pith:EAXWJMPE
MicroBi-ConvLSTM: An Ultra-Lightweight Efficient Model for Human Activity Recognition on Resource Constrained Devices
MicroBi-ConvLSTM deploys successfully on all eight HAR benchmarks across both Pico 2 and ESP32 microcontrollers with an average of 11.4K parameters.
arxiv:2602.06523 v3 · 2026-02-06 · cs.CV · cs.HC
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\pithnumber{EAXWJMPE6FVBSH5GI35CHZCSFT}
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Claims
MicroBi-ConvLSTM is the only architecture achieving full 8/8 dataset coverage on both platforms, with 72.8 ms average latency on Pico 2 and 97.9% PyTorch parity on ESP32, while all three baselines show partial or complete deployment failure.
That the reported parameter count and accuracy hold after accounting for operating-system overhead on the target microcontrollers and that the chosen eight benchmarks are representative enough to generalize to real-world deployment without additional tuning.
MicroBi-ConvLSTM achieves competitive accuracy on eight HAR benchmarks with only 11.4K parameters and successful full deployment on two microcontroller platforms where prior models fail.
Formal links
Receipt and verification
| First computed | 2026-05-20T00:03:05.027582Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
202f64b1e4f16a191fa646fa23e4522cf50b35d34834f53598470ca80f38069c
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/EAXWJMPE6FVBSH5GI35CHZCSFT \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 202f64b1e4f16a191fa646fa23e4522cf50b35d34834f53598470ca80f38069c
Canonical record JSON
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