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Rehabilitation Engineering Research Center on Technologies to Support Aging-in-Place for People with Long-Term Disabilities

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https://hdl.handle.net/1853/70667

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An Evaluation of a Telepresence Robot: User Testing Among Older Adults with Mobility Impairment

(Georgia Institute of Technology, 2017-03) Wu, Xian ; Thomas, Rebecca ; Drobina, Emma ; Mitzner, Tracy ; Beer, Jenay

For older adults with mobility impairment, maintaining health and wellness while aging-in-place independently is crucial. Telepresence technology, such as Kubi, can be potentially beneficial for this target population to stay socially connected [1]. However, the Kubi robot is not specifically designed for older adults with mobility impairment. For this target population to adopt the technology successfully, it is important to ensure that they would not experience usability barriers. Thus, we conducted usability testing of Kubi with five older adults with self-reported mobility impairment. The findings indicated both hardware and GUI problematic issues for this population. Hardware problems were primarily related to the base. GUI usability issues were caused by system visibility and control of the robot. These findings provide direction for improving the usability of telepresence robots, particularly for adults aging with mobility impairment.
SmartBathroom: Developing a smart environment to study bathroom transfers

(RESNA, 2017) Jones, Brian D. ; Pandey, Shiva ; Presti, Peter ; Taylor, Russell ; Natarajan, Prasanna ; Mahajan, Shambhavi ; Mahajan, Harshal P. ; Sanford, Jon

Individuals’ functional abilities change over time; they increase and then decrease over the lifespan, and in some they may fluctuate over the course of a day. While these fluctuations may not impact one’s ability to engage in daily activities, they can be problematic for people aging with disability or a progressive chronic condition such as arthritis or multiple sclerosis, particularly when performing toilet or shower/bath transfers. Although various assistive technologies (AT) and environmental modifications are designed to facilitate bathroom transfers, they are static solutions, selected to match an individual’s ability at one point in time rather than providing a dynamic environment that can adapt to support changing abilities. The SmartBathroom Laboratory is being developed as part of the RERC TechSAge as a highly sensed, adjustable residential bathroom environment to accommodate a wide variety of research studies on task performance during bathroom transfers. These studies will focus on identifying the problems faced by people with functional limitations as they age as well as on exploring viable solutions to these problems. In this paper, we describe design and engineering requirements, challenges, and choices in the development of the SmartBathroom Laboratory.
Haptic Simulation for Robot-Assisted Dressing

(Georgia Institute of Technology, 2017) Yu, Wenhao ; Kapusta, Ariel ; Tan, Jie ; Kemp, Charles C. ; Turk, Greg ; Liu, C. Karen

There is a considerable need for assistive dressing among people with disabilities, and robots have the potential to fulfill this need. However, training such a robot would require extensive trials in order to learn the skills of assistive dressing. Such training would be time-consuming and require considerable effort to recruit participants and conduct trials. In addition, for some cases that might cause injury to the person being dressed, it is impractical and unethical to perform such trials. In this work, we focus on a representative dressing task of pulling the sleeve of a hospital gown onto a person’s arm. We present a system that learns a haptic classifier for the outcome of the task given few (2-3) real-world trials with one person. Our system first optimizes the parameters of a physics simulator using real-world data. Using the optimized simulator, the system then simulates more haptic sensory data with noise models that account for randomness in the experiment. We then train hidden Markov Models (HMMs) on the simulated haptic data. The trained HMMs can then be used to classify and predict the outcome of the assistive dressing task based on haptic signals measured by a real robot’s end effector. This system achieves 92.83% accuracy in classifying the outcome of the robot-assisted dressing task with people not included in simulation optimization. We compare our classifiers to those trained on real-world data. We show that the classifiers from our system can categorize the dressing task outcomes more accurately than classifiers trained on ten times more real data.
Data-Driven Haptic Perception for Robot-Assisted Dressing

(Georgia Institute of Technology, 2016-08) Kapusta, Ariel ; Yu, Wenhao ; Bhattacharjee, Tapomayukh ; Liu, C. Karen ; Turk, Greg ; Kemp, Charles C.

Dressing is an important activity of daily living (ADL) with which many people require assistance due to impairments. Robots have the potential to provide dressing assistance, but physical interactions between clothing and the human body can be complex and difficult to visually observe. We provide evidence that data-driven haptic perception can be used to infer relationships between clothing and the human body during robot-assisted dressing. We conducted a carefully controlled experiment with 12 human participants during which a robot pulled a hospital gown along the length of each person’s forearm 30 times. This representative task resulted in one of the following three outcomes: the hand missed the opening to the sleeve; the hand or forearm became caught on the sleeve; or the full forearm successfully entered the sleeve. We found that hidden Markov models (HMMs) using only forces measured at the robot’s end effector classified these outcomes with high accuracy. The HMMs’ performance generalized well to participants (98.61% accuracy) and velocities (98.61% accuracy) outside of the training data. They also performed well when we limited the force applied by the robot (95.8% accuracy with a 2N threshold), and could predict the outcome early in the process. Despite the lightweight hospital gown, HMMs that used forces in the direction of gravity substantially outperformed those that did not. The best performing HMMs used forces in the direction of motion and the direction of gravity.
Analysis Of The Effect Of A Rear Wall Grab Bar Configuration On The Fall Risk Associated With Toilet Transfers In Older Adults With Mobility Impairment

(RESNA/NCART, 2016) Sanath, Achyuthkumar A. ; Mahajan, Harshal P. ; Gonzalez, Elena ; Sanford, Jon ; Fain, W. Brad

Twenty-three older adults were recruited for a two-hour in-home study. As a part of the process, the researchers asked questions regarding their process for toilet transfer, asked to provide ratings about toilet transfer in terms of their levels of confidence, difficulty, and how much more challenging it has become with age and with their permission, took photographs of their toilets. The objective of this project is to conduct a secondary analysis of this photographic and interview data to test our hypothesis that the presence of a grab bar on the rear wall prompted older adults to stretch, in order to reach for it across the toilet, consequently increasing the fall risk. The analysis showed us that the presence of a rear wall grab bar, though perceived to ease transfers, might actually prove more challenging to perform transfers with. The rear wall grab bar might only provide an illusion of safety. The results from this analysis may help guide future research undertakings to understand the relationship between a grab bar’s configuration and the risk of falling.
Autobed: Open Hardware for Accessible Web-based Control of an Electric Bed

(Georgia Institute of Technology, 2016) Grice, Phillip M. ; Chitalia, Yash ; Rich, Megan ; Clever, Henry M. ; Kemp, Charles C.

Individuals with severe motor impairments often have difficulty operating the standard controls of electric beds and so require a caregiver to adjust their position for utility, comfort, or to prevent pressure ulcers. Assistive human-computer interaction devices allow many such individuals to operate a computer and web browser. Here, we present the Autobed, a Wi-Fi-connected device that enables control of an Invacare Full-Electric Homecare Bed, a Medicare-approved device in the US, from any modern web browser, without modification of existing hardware. We detail the design and operation of the Autobed. We also examine its usage by one individual with severe motor impairments and his primary caregiver in their own home, including usage logs from a period of 102 days and detailed questionnaires. Finally, we make the entire system, including hardware design and components, software, and build instructions, available under permissive open-source licenses.
Moving In Out and Around the Home: Solutions from Older Adults with Long-term Mobility Impairment

(Georgia Institute of Technology, 2015-06) Gonzalez, Elena ; Fausset, Cara ; Foster, Amanda ; Cha, Grace ; Fain, W. Brad

The purpose of this study was to explore how older adults aging with long-term mobility impairment have adapted to mobility challenges in the home. Through in-home interviews, participants discussed their experience moving in, out and around their home with regard to challenges, solutions, barriers, and changes with age. This paper provides a characterization of the solutions used by participants to overcome in-home mobility challenges as well as unresolved barriers they faced. These themes illustrate the ingenuity of the participants as well as opportunities to support aging in place via design to better match a person’s environment to his/her capabilities.
A Robotic System for Reaching in Dense Clutter that Integrates Model Predictive Control, Learning, Haptic Mapping, and Planning

(Georgia Institute of Technology, 2014-09) Bhattacharjee, Tapomayukh ; Grice, Phillip M. ; Kapusta, Ariel ; Killpack, Marc D. ; Park, Daehyung ; Kemp, Charles C.

We present a system that enables a robot to reach locations in dense clutter using only haptic sensing. Our system integrates model predictive control [1], learned initial conditions [2], tactile recognition of object types [3], haptic mapping, and geometric planning to efficiently reach locations using whole- arm tactile sensing [4]. We motivate our work, present a system architecture, summarize each component of the system, and present results from our evaluation of the system reaching to target locations in dense artificial foliage.
Learning to Reach into the Unknown: Selecting Initial Conditions When Reaching in Clutter

(Georgia Institute of Technology, 2014-09) Park, Daehyung ; Kapusta, Ariel ; Kim, You Keun ; Rehg, James M. ; Kemp, Charles C.

Often in highly-cluttered environments, a robot can observe the exterior of the environment with ease, but cannot directly view nor easily infer its detailed internal structure (e.g., dense foliage or a full refrigerator shelf). We present a data-driven approach that greatly improves a robot’s success at reaching to a goal location in the unknown interior of an environment based on observable external properties, such as the category of the clutter and the locations of openings into the clutter (i.e., apertures). We focus on the problem of selecting a good initial configuration for a manipulator when reaching with a greedy controller. We use density estimation to model the probability of a successful reach given an initial condition and then perform constrained optimization to find an initial condition with the highest estimated probability of success. We evaluate our approach with two simulated robots reaching in clutter, and provide a demonstration with a real PR2 robot reaching to locations through random apertures. In our evaluations, our approach significantly outperformed two alter- native approaches when making two consecutive reach attempts to goals in distinct categories of unknown clutter. Notably, our approach only uses sparse readily-apparent features.
Interleaving Planning and Control for Efficient Haptically-guided Reaching in Unknown Environments

(Georgia Institute of Technology, 2014) Park, Daehyung ; Kapusta, Ariel ; Hawke, Jeffrey ; Kemp, Charles C.

We present a new method for reaching in an initially unknown environment with only haptic sensing. In this paper, we propose a haptically-guided interleaving planning and control (HIPC) method with a haptic mapping framework. HIPC runs two planning methods, interleaving a task-space and a joint-space planner, to provide fast reaching performance. It continually replans a valid trajectory, alternating between planners and quickly reflecting collected tactile information from an unknown environment. One key idea is that tactile sensing can be used to directly map an immediate cause of interference when reaching. The mapping framework efficiently assigns raw tactile information from whole-arm tactile sensors into a 3D voxel-based collision map. Our method uses a previously published contact-regulating controller based on model predictive control (MPC). In our evaluation with a physics simulation of a humanoid robot, interleaving was superior at reaching in the 9 types of environments we used.