent space transfer.
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Friday 8th July
Poster Session 3.1 - onsite 15:20 - 16:20, virtual 11:00 - 12:00 (UTC+2)
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