| 98.2% packet retention |

Key Findings:

• Morse encoding reduces cross-modal translation to a fixed-state machine, cutting pipeline latency by ~70%.
• Hardware interrupt-driven decoding on the MCU eliminates OS scheduling jitter, maintaining <5% timing variance.
• Separating compute (SoC) from real-time I/O (MCU) reduces active power draw by 67% during idle/standby cycles.

Sweet Spot: 150ms end-to-end latency with adaptive WPM calibration (15–25 WPM) enables natural conversational pacing while preserving decoding accuracy above 95% across speech, text, and haptic channels.

Core Solution

The Unified Assistive Communication System (UACS) implements a dual-layer, event-driven architecture that treats communication as a single deterministic pipeline: Speech ⇄ Text ⇄ Morse ⇄ Haptic.

Architecture Split & Rationale

• Processing Layer (Raspberry Pi 4 Model B): Handles compute-intensive tasks including speech capture, STT/TTS inference, text normalization, and Morse encoding/decoding logic. Offloaded to prevent MCU resource exhaustion.
• Interaction Layer (ATmega328P): Manages timing-critical operations: SPDT switch interrupt capture, dot/dash classification, PWM haptic driver control, audio buzzer output, and UART Bluetooth bridging. Ensures real-time determinism independent of Linux OS variability.

This separation guarantees:

1. Deterministic timing for sensor input and haptic/audio output.
2. Clear boundary between compute-heavy inference and time-critical execution.
3. Portability: MCU unit functions as a compact HMI peripheral while heavy processing remains external.

Operational Flow

Speech → Haptic Output

1. Capture speech via microphone array
2. Convert to text using local STT pipeline
3. Encode text into Morse bitstream
4. Transmit via UART/Bluetooth to ATmega328P
5. Render as PWM-controlled vibration patterns on LRA

Haptic Input → Speech

1. User inputs Morse via SPDT switch
2. MCU decodes timing intervals into text characters
3. Text transmitted to Raspberry Pi
4. TTS engine generates audio output
5. Full-duplex communication loop established

Embedded Implementation Details

• Interrupt-driven Morse decoding: External interrupts on pin change capture press/release timestamps. Dot/dash classification uses fixed timing thresholds with adaptive WPM scaling.
• PWM-controlled haptics: Linear Resonant Actuator driven at resonant frequency (~200Hz) with duty-cycle modulation for intensity control.
• UART Bluetooth communication: HC-05 module configured for hardware flow control, packet framing, and ACK/NACK retry logic to prevent data loss.
• Battery optimization: Li-ion management with hardware-level protections: overcharge, deep discharge, short-circuit, and thermal cutoff. ATmega328P enters sleep mode between input events.

Software & Control Logic

Event-driven state machine governs the pipeline: Input Capture → Decode → State Transition → Output Scheduling

• Morse segmentation uses dynamic timing windows calibrated during initialization.
• Serial communication handles framing, checksum validation, and buffer management.
• Processing pipelines: Speech-to-Text → Text-to-Morse → Morse-to-Text → Text-to-Speech.

Hardware Stack: Raspberry Pi 4B, ATmega328P, HC-05 BT, LRA haptic motor, active buzzer, SPDT switch, Li-ion battery. Validation: Tested for low-latency throughput, decoding accuracy, BT stability, and consistent tactile/audio feedback under real-world conditions. Cost: ~₹16,700 (RPi is primary cost driver; remaining components optimized for affordability).

Pitfall Guide

1. OS-Level Timing Jitter: Running real-time Morse decoding on a general-purpose OS causes missed interrupts and variable latency. Best Practice: Offload all timing-critical I/O to a dedicated MCU with hardware interrupts and disable non-essential background services.
2. Inconsistent Morse Timing Windows: Fixed dot/dash thresholds break accuracy across users with different input speeds. Best Practice: Implement adaptive timing windows with a calibration phase that measures user WPM and dynamically adjusts segmentation thresholds.
3. Bluetooth Packet Loss in Real-Time Streams: HC-05/HC-08 modules drop frames under high baud rates or RF interference. Best Practice: Use UART with hardware flow control, implement ACK/NACK retry logic, and buffer Morse frames before transmission to prevent pipeline stalls.
4. Haptic Motor PWM Resonance Mismatch: Driving an LRA with arbitrary PWM frequencies causes inefficient vibration, heat buildup, or motor damage. Best Practice: Match PWM frequency to the LRA's mechanical resonant frequency (~200Hz) and use closed-loop current sensing for consistent tactile feedback.
5. Battery Drain from Continuous Audio/Bluetooth: Active buzzer + Bluetooth + SoC inference rapidly depletes Li-ion cells. Best Practice: Implement hardware sleep states on the ATmega328P, use power gating for unused peripherals, and trigger RPi STT/TTS only on Voice Activity Detection (VAD) events.
6. State Machine Deadlocks: Event-driven pipelines can hang if transitions lack exhaustive timeout handling. Best Practice: Define explicit idle/timeout states, implement a hardware watchdog timer, and ensure all state transitions have fallback paths to prevent communication loop paralysis.

Deliverables

• Unified Architecture Blueprint: Complete system diagram detailing SoC/MCU boundary, data flow, and hardware protection circuits.
• Hardware Protection & Power Management Checklist: Verification matrix for overcharge, deep discharge, short-circuit, thermal cutoff, and sleep-state validation.
• ATmega328P Firmware Configuration Templates: Pre-configured timing windows, PWM profiles, UART baud rates, and interrupt handler scaffolding.
• Raspberry Pi Pipeline Setup Guide: STT/TTS integration scripts, Bluetooth pairing automation, and Morse encoding/decoding module configuration.
• Validation Test Suite: Benchmark scripts for latency measurement, decoding accuracy tracking, Bluetooth packet retention analysis, and battery drain profiling.
• Project Resources: Official Project Page | GitHub Repository