Micro-sensor-enabled smart materials: Get to know proprioceptive materials - Small Times
This article is from

Micro-sensor-enabled smart materials: Get to know proprioceptive materials

Dominique Vicard and Nathalie Sprynski, CEA-Leti, France

For more than a decade, CEA-Leti has been developing microsensors capable of precisely measuring their orientation in relation to the Earth's gravitational and magnetic fields. Now, Leti is beginning to incorporate arrays of these tiny micro sensors into new kinds of instrumented -- or proprioceptive -- materials.

The goal of this embedded micro sensor initiative is to capture detailed information about the shape, position and motion of objects fabricated out of these new sensor-laden smart materials, and then use mathematical algorithms to analyze that data and apply it to real-world problems.

Potential applications for proprioceptive materials include:

  • Medicine, where they could help determine the shape and curvature of patients' spinal columns, or define the best shape for medical belts or wound dressings;
  • Aviation and auto design, where flexible, sensor-equipped ribbons could be used to measure turbulence in the wake of fast-moving aircraft or cars;
  • Computer-aided design (CAD), where applying smart-material wrappers to real objects could generate data for creating detailed numerical models, possibly taking the place of 3D scanners;
  • Sports equipment, where the performance of sails, surfboards, skis or other gear could be monitored, evaluated and improved;
  • Games and virtual reality, where smart clothes could capture players' complete body moves, allowing them to be totally integrated into the game.

Figure 1. CEA-Leti's prototype Morphosense tool -- embedded with micro sensors -- is shown superimposed over a virtual ribbon generated by the system.

To explore the capabilities of proprioceptive materials, Leti researchers have created a ribbon-like prototype called Morphosense, which allows users to remotely capture information about curved shapes in space (Figure 1). Built from a flexible, plastic-coated printed circuit board (PCB) with 16 coupled sensors distributed along the ribbon at 25mm intervals, the system uses a serial-peripheral-interface (SPI) bus to read the sensors' position data, which is then transmitted wirelessly to a host computer using Bluetooth 2.0 technology.

Two types of sensors are used in tandem to determine the orientation of each point along the Morphosense ribbon. Micro-accelerometer sensors provide data on the ribbon's orientation to the Earth's gravitational field as well as acceleration data when the sensor is moving. Micro-magnetometer sensors provide data on the ribbon's orientation to the Earth's magnetic field. By embedding both kinds of sensors at each location and combining their orientation information, it is possible to determine the absolute orientation of known points along the ribbon's surface, which can then be used to generate a mathematical representation of its shape.

The algorithm used to reconstruct the ribbon's curves requires knowledge of tangential data at each sensor's position, and the distance between sensors along the ribbon. Based on that, the derivative function can be determined using the arc-length parameter and cubic splines on the unit sphere, and then integrating the three components to retrieve the curve.

Morphosense has enabled Leti to validate the use of proprioceptive materials for both shape capture and motion capture. We still have much to learn about the capabilities and potential uses of embedded micro-sensors, but the technology is looking very promising.

Figure 2. The shape of more-complex surfaces can be acquired using multiple sensor ribbons in a comb structure.

Having proven the concept of capturing relatively simple shapes and motion from ribbons, Leti is turning its attention to more complex three-dimensional surfaces. One promising approach is to embed sensors in either a rectangular mesh, or a comb structure (Figure 2). By reconstructing the curves independently, then adjusting them according to their distribution (based on known information about the system), it should be possible to reconstruct a smooth surface fitting the curves.

Looking ahead, Leti researchers expect to continue miniaturizing these sensors, as well as integrating them into a wider variety of plastic and textile smart materials. Better algorithms should also help improve the interpretation of sensor data and allow the development of more accurate surface models.

Dominique Vicard is Leti's lab manager for sensors, functionalization and environment. Contact Vicard at +; [email protected].

Nathalie Sprynski is a research engineer in mathematics.
Follow Small Times on Twitter.com by clicking www.twitter.com/smalltimes. Or join our Facebook group