Feature extraction and classification of bio-signals are crucial in human-machine interface (HMI), yet suffer from high delay and limited energy efficiency using conventional hardware. To mitigate this challenge, we propose an SDISC architecture, a neuromorphic HMI with the innovation from signal encoding, computing-in-memory (CIM) hardware, to algorithm-hardware co-optimization. The following strategies are implemented: (1) A spike-driven feature extractor, achieving > $10 \times$ sparser dataflow than frame-based method; (2) In-situ computing based on resistive random-access memory (RRAM), enabling energy-efficient (4.09 TOPS/W) spiking neural network (SNN) classifier; (3) A Spike-Activity-Distillation algorithm and an Aid-Loser-Only recovery scheme to alleviate the non-ideality of RRAM devices, ensuring SDISC maintains high accuracy ($\sim \mathbf{9 8. 0 \%}$) in long time inference ($\boldsymbol{\gt} \mathbf{1 5}$ days). We further develop an end-to-end SDISC system for real-time EMG-based robot control, achieving a low latency ($34 \mu \mathrm{~s}$) and low power ($39.72 \mu \mathrm{~W} /$ sample) interaction on edge.