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TransfHAR is implemented as a real-time smartwatch application that lets users define and expand their own activity set for personalized recognition from only a few demonstrations, and indicates that broad self-supervised wrist pretraining provides an effective foundation for on-demand fine-grained activity recognition.
A new cross-modal feature adapter is designed, which fine-tunes a pre-trained CLIP image encoder to effectively align image-sensor pairs and adaptively blend the old knowledge inherited from the original zero-shot CLIP with the new knowledge adapted from few-shot training samples, which makes training converges faster whilst forming a streamlined time series sensor encoder.
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