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How To Measure Joint Angle With Stretch Sensors



Stretch sensors are small, stretchy capacitors. They are lightweight, soft, flexible and can be sewn into garments, making them well suited for use on the human body. Sewing stretch sensors into form-fitting clothes allows us the ability to visualize and record human body movement, enabling new kinds smart clothing. In this case study, we’ll look at how we can do just that! If you need some more information about how they work, you can read our eGuide: How to use a fabric sensor.

Using Stretch Sensors To Track Movement

When we want to use sensors to track, record or visualize body movement, the process often involves measuring the angle of joints. It may seem obvious, but joints are areas that have a lot of movement, and it’s the movement of the body that we’re interested in for wearables!

The Process Works Like This

We correlate a real world quantity to capacitance data we gather from our sensor by using a mathematical ‘curve fit’ equation. This process is called characterization. Let’s look at a fabric stretch sensor sewn onto the elbow region of a long sleeve compression garment as an example. In this case, the real world quantity will be angle values taken at different stages of the arm bending. The sensor data will be capacitance values taken at those same positions. With these two datasets, we can establish a mutual relationship between the movement of the joint and the movement of a sensor (or sensors) placed on that joint.

Checklist To Getting Started

  1. Fabric stretch sensors
  2. Needle and thread
  3. Close fitting garment or sleeve to sew the sensors in (compression shirts are excellent)
  4. Test person
  5. Extra coin cell batteries
  6. iOS or Android device with the StretchSense Fabric Sensor Evaluation App
  7. For a step by step guide to using the StretchSense iOS App refer to our eGuide

Guide To Measuring Joint Angle With Fabric Stretch Sensors

Make sure the sensor is sewed into the correct location, stretching over the elbow and not moving out of place when the arm moves. If the sensor is correctly mounted, bending the arm will cause the sensor to extend in length and increase in capacitance. When the arm is straight, the sensor should be in its resting state. The arm at full bend should be the point where the sensor is at the maximum extension, which will also be the sensors maximum capacitance value.

To begin, with the arm straight (0 degrees), record the sensor data’s capacitance. We recorded a capacitance value of 205 pF at this position.

stretch sensors, smart garment, fabric sensors, stretchsense, smart clothing, joint angle measurements

Next, bend the arm at 5-degree increments and record the capacitance values given by the sensor at each of these points. We drew out 5-degree angle lines on a piece of paper using a protractor to measure the angle of the arm. Taking your measurements while bending and straightening the arm is needed to get accurate results.

Our Two Data Sets

Data Processing

To visualize our table data in graph, we start by creating a scatter plot in Excel. We then created a trend line using a second order polynomial for our curve fit. We now have a relationship between joint angle and capacitance. Using this relationship we are now able to see what angle the arm would be bending at just by looking at sensor data and vice versa!

What Are Some Challenges You May Face?

All real sensing systems like the application we just made have imperfections resulting in some error. In the above graph, you can clearly see the presence of noise in the data. The impact of noise is that it will reduce the overall accuracy of the application. Noise can be caused by any or every part of the system. The sensor, the garment, the electronics, the App, even the protractor setup used to get the reference data in the first place will all contribute some loss of accuracy. Make sure you cover all your bases when debugging.

Another thing you can see from the graph is that the response is non-linear. We expect this – stretch sensors are linear when stretched uniaxially in free space, but when mounted on the body other factors come into play. When thinking our elbow application, the elbow is applying pressure to the sensor and the garment is pulling at the width of the sensor which complicates the relationship. The good thing is, mild non-linearity like this doesn’t really matter as long as the response is repeatable. With repeatability and a good characterisation curve, you can get a reliable measurement of the joint. We can test for repeatability by doing multiple cycles, testing the sensor again over time, and subjecting it to changing environmental conditions.

One of the most challenging issues R&D teams face when creating wearables is mounting the sensors, so they get repeatable results every time. If you want a sensor to give us information about the position of the elbow we have to make sure the sensor stays in the correct position. If the compression garment doesn’t fit well causing the sensor to move out of position, you won’t get accurate results. Adding structure to the garment that prevents the sensors moving out of place can be helpful. Adding more sensors can also help. With a larger number sensors, you have some redundancy, meaning if the garment rotates there will still be sensors that can give us a reading and the correct data.

The best way forward is to prototype fast and early. Set a clear goal for the overall specifications and performance of your sensing system and then iteratively improve until you hit it. If you want any help, just let us know.

Creating Your Own App

You can use this characterization method to create your App to visualize or record joint movement. The StretchSense Fabric Evaluation Apps are a great way to test and prototype this process but when you’re ready to build a custom App we also have two API Libraries to help simplify the process. The APIs demonstrate how to establish a connection between a one or more StretchSense Fabric Evaluation circuits and a Bluetooth® low energy enabled Android or iOS device. If you or your team would like to experiment with our API Libraries, just fork the repository of your choice here and check out the documentation.

It Doesn’t Just Have To Be Joints!

We could use this same process to track other kinds of body movements! For example; the motion of the chest expanding and contracting as we breath in and out, muscles in the arms or legs expanding and contracting as we lift weights, the shoulder blades moving as we adjust our posture or the torso twisting as we rotate our body. The only limit is your imagination! If you have any unique ideas on types of body motion you would like to visualize or record using our sensor technology you can get in touch here.

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