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Title mReasyDeck fEmgApe: Overview, Design, Applications, and Evaluation Abstract mReasyDeck fEmgApe is a hypothetical compact wearable platform combining modular sensor deck architecture (mReasyDeck) with fEmgApe — a focused electromyography (fEMG) acquisition and processing engine — for intuitive gesture recognition, prosthetic control, and human–computer interaction. This paper describes system architecture, hardware design, signal processing pipeline, machine-learning models, evaluation methodology, and potential applications, and identifies limitations and future work. 1. Introduction

Problem: Portable, low-cost, and modular sEMG systems often trade off between usability, signal quality, and on-device processing capability. Contribution: Present mReasyDeck fEmgApe, a modular wearable platform that streamlines sensor attachment, real-time fEMG acquisition, edge preprocessing, and customizable ML inference for gesture and prosthetic control.

2. System Architecture 2.1 Modular Deck Concept (mReasyDeck)

Stackable boards: sensor module, analog front-end (AFE), microcontroller/edge compute, power management, and wireless comms. Standardized connector and header pinout for easy swapping and expansion. Form factors: wristband, armband, adhesive patch. mreasydeck femgape new

2.2 fEmgApe Engine

Purpose-built firmware library providing:

Analog front-end configuration (gain, filters). Multi-channel synchronized sampling. Real-time preprocessing (buffering, notch/ bandpass filters). Feature extraction and lightweight ML inference. Communication API (BLE/USB/serial). System Architecture 2

3. Hardware Design

Sensors: Dry Ag/AgCl or conductive textile electrodes; 4–8 channels typical. AFE: Low-noise instrumentation amplifier, variable gain (40–80 dB), programmable high-pass (~10–20 Hz) and low-pass (~400–1000 Hz), notch filter (50/60 Hz). ADC: 12–16 bit, 1–2 kSPS per channel. MCU/Edge: Cortex-M4/M7 or tiny ML-capable MCU with 256 KB–1 MB flash and 192 KB–512 KB RAM; optional ML accelerators (e.g., Arm Ethos-U). Power: 3.7 V LiPo, 200–500 mAh; power budget optimized for multi-hour continuous use. Comms: Bluetooth Low Energy 5.x, optional Wi‑Fi or USB-C.

4. Signal Processing Pipeline

Electrode placement and skin preparation guidelines. Analog conditioning: prefiltering and amplification. ADC sampling with synchronized timestamps. Digital preprocessing:

Bandpass (20–450 Hz) to focus on EMG. Notch (50/60 Hz) adaptive removal. Rectification and smoothing (RMS or moving average).