The United States population has never been older. The median age of the United States is 38.9 years old, nearly a decade more than in 1980. The number of adults over 65 is expected grow from 58 to 82 millions by 2050. Care for the elderly is a growing social issue, especially in light of shortages of care workers, rising costs and changing family structures.
A team of MIT engineers are looking to robotics to help them address the eldercare challenges. They have tested the Elderly Bodily Assistance Robot (E-BAR), a mobile robot that is designed to support the elderly physically and prevent them from tripping as they move about their homes.
EBAR is a set robotic handlebars which follows a person behind. The robot arms can be used to support the user or they can walk independently. The robot can lift the user from a sitting position to a standing position in a natural trajectory. The robot’s arms can help them by inflating side-airbags quickly if they start to fall.
The researchers hope that their design will prevent falls which are today the leading cause of injury for adults 65 and older. Harry Asada is the Ford Professor of Engineering and MIT. “Our design concept is to provide older adults having balance impairment with robotic handlebars for stabilizing their body. The handlebars go anywhere and provide support anytime, whenever they need.”
The robot is controlled by remote control in its current version. In future versions, the team will automate a large part of the bot’s functions, allowing it to follow and assist a person physically. Researchers are also working to streamline the device so that it is slimmer and easier to maneuver in small spaces.
“I think eldercare is the next great challenge,” E-BAR designer Roberto Bolli is a graduate student at the MIT Department of Mechanical Engineering. Bolli and Asada are presenting a paper on the design of EBAR at the IEEE Conference on Robotics and Automation, ICRA, later this month.
Home Support
Asada’s group at MIT has developed a variety technologies and robotic aides for the elderly. Others have developed fall-prediction algorithms, designed robots, self-inflating wearable airbags and robotic frames which secure a person and move with them while they walk.
Asada & Bolli designed E-BAR to do three things: provide physical support, prevent falls, and move safely and unobtrusively with a user. They also wanted to eliminate any harness to give the user more mobility and independence.
“Elderly people overwhelmingly do not like to wear harnesses or assistive devices,” Bolli says. “The idea behind the E-BAR structure is, it provides body weight support, active assistance with gait, and fall catching while also being completely unobstructed in the front. You can just get out anytime.”
They wanted to design a robot that would help older adults age in place at home, or in care facilities. In their interviews with older adults, the team came up with a list of design requirements. These included that the robot had to fit through doors in the home, allow users to take a full step, and support the full weight of the user. This would help with balance, posture and transitions between sitting and standing.
This robot is made up of a 220-pound base, whose dimensions and construction were optimized to support an average human’s weight without tipping or sliding. A set of omnidirectional casters is located beneath the base, which allows the robot to move any direction it needs without having to pivot. Imagine a car’s wheels shifting in order to slide between two other vehicles, without parallel parking.
An articulated body, made of 18 interconnected bars or linkages, extends out from the robot base. It can be reconfigured like a folding crane to lift a human from a seated to a standing position and vice versa. Two arms with handlebars extend out from the robot, forming a U shape. A person can lean on these arms if they require additional support. Each arm of the robot has airbags embedded in it. They are made from a soft, yet grippable, material that can be inflated instantly to catch someone if they fall without bruising them. Researchers believe that E-BAR can catch a person falling without the use of wearable devices or harnesses.
The researchers tested the robot with an older adult in the lab. She volunteered to use it in different household scenarios. The team found E-BAR actively supported the person when they bent down to lift something from the floor and stretched up to grab an object off of a shelf – tasks that can be difficult to do while maintaining their balance. The robot was also able to lift a person up and over a lip of a bath, simulating a task such as getting out of the tub. Bolli envisions that a design similar to E-BAR could be used in the home for elderly people with moderate muscle strength who require assistive devices. Bolli says
“Seeing the technology used in real-life scenarios is really exciting,” .
The researchers have not included any fall-prediction capability in the E-BAR airbag system. Another project in Asada’s lab, led my graduate student Emily Kamienski focused on developing machine learning algorithms to control a robot in response to a user’s real time fall risk level.
Asada believes that different technologies in his laboratory can provide different levels of assistance to people at different phases of their lives or mobility.
“Eldercare conditions can change every few weeks or months,” Asada says. This work was partially supported by the National Robotics Initiative, and the National Science Foundation.
