Rehabilitation is broken. Patients drop out. Progress isn’t measurable. Outcomes aren’t trusted. We’re fixing this with AI agents.

ARMIC is learning to understand movement. Today we’re testing the first stage of our motion capture pipeline using wireless IMU devices and Edge Impulse. The goal: turn raw acceleration gyroscope data into rehabilitation exercise detection. Before ARMIC can adapt to a patient, it needs to recognize how the patient is moving. Perception → Motion Capture → Adaptation 🤖

Foundation phase update 🛠️ Last week we demonstrated local Edge AI on the Arduino UNO Q. This week we're focusing on perception. We're experimenting with ECG signals, motion sensing, and MQTT-based data pipelines to help ARMIC better understand patient activity during rehabilitation sessions. The goal isn't just automation. It's building systems that can observe, understand, and eventually adapt in real time.

One of the main reasons we migrated from the ESP32 to the Arduino UNO Q was the need to keep both AI inference and hardware coordination on the same edge device. Instead of relying on cloud execution, the goal is to enable: • local inference • real-time motor control • synchronized MCU MPU operation • adaptive robotic interaction This shifts ARMIC from rigid automation toward autonomous rehabilitation systems driven directly at the edge.

I just made a purchase from @dexscreener using @moonpay 🔥 moonpay.hel.io/pay/69f6ef916… Man of my word, DEX paid. See you the next update.

Thesis on Armic (@projectarmic): @projectarmic is a legitimate early-stage DeSci project building autonomous AI agents for patient recovery — AI that tracks, adapts, verifies, and rewards rehab progress, paired with robotic execution and on-chain incentives. The team is fully doxxed: biomedical engineers Luis Eduardo Arevalo Oliver (@EddOliver_), Alejandro Sanchez Gutierrez, and Victor Alonso Altamirano Izquierdo (@outlay_pay), with real experience in healthcare hardware, IoT, AI agents, and Web3 systems (prior hackathon work and roles at Blank!t). Launched transparently on Solana via @EasyA_Kickstart (in collaboration with @dom_kwok and @kwok_phil), with immediate supply locking via @Streamflow_Fi, fee allocation for hardware/open-sourcing, and a focus on real builds over hype. In a $30B rehab market crippled by poor adherence, this combination of AI autonomy, robotics, and tokenized incentives has genuine product-market fit potential. High execution risk (still pre-product), but the doxxed builder team, on-chain transparency, and narrative alignment (AI agents health DeSci) make it one of the more credible micro-funding experiments in the space right now.

Just for transparency, the money for fees WILL be used to improve the project and Open Source it. In term of better hardware, documentation and partnership with hardware companies. This is a purely DeSci and micro-funding initiative, of course inspired by @EasyA_Kickstart

Quiet before results. We didn’t spend this time optimizing optics. We built the next layer: → agents → Solana integration → real flows Outcome is one checkpoint. We’re playing a longer game. Still hoping for the best 😁 @EasyA_Kickstart

🔐 Just locked 1,391,172 $ARMIC tokens with @Streamflow_Fi No shortcuts. Long-term alignment > short-term extraction. Building for real systems, not hype. It's on-chain. You can check the amount, time-period and recipients. Check it out👇 app.streamflow.finance/contr…