Examples

All examples are in the public repository: github.com/teslasuit/RapidKit/tree/main/examples. Each example folder has its own README with run instructions.

Decision tree — which example should I read?#

Are you new to the framework?
├── YES → start with the atomic examples (one concept each).
│         See [Atomic examples](atomic.md) — 5 single-file demos
│         covering Steps 1, 2, 3, 4, and 6/7 of the
│         implementation guide.
│
└── NO → what does your application look like?

       Continuous joint-angle control / antagonist muscle pairs?
       └── [Elbow flexion](elbow_flexion.md) — PID control of
           biceps + triceps with auto-tuner and PyQt5 GUI.

       Haptic cues triggered from the keyboard?
       └── [Haptic navigation](haptic_navigation.md): discrete
           directional cues via `HapticLibrary`.

       Haptic cues driven by direction and distance?
       └── [Haptic proximity radar](haptic_proximity_radar.md):
           a draggable target selects the body zone (direction)
           and the intensity (distance).

       Balance biofeedback from body posture?
       └── [Vestibular training](vestibular_training.md): haptic
           cues on the side of the lean when postural sway leaves
           a stability boundary.

       Need a GUI scaffold without a real strategy?
       └── [Generic GUI](generic_gui.md) — reference PyQt5 template
           wired to the framework's `SharedRingBuffer`.

Catalog#

Atomic examples#

Five single-file demos under examples/atomic/. Each one is ~80 lines, covers exactly one framework concept, and runs as a standalone script:

These are the canonical reference for each step in the Implementation Guide.

Folder: examples/atomic/

Elbow flexion#

A complete PID-controlled FES application for elbow flexion. Uses agonist/antagonist muscle pairs (biceps for flexion, triceps for extension), an FFT-based PID auto-tuner, and a PyQt5 GUI with control sliders and real-time plots.

Read it when you need to do continuous joint-angle tracking — the strategy's structure transfers directly to any one-DOF FES application (knee, ankle, wrist).

Folder: examples/elbow_flexion/

Haptic navigation#

Demonstrates the HapticLibrary extension point. Arrow keys in a GUI fire haptic cues on different parts of the wearer's body — belly, back, shoulders. No closed-loop control logic; this is the canonical example of using EMS as tactile feedback rather than functional movement.

Read it when you need custom haptic patterns, named playable slots, or any non-functional use of EMS hardware.

Folder: examples/haptic_navigation/

Haptic proximity radar#

The continuous analogue of haptic navigation. A draggable target sits on a radar disc around the wearer: its direction selects which body zone fires (neighbouring zones blend on the diagonals) and its distance sets the intensity.

Read it when your cue needs to encode a continuous direction and magnitude rather than discrete on/off events.

Folder: examples/haptic_proximity_radar/

Vestibular training#

A balance rehabilitation concept using haptic biofeedback. Motion capture tracks postural sway in real time; when the trunk leaves a configurable stability boundary, the suit cues the side of the lean. This is a concept, not a clinically validated protocol.

Read it when you need sensor-driven haptic output (no EMS) from motion capture data.

Folder: examples/vestibular_training/

Generic GUI#

A reference PyQt5 GUI template using the framework's reusable widget toolkit (teslasuit_rapidkit.gui.app.FesApp, teslasuit_rapidkit.gui.data_adapter.DataAdapter, plus tab classes for overview and sensor data). Comes with a no-op DummyStrategy so you can see the GUI light up without any real control logic.

Read it when you're starting a new GUI-based application from scratch and want a clean template to copy.

Folder: examples/generic_gui/

How to run any example#

From the repo root, with the Teslasuit connected and Control Center running:

python -m examples.<name>.main
# e.g.
python -m examples.elbow_flexion.main
python -m examples.haptic_navigation.main
python -m examples.haptic_proximity_radar.main
python -m examples.vestibular_training.main
python -m examples.generic_gui.main

The GUI examples need the optional GUI dependencies (pip install -e ".[gui]").

Atomic examples run as direct scripts:

python examples/atomic/minimal_closed_loop.py

Press Ctrl-C to stop. The framework's unconditional cleanup ensures EMS is muted and mocap is stopped before the process exits.

Hardware requirements#

There is no mock / simulator path. If the suit isn't connected, SuitHandler construction will fail during engine initialisation. See Installation for the full hardware setup.