Teleoperation of Collaborative Robot for Remote Dementia Care in Home Environments

IEEE J Transl Eng Health Med. 2020 Jun 15:8:1400510. doi: 10.1109/JTEHM.2020.3002384. eCollection 2020.

Abstract

As a senile chronic, progressive and currently incurable disease, dementia has an enormous impact on society and life quality of the elderly. The development of teleoperation technology has changed the traditional way of care delivery and brought a variety of novel applications for dementia care. In this paper, a telerobotic system is presented which gives the caregivers the capability of assisting dementia elderly remotely. The proposed system is composed of a dual-arm collaborative robot (YuMi) and a wearable motion capture device. The communication architecture is achieved by the robot operation system (ROS). The position-orientation data of the operator's hand are obtained and used to control the YuMi robot. Besides, a path-constrained mapping method is designed for motion trajectory tracking between the robot and the operator in the progress of teleoperation. Meanwhile, corresponding experiments are conducted to verify the performance of the trajectory tracking using the path-constrained mapping method. Results show that the position tracking deviation between the trajectory of the operator and the robot measured by dynamic time warping distance is 1.05 mm at the sampling frequency of 7.5 Hz. Moreover, the practicability of the proposed system was verified by teleoperating the YuMi robot to pick up a medicine bottle and further demonstrated by assisting an elderly woman in picking up a cup remotely. The proposed telerobotic system has potential utility for improving the life quality of dementia elderly and the care effect of their caregivers.

Keywords: Assistive robot; motion capture; remote dementia care; teleoperation; telerobotic system.

Grants and funding

This work was supported in part by the National Natural Science Foundation of China under Grant 51975513, in part by the Natural Science Foundation of Zhejiang Province, China under Grant LR20E050003, in part by the Major Research Plan of National Natural Science Foundation of China under Grant 51890884, in part by the Zhejiang University Special Scientific Research Fund for COVID-19 Prevention and Control under Grant 2020XGZX017, in part by the Director Foundation of the State Key Laboratory of Fluid Power and Mechatronic Systems under Grant SKLoFP_ZZ_2002, in part by the Science Fund for Creative Research Groups of the National Natural Science Foundation of China under Grant 51821093, in part by the Robotics Institute of Zhejiang University under Grant K18-508116-008-03, and in part by the China’s Thousand Talents Plan Young Professionals Program.