Eriksson’s paper discusses an autonomous assistive mobile robot that aids stroke patient rehabilitation by providing monitoring, encouragement, and reminders. This is an interesting piece of the assistive robot research because social interaction is a key component to having the elderly accept robots as helpers and not as hindrances.  The robots actually encourage the patient to use the disabled arm and with a monitoring process, the robot would actually provide support and motivation when regular physical therapy is not suitable or available. The robot must act as a friendly companion during this time because the healing process for the patient can be very difficult.

One key note in this research is that the elderly patients viewed the robots as companions like that of a pet. This is important because the robot does not have to contain human features to be accepted by the majority of the population. Research has shown that pet ownership increases the health and emotional well-being of the elderly patients. The actual design of the robot implemented a behavior-based system in which the conditions were designed to properly coordinate behavior execution in response to real-time feedback from the sensors. Another interesting piece of this research is that the research was done with the Pioneer robots with the Player robot device server which is the same environment that current experiments are being done on in the lab.

In my research, I have come across many papers in which the human interacting with the robot significantly prefers the user of recorded human voice to synthesize ones as this allows the user to be more accepting of the robot as a companion than as a robot with a computer based voice. In one of the cases, a happy pre-recorded voice and encouraging movements made the patient interested and inspired to perform the activity well. Another result in the testing deals specifically with the length of the experiments, where the experiments needed to be terminated before the patient had stopped doing the activity, thus preventing the collection of reliable compliance data and possibly further underestimating the compliance boost. One side effect of the monitoring by the robot is the inevitable situation where the user will try to fool the robot into thinking that the requested activity was being performed. Even with the patient tricking the robot, they are still exercising their arm and so the robot still completes its purpose.

In the coming years, home rehabilitation and elderly services will be a huge market. Another area to understand is that those retired did not grow up in the information age of computers and therefore will be less accepting of technological solutions to their problems. It is an important area to understand social interactions and how to better enable the robots to simulate empathy with the patient. If the elderly can accept these type of assistive robots than they will heal faster and live a much healthier retirement.

Reference:

  1. Eriksson, Jon. “Hands-off Assistive Robotics for Post-Stroke Arm Rehabilitation.” <http://robotics.usc.edu/publications/media/uploads/pubs/452.pdf>