Interpreting and tracking finger motion in free space is of use in the development of control interfaces for augmented and virtual reality systems. One approach to create human–machine interfaces capable of accurate finger motion recognition is to use wearable sensors with integrated neuromorphic computing. Here we show that an integrated titanium-oxide-based artificial synapse array and organic motion sensor can be conformably attached to a finger and provide real-time motion recognition. The synaptic device and sensor exhibit well-defined synaptic and light-responsive electrical properties, respectively, as well as flexibility and mechanical robustness. The integrated synapses–sensor enables optical–electrical signal conversion and summation of post-synaptic current. Finger motions for time-resolved digit patterns (0–9) can be learned and recognized with an accuracy of up to 95% at varying strains and up to 100 strain cycles.
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© 2023, The Author(s), under exclusive licence to Springer Nature Limited.
ASJC Scopus subject areas
- Electronic, Optical and Magnetic Materials
- Electrical and Electronic Engineering