The NRI Eldercare Project in the d’Arbeloff Lab consists of several different thrusts, led by different graduate students. These are as follows:
Graduate Student: John Bell
The sit-to-stand transition is a key activity of daily living which many elderly people require the assistance of a caregiver to perform. Due to caregiver shortages in the workforce and the increased risk of mass infection potentially caused by in-person caregiving, it is important to develop a solution that allows elderly patients to receive assistance in the sit-to-stand transition without the required presence of a human caregiver. Such a solution should work collaboratively with the elderly patient, such that the patient is never forced to perform a motion they do not wish to perform, and so that the patient is properly supported.
Our Research Solutions
We are working on the development of a sit-to-stand assistance robot, which will ultimately serve as the basis for a walker robot. Key to the development of such a robot are:
- Methods for effectively establishing mutual understanding between robot and human
- The mechanical ability to gently support the elderly patient’s weight through the sit-to-stand transition
Each of these key questions are being addressed by a different aspect of the project:
Dual-Motor Actuator Development
One aspect of the project is focused on development of a design of dual-motor actuator which is used to lift the full or partial weight of the elderly patient through the sit-to-stand transition. This dual-motor actuator is designed to support high bias loads at low frequencies of feedback control, but also to possess high-fidelity feedback control at high frequencies, which can be used to apply high-frequency cues to the user or actively damp vibrations.
Human–Robot Communication and Mental State Estimation
The other aspect of the project is focused on how a robot can estimate the mental state of a human, as well as on how a robot can effectively use cues and other communication methods to convey recommendations and requests to the human user. Key to this aspect of the project are human subject experiments, focused on:
- Characterizing the response of a human to applied cues and instructions when possessing different basic mental states. This can be used to develop models which estimate mental state from behavior.
- Testing the effectiveness of different cues, communication methods, and instructions from robot to human, to both precede the sit-to-stand transition and guide its motion.
Handle Assist Robot
Graduate Student: Roberto Bolli
Studies show that balance is improved when an object provides tactile sensation and/or body support to the hand. Many elderly people suffering from impaired balance benefit from grab bars placed throughout the home, especially in the bedroom and bathroom. However, installing grab bars is costly, expensive, and restricted by wall geometry and other structures, especially where it would most benefit the elderly.
Our Research Solution
We aim to develop a robot that can provide both tactile and body-weight support to elderly persons by leveraging empirical ergonomics research. The robot will have a handle with embedded force sensors that can extend to provide an anchor of support depending on the position of the user. In addition, the robot will leverage a unique high-friction omnidirectional drive base to safely and effectively navigate the home environment.
Graduate Student: Emily Kamienski
Many elderly people suffer from serious fall injuries despite using a walker. This is because walkers can tip over during a fall or the person can lose their grip on the walker, and lose all support benefits. Other mobility aids, such as gait trainers, have a larger footprint and are harder to tip over. But these devices are not suitable for home use due to their large size, which severely limits their maneuverability.
Our Research Solution
We are developing a reconfigurable walker that can support the user and quickly transition between a compact maneuverable configuration, and a more stable configuration if an eminent fall is predicted. The user is constantly monitored via wearable sensors, whose data is fed into a prediction algorithm to determine the real-time fall risk. Once a high risk of falling is predicted, the walker will rapidly expand its base of support (BOS), creating a more stable walker configuration that is untippable. Additionally, the user is attached to the walker frame via a harness so that they can be supported.