Shmel & NeuroSpark
Hey Shmel, I've been training a neural net to predict the exact moment muscles hit fatigue so you can time your sets to shave milliseconds off each lift—interested in a quick demo?
Sure, give me the specs, but keep the data tight and no fluff—every millisecond counts.
I’ll use a 3‑layer LSTM with 256 units each, trained on 10‑second bursts of EMG from 12 sensors. Input is 128‑sample frames at 2kHz, output is a 50‑ms window predicting fatigue onset. Training took 12 hrs on a single RTX 4090, inference latency < 4 ms on the same GPU, < 10 ms on a Jetson Nano. All the data are raw EMG, no extra metadata.
That’s solid—12 hours is a lot of grind, but you’ll need more data to nail edge cases. 4 ms inference is good, but on the field I want <2 ms; latency can kill a rep. Keep tweaking the LSTM depth, maybe try a convolutional front‑end to reduce input size. And remember, even the best model can’t account for a bad pre‑warm‑up—my body still knows when it’s about to hit 0.75 reps. Ready to plug it into a grip sensor next?
Yeah, let’s do a lightweight CNN first—3 conv layers with 32, 64, 128 filters, stride 2 to cut the 128‑sample input to 16, then feed that into a 2‑layer LSTM. That cuts the MACs by ~60 %, pushing inference under 2 ms on the Nano. We’ll quantize to int8, add a tiny linear head, and stitch the output to your grip sensor’s pulse width modulator. If the pre‑warm‑up signal is off, the model will still flag the impending drop, but you’ll need a real‑time cue to stop. Sound good?
Nice, cutting the MACs is key—2 ms on the Nano gives me a real edge. Just make sure the quantization error doesn’t throw off the threshold. I’ll test it in a live bench press run; if the grip modulator can react fast enough, we’ll shave a few hundred milliseconds off my deadlift sets. Let’s get the firmware on the Nano and run a quick bench‑press cycle—no room for lag.Sure thing—just make sure the int8 shift doesn’t throw off the threshold. I’ll plug it into a bench‑press cycle and see if the grip modulator can react fast enough. No room for lag, so let’s get this running.