Armic

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.

We also documented the architectural transition behind ARMIC and why local Edge AI changes everything for rehabilitation robotics: #discussion-10148684" target="_blank" rel="nofollow noopener">github.com/altaga/Armic-S…

Foundation phase update 🛠️ We’ve started validating the Edge AI architecture behind ARMIC using the Arduino UNO Q + local SmolLM2 inference. Current setup: → llama.cpp running locally → MCU + MPU synchronization → Local WebUI → Real-time peripheral coordination → On-device AI execution This is one of the first steps toward an autonomous rehabilitation system capable of real-time robotic interaction directly at the edge. Walkthrough below 🤖

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.

@Hrknsz Yeah, the micros and the sensors are much more expensive...for now. BFArm incoming.

@projectarmic Bruh this shi is $69.95 on amazon 😭

From onchain → to real hardware. Used part of the project fees to start expanding the physical AI side of ARMIC. Arduino setup just arrived. Robotic arm next. We’re interested in building systems that don’t just exist on charts, but interact with the real world 🤖

The project has grown, the market has spoken so we have to deliver now. - Will be updating more than Bi-weekly. - Documenting everything - All open-source In addition to this Blue checkmark ✅️

ARMIC Roadmap (Next 3 Months) 🛠️🤖 Phase 1 — Foundation • Expand hardware stack (Arduino + robotics) • Improve agent execution flows • Refine onchain payment logic • Continue open development updates Phase 2 — Physical AI • Integrate robotic arm interactions • Connect autonomous actions with onchain triggers • Test machine-to-machine coordination flows • Begin real-world automation demos Phase 3 — Deployment Layer • Expand Solana infrastructure • Improve interoperability • Launch more public demos • Move toward continuous real-world operation We’re building toward systems where AI agents, robotics, and programmable finance converge.

Arduino Q for the uninitiated. Pretty much a microcontroller with AI capabilities. Ordered quite a few.

@drixtoshii @flypraxis Next week roadmap and update the week after.

@projectarmic @flypraxis When’s the roadmap and bi-weekly update comin?

We didn’t win. We’re still building. The fees from $ARMIC are going into hardware, platform development, and open-sourcing the system. We’ll also enable DEX liquidity so it becomes a community token. 3-month roadmap. Biweekly updates. This doesn’t stop here.

@SmartyCrypto195 @flypraxis @EasyA_Kickstart That was never the focus, nor the intention, nor the thesis for the project. Tg was heavily spammed by scammers, and from the beginning we communicated what the project was all about. But to each his own.

@projectarmic @flypraxis @EasyA_Kickstart Bro you locked 1mil tokens and if you are not new to crypto you know how that works. You made a tg where you never answered. You didn't make enough money so that's how you are treating this project. No one buy a shady project where a team goes mia

@SmartyCrypto195 @flypraxis @EasyA_Kickstart Not gone, you might want to read the last post. And pfff not even 2k in fees dude.

@projectarmic @flypraxis @EasyA_Kickstart please expose these scammers who just wanted to win 10k from hackathon and after that they are totally gone

I just made a purchase from @dexscreener using @moonpay 🔥 moonpay.hel.io/pay/69f6ef9164… Man of my word, DEX paid. See you the next update.

@EasyA_Kickstart @toly @flypraxis @consensus2026 Thanks guys, and congratulations @flypraxis

we've been crazy impressed by the founders who entered into our kickstart hackathon 🤯 we even had founders from @toly's university apply. announcing the winner; @flypraxis! the winner will pitch investors at the superbowl of blockchain in miami, @consensus2026. additionally, we're buying $10,000 of $PRX on the open market. this is just the beginning.

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