Robotic System Automates Chronic Pain Therapy
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By HospiMedica International staff writers Posted on 20 Jun 2019 |
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The PBM cobot applies targeted laser therapy to pain hot spots (Photo courtesy of Swinburne Institute of Technology).
A new collaborative robot system can automatically treat back, neck, and head chronic pain caused by soft tissue injury.
Developed at the Swinburne Institute of Technology (Melbourne, Australia), in collaboration with partner IR Robotics(Melbourne, Australia), the automated photobiomodulation (PBM) collaborative robot (cobot) is a fully working prototype that can treat chronic pain by applying targeted low-level laser light to the surface of the body so as to stimulate and heal soft tissues. To identify the pain ‘hot spots’, the system takes advantage of a thermal camera, and subsequently projects targeted laser therapy to relieve pain and inflammation.
Unlike conventional industrial robots that operate in a cage, cobots are designed to work alongside humans in an uncaged environment. To do so, they incorporate multiple advanced sensors, software, and end-of-arm-tooling (EOAT) guides that help them swiftly and easily adapt to any sort of intrusion in the work envelope. They are also power- and speed-limited, so even if they collide with people, they do not cause harm. Cobots can be programmed to respond immediately by stopping or reversing positions when coming into human contact.
“Using the same technology used in cricket to show whether the ball has made contact with the bat, a thermal camera scans the patient and locates injuries and inflammation by identifying hot spots in a thermal image,” said Mats Isaksson, PhD, of the Swinburne Institute of Technology. “The location of the hot spot is then sent to the collaborative robot that moves a low-level infrared laser into contact with the patient to perform treatment.”
“Studies have shown PBM therapy to have positive effects on chronic pain symptoms,” said Mark Rogers, PhD, co-founder of IR Robotics. “Introducing collaborative robots to deliver treatment has the potential to improve the precision of the therapy in addition to reducing costs involved. Building on Industry 4.0 technologies and big data analysis, the derived platform can self-adapt to provide individually optimal treatment.”
Related Links:
Swinburne Institute of Technology
Developed at the Swinburne Institute of Technology (Melbourne, Australia), in collaboration with partner IR Robotics(Melbourne, Australia), the automated photobiomodulation (PBM) collaborative robot (cobot) is a fully working prototype that can treat chronic pain by applying targeted low-level laser light to the surface of the body so as to stimulate and heal soft tissues. To identify the pain ‘hot spots’, the system takes advantage of a thermal camera, and subsequently projects targeted laser therapy to relieve pain and inflammation.
Unlike conventional industrial robots that operate in a cage, cobots are designed to work alongside humans in an uncaged environment. To do so, they incorporate multiple advanced sensors, software, and end-of-arm-tooling (EOAT) guides that help them swiftly and easily adapt to any sort of intrusion in the work envelope. They are also power- and speed-limited, so even if they collide with people, they do not cause harm. Cobots can be programmed to respond immediately by stopping or reversing positions when coming into human contact.
“Using the same technology used in cricket to show whether the ball has made contact with the bat, a thermal camera scans the patient and locates injuries and inflammation by identifying hot spots in a thermal image,” said Mats Isaksson, PhD, of the Swinburne Institute of Technology. “The location of the hot spot is then sent to the collaborative robot that moves a low-level infrared laser into contact with the patient to perform treatment.”
“Studies have shown PBM therapy to have positive effects on chronic pain symptoms,” said Mark Rogers, PhD, co-founder of IR Robotics. “Introducing collaborative robots to deliver treatment has the potential to improve the precision of the therapy in addition to reducing costs involved. Building on Industry 4.0 technologies and big data analysis, the derived platform can self-adapt to provide individually optimal treatment.”
Related Links:
Swinburne Institute of Technology
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