STEMNIST: Spiking Tactile Extended MNIST Neuromorphic Dataset
Anubhab Tripathi, Li Gaishan, Zhengnan Fu, Chiara Bartolozzi, Bert E. Shi, Arindam Basu

TL;DR
STEMNIST is a large-scale neuromorphic tactile dataset with 35 alphanumeric classes, designed to advance event-based haptic recognition and neuromorphic hardware testing.
Contribution
It introduces a comprehensive tactile dataset extending ST-MNIST to alphanumeric characters, with detailed event-based encoding and baseline recognition benchmarks.
Findings
Baseline CNN accuracy: 90.91%
Baseline SNN accuracy: 89.16%
Dataset supports neuromorphic hardware evaluation
Abstract
Tactile sensing is essential for robotic manipulation, prosthetics and assistive technologies, yet neuromorphic tactile datasets remain limited compared to their visual counterparts. We introduce STEMNIST, a large-scale neuromorphic tactile dataset extending ST-MNIST from 10 digits to 35 alphanumeric classes (uppercase letters A--Z and digits 1--9), providing a challenging benchmark for event-based haptic recognition. The dataset comprises 7,700 samples collected from 34 participants using a custom \(16\times 16\) tactile sensor array operating at 120 Hz, encoded as 1,005,592 spike events through adaptive temporal differentiation. Following EMNIST's visual character recognition protocol, STEMNIST addresses the critical gap between simplified digit classification and real-world tactile interaction scenarios requiring alphanumeric discrimination. Baseline experiments using conventional…
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Taxonomy
TopicsAdvanced Sensor and Energy Harvesting Materials · Advanced Memory and Neural Computing · Neural Networks and Reservoir Computing
