A European consortium of research institutes, universities and technology companies has developed a highly customizable robot companion to help seniors to maintain their quality of life, stay healthy and avoid social exclusion.
The robot, a mobile wheeled semi-humanoid figure equipped with cameras, sensors, audio, and a touch screen interface, can remind users to take their medicine, suggest they have their favorite drink, or prompt them to go for a walk or visit friends if they haven’t been out for a while. As part of a larger smart-home environment that can include smart clothing to monitor vital signs, the system can monitor user’s health and safety, and alert emergency services if necessary.
DARPA continues to build technology with academic partners to enable amputees to control prosthetic limbs with their minds. Examples follow:
Researchers at the Rehabilitation Institute of Chicago demonstrated a type of peripheral interface called targeted muscle re-innervation (TMR). By rewiring nerves from amputated limbs, new interfaces allow for prosthetic control with existing muscles.
Researchers at Case Western Reserve University used a flat interface nerve electrode (FINE) to demonstrate direct sensory feedback. By interfacing with residual nerves in the patient’s partial limb, some sense of touch by the fingers is restored. Other existing prosthetic limb control systems rely solely on visual feedback. Unlike visual feedback, direct sensory feedback allows patients to move a hand without keeping their eyes on it—enabling simple tasks, like searching a bag for small items, not possible with today’s prosthetics.
Cornell University researchers have programmed a PR-2 robot to not only carry out everyday tasks, but to anticipate human behavior and adjust its actions.
From a database of 120 3-D videos of people performing common household activities, the robot has been trained to identify human activities by tracking the movements of the body – reduced to a symbolic skeleton for easy calculation – breaking them down into sub-activities like reaching, carrying, pouring or drinking, and to associate the activities with objects.
Observing a new scene with its Microsoft Kinnect 3-D camera, the robot identifies the activities it sees, considers what uses are possible with the objects in the scene and how those uses fit with the activities; it then generates a set of possible continuations into the future – such as eating, drinking, cleaning, putting away – and finally chooses the most probable. As the action continues, it constantly updates and refines its predictions.