Hi everyone,
We are prototyping a wearable spatial input interface using an ESP32-S3 and finger-mounted LSM6DSOX sensors, delivering discrete keystrokes over BLE HID with a strict sub-20ms end-to-end latency budget.
Currently, our pipeline uses a heuristic state machine: dynamic jerk thresholding, baseline gravity tracking via a low-pass filter, and a temporal lockout window to prevent false triggers during finger recoil/return strokes.
As we scale from a single-node PoC to a 5-finger chording matrix, we are evaluating whether to stick to an optimized deterministic state machine or implement an on-device TinyML classifier (such as a lightweight 1D-CNN or quantized decision forest) directly on the ESP32.
Key considerations:
- How does TinyML inference window latency hold up against strict <15ms processing constraints?
- Is supervised classification resilient enough to handle parasitic tendon movement across adjacent fingers without extensive per-user training?
Would love to hear from anyone who has deployed gesture models on fast-moving finger IMUs.