Ankle Assist Exosuit Walking Performance

ISEF Category: Engineering Technology: Statics and Dynamics

Subcategory: Other  ·  Difficulty: Advanced  ·  Setup: University Lab  ·  Time: Full Year

The Hook

Walking uphill costs more than walking on level ground. Your body has to do more work at the ankle, and that makes the ankle a smart place to help. A small exosuit can add force at the right moment, like a well-timed push on a swing. Your job is to test whether that help really changes gait and effort.

What Is It?

This project studies an ankle-assist exosuit, which is a wearable robot that helps your ankle move during walking. A brushless motor pulls a Bowden cable, which is a flexible cable inside a sheath that can transmit force from one place to another. An inertial measurement unit, or IMU, tracks motion, and EMG, short for electromyography, tracks muscle activity. Together, these sensors let you see when the device helps and how your body responds.

Think of it like power steering for walking. The motor does part of the job, but only when the timing is right. If the assist comes too early or too late, it can fight your natural stride instead of helping it. You can compare flat walking and incline walking to see when ankle support matters most and whether the device changes step cadence, muscle effort, or a heart-rate variability proxy for effort.

Why This Is a Good Topic

This is a strong science fair topic because you can test a clear engineering question, measure real human movement, and compare a device to a no-assist condition. It connects to rehabilitation, mobility assistance, sports performance, and fatigue reduction. You can learn sensor integration, control timing, data cleaning, and basic biomechanics. A student with good planning can make a real prototype and collect meaningful data.

Research Questions

• How does ankle-assist timing affect step cadence during flat and incline walking?
• What is the effect of different assist levels on EMG amplitude in the calf muscles?
• Does Bowden-cable tension change the consistency of ankle torque delivery across strides?
• To what extent does the exosuit change heart-rate variability during incline walking compared with no assist?
• Which gait phase produces the largest change in ankle motion when the exosuit is active?
• How does IMU-based step detection compare with manual cadence counts for walking trials?

Basic Materials

• Brushless DC motor with controller
• Bowden cable and sheath
• IMU sensor module
• MyoWare EMG sensor kit
• Heart-rate monitor with data export
• Rechargeable battery pack
• Microcontroller board such as Arduino or similar
• Flexible ankle brace or lightweight wearable frame
• Laptop for data collection
• Elastic straps and fasteners
• Basic hand tools
• Safety glasses.

Advanced Materials

• Custom 3D-printed or machined brace components
• Torque sensor or inline load cell for cable force measurement
• Motion-capture markers or video-based gait tracking system
• Force plate access for ground reaction data
• Multiple EMG channels for tibialis anterior and gastrocnemius
• Higher-bandwidth motor driver and encoder feedback
• Data acquisition system with synchronized timestamps
• Calibrated treadmill with incline control
• Spare motor and cable assemblies for repeat trials
• Biomechanics analysis software.

Software & Tools

• Python: Cleans sensor data, synchronizes signals, and runs statistics on gait and EMG measures.
• ImageJ: Measures motion or video-based joint changes if you record walking trials on camera.
• Arduino IDE: Programs the microcontroller that reads the IMU, EMG, and motor signals.
• Excel: Organizes trial data and makes first-pass graphs and summary tables.
• JASP: Runs t tests, repeated-measures tests, and effect size calculations without paid software.

Experiment Steps

1. Define the walking outcome you want to improve, such as cadence, muscle activity, or effort proxy.
2. Choose one control variable at a time, such as assist timing, assist level, or incline angle.
3. Design a no-assist baseline and a matched assist condition so you can compare the device fairly.
4. Plan how you will synchronize IMU, EMG, heart-rate, and cadence data across each trial.
5. Build a calibration plan that converts raw sensor output into usable gait metrics.
6. Set up a repeat-trial structure that lets you test whether changes hold across multiple walkers or multiple runs.

Common Pitfalls

• Letting the Bowden cable rub or bind, which changes force delivery and makes the assist feel inconsistent.
• Using EMG electrodes with poor skin contact, which adds noise and hides real muscle changes.
• Comparing flat and incline trials without matching pace, which mixes speed effects with assist effects.
• Trusting heart-rate variability as a direct metabolic measure, which can overstate what the proxy actually shows.
• Changing assist timing and assist strength at the same time, which makes it hard to tell which design choice caused the result.

What Makes This Competitive

A competitive version goes past a simple before-and-after demo. You would compare multiple assist strategies, control for walking speed, and use synchronized data streams to connect device timing with human response. Strong projects often include a clean null condition, repeated trials, and effect sizes, not just averages. The best entries ask a narrow question and answer it with careful biomechanics and signal analysis.

Project Variations

• Test how ankle assist changes uphill walking versus stair climbing, then compare the two movement patterns.
• Swap EMG placement between the tibialis anterior and gastrocnemius to see which muscle responds more to assist timing.
• Compare open-loop assist timing with IMU-triggered assist timing to see which one matches the gait cycle better.

Learn More

• NIH PubMed: Search review articles on ankle exosuits, gait assistance, EMG analysis, and wearable robotics.
• NASA ImageJ guide: Find tutorials for measuring motion and tracking features from walking videos.
• MIT OpenCourseWare, biomechanics and robotics courses: Look for lecture notes on gait, control systems, and wearable robots.
• NOAA National Centers for Environmental Information: Use background resources on human activity monitoring methods and data handling basics.
• Journal of NeuroEngineering and Rehabilitation: Search for peer-reviewed studies on wearable assist devices and human gait.