A 52-year-old paralyzed woman controls robots that can help her with day-to-day tasks.
Paralyzed from the neck down, the woman is able to command the robotic arm to carry out complex tasks including but not limited to pouring, moving and stacking cups and feeding her beverages. The experiments were conducted with the patient, Jan, at the University of Pittsburgh where she manipulated the mind-controlled arm with ease. According to researchers from The Lancet, swift movements were accomplished by Jan with only two days of practice and within two weeks, she was able to command the bot to bring items to her from distant places. The design, which many researchers are beginning to use, is to program these automatons so that they are more intuitive, allowing users to instruct them to do an overall task rather than directing them every step of the way.
What Makes This Trend Stand Out
- Intuitive Robotic Assistance
- The trend of developing intuitive robotic assistance allows paralyzed individuals to easily command and control robots to perform complex tasks.
- Mind-controlled Robotics
- The trend of utilizing mind-control technology in robotics enables paralyzed individuals to manipulate robotic arms and perform day-to-day tasks with ease.
- Intuitive Programming
- The trend of intuitive programming in robots empowers users to give high-level instructions, allowing the robots to complete overall tasks without detailed step-by-step directions.
Sectors Adopting This
- Medical Robotics
- The medical robotics industry can capitalize on the trend of intuitive robotic assistance to develop advanced tools and devices that improve the quality of life for individuals with physical disabilities.
- Neurotechnology
- The neurotechnology industry has the opportunity to further advance mind-controlled robotics and create innovative solutions for individuals with paralysis.
- Artificial Intelligence
- The artificial intelligence industry can play a significant role in the trend of intuitive programming by developing smart algorithms and machine learning techniques to enhance robotic autonomy and understanding of high-level instructions.
