Abstract
Robotic orthoses have the potential to provide effective rehabilitation while overcoming the availability and cost constraints of therapists. These orthoses must be characterized by the naturally safe, reliable, and controlled motion of a human therapist's muscles. Such characteristics are only possible in the natural kingdom through the pain sensing realized by the interaction of an intelligent nervous system and muscles' embedded sensing organs.
McKibben fluidic muscles or pneumatic muscle actuators (PMAs) are a popular orthosis actuator because of their inherent compliance, high force, and muscle-like load-displacement characteristics. However, the circular cross-section of PMA increases their profile. PMA are also notoriously unreliable and difficult to control, lacking the intelligent pain sensing systems of their biological muscle counterparts.
Here the Peano fluidic muscle, a new low profile yet high-force soft actuator is introduced. This muscle is smart, featuring bioinspired embedded pressure and soft capacitive strain sensors. Given this pressure and strain feedback, experimental validation shows that a lumped parameter model based on the muscle geometry and material parameters can be used to predict its force for quasistatic motion with an average error of 10 - 15N. Combining this with a force threshold pain sensing algorithm sets a precedent for flexible orthosis actuation that uses embedded sensors to prevent damage to the actuator and its environment.
McKibben fluidic muscles or pneumatic muscle actuators (PMAs) are a popular orthosis actuator because of their inherent compliance, high force, and muscle-like load-displacement characteristics. However, the circular cross-section of PMA increases their profile. PMA are also notoriously unreliable and difficult to control, lacking the intelligent pain sensing systems of their biological muscle counterparts.
Here the Peano fluidic muscle, a new low profile yet high-force soft actuator is introduced. This muscle is smart, featuring bioinspired embedded pressure and soft capacitive strain sensors. Given this pressure and strain feedback, experimental validation shows that a lumped parameter model based on the muscle geometry and material parameters can be used to predict its force for quasistatic motion with an average error of 10 - 15N. Combining this with a force threshold pain sensing algorithm sets a precedent for flexible orthosis actuation that uses embedded sensors to prevent damage to the actuator and its environment.
| Original language | English |
|---|---|
| Title of host publication | Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2015 |
| Editors | Jerome P. Lynch |
| Publisher | SPIE |
| Pages | 1-11 |
| Number of pages | 11 |
| Volume | 9435 |
| ISBN (Print) | 9781628415384 |
| DOIs | |
| Publication status | Published - 27 Mar 2015 |
Keywords
- Peano
- fluidic muscle
- soft actuator
- McKibben
- artificial muscle
- pain sensing
- embedded sensing
- orthosis