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As over 11,000 people turn 65 each day in the U.S., our country, like many others, is facing growing challenges in caring for elderly persons, further exacerbated by a major shortfall of care workers. To address this, we introduce an eldercare robot (E-BAR) capable of lifting a human body, assisting with postural changes/ambulation, and catching a user during a fall, all without the use of any wearable device or harness. Our robot is the first to integrate these 3 tasks, and is capable of lifting the full weight of a human outside of the robot’s base of support (across gaps and obstacles). In developing E-BAR, we interviewed nurses and care professionals and conducted user experience tests with elderly persons. Based on their functional requirements, the design parameters were optimized using a computational model and trade-off analysis. We developed a novel 18-bar linkage to lift a person from a floor to a standing position along a natural trajectory, while providing maximal mechanical advantage at key points. An omnidirectional, nonholonomic drive base, in which the wheels could be oriented to passively maximize floor grip, enabled the robot to resist lateral forces without active compensation. With a minimum width of 38 cm, the robot’s small footprint allowed it to navigate the typical home environment. Four airbags were used to catch and stabilize a user during a fall in ≤ 250 ms. We demonstrate E-BAR’s utility in multiple typical home scenarios, including getting into/out of a bathtub, bending to reach for objects, sit-to-stand transitions, and ambulation.
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Monitoring the Mental State of Cooperativeness for Guiding an Elderly Person in Sit-to-Stand Assistance
In providing physical assistance to elderly people, ensuring cooperative behavior from the elderly persons is a critical requirement. In sit-to-stand assistance, for example, an older adult must lean forward, so that the body mass can shift towards the feet before a caregiver starts lifting the body. An experienced caregiver guides the older adult through verbal communications and physical interactions, so that the older adult may be cooperative throughout the process. This guidance is of paramount importance and is a major challenge in introducing a robotic aid to the eldercare environment. The wide-scope goal of the current work is to develop an in-telligent eldercare robot that can a) monitor the mental state of an older adult, and b) guide the older adult through an assisting procedure so that he/she can be cooperative in being assisted. The current work presents a basic modeling framework for describing a human's physical behaviors reflecting an internal mental state, and an algorithm for estimating the mental state through interactive observations. The sit-to-stand assistance problem is considered for the initial study. A simple Kalman Filter is constructed for estimating the level of cooperativeness in response to applied cues, with a thresholding scheme being used to make judgments on the cooperativeness state.
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- Award ID(s):
- 2133072
- PAR ID:
- 10396547
- Date Published:
- Journal Name:
- 2022 IEEE International conference on robotics and automation
- Page Range / eLocation ID:
- 6465 - 6471
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ICRA46639.2022.9812422
