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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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Author: Chitalia, Yash ×

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    3D Human Pose Estimation on a Configurable Bed from a Pressure Image
    ( 2018) Clever, Henry M. ; Kapusta, Ariel ; Park, Daehyung ; Erickson, Zackory ; Chitalia, Yash ; Kemp, Charles C.
    Robots have the potential to assist people in bed, such as in healthcare settings, yet bedding materials like sheets and blankets can make observation of the human body difficult for robots. A pressure-sensing mat on a bed can provide pressure images that are relatively insensitive to bedding materials. However, prior work on estimating human pose from pressure images has been restricted to 2D pose estimates and flat beds. In this work, we present two convolutional neural networks to estimate the 3D joint positions of a person in a configurable bed from a single pressure image. The first network directly outputs 3D joint positions, while the second outputs a kinematic model that includes estimated joint angles and limb lengths. We evaluated our networks on data from 17 human participants with two bed configurations: supine and seated. Our networks achieved a mean joint position error of 77 mm when tested with data from people outside the training set, outperforming several baselines. We also present a simple mechanical model that provides insight into ambiguity associated with limbs raised off of the pressure mat, and demonstrate that Monte Carlo dropout can be used to estimate pose confidence in these situations. Finally, we provide a demonstration in which a mobile manipulator uses our network’s estimated kinematic model to reach a location on a person’s body in spite of the person being seated in a bed and covered by a blanket.
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    Collaboration Between a Robotic Bed and a Mobile Manipulator May Improve Physical Assistance for People with Disabilities
    (Georgia Institute of Technology, 2016-08) Kapusta, Ariel ; Chitalia, Yash ; Park, Daehyung ; Kemp, Charles C.
    We present a robotic system designed to provide physical assistance to a person in bed. The system consists of a robotic bed (Autobed) and a mobile manipulator (PR2) that work together. The 3 degree-of-freedom (DoF) robotic bed moves the person’s body and uses a pressure sensing mat to estimate the body’s position. The mobile manipulator positions itself with respect to the bed and compliantly moves a lightweight object with one of its 7-DoF arms. The system optimizes its motions with respect to a task model and a model of the human’s body. The user provides high-level supervision to the system via a web-based interface. We first evaluated the ability of the robotic bed to estimate the location of the head of a person in a supine configuration via a study with 7 able-bodied participants. This estimation was robust to bedding, including a pillow under the person’s head. We then evaluated the ability of the full system to autonomously reach task-relevant poses on a medical mannequin placed in a supine position on the bed. We found that the robotic bed’s motion and perception each improved the overall system’s performance. Our results suggest that this type of multi-robot system could more effectively bring objects to desired locations with respect to the user’s body than a mobile manipulator working alone. This may in turn lead to improved physical assistance for people with disabilities at home and in healthcare facilities, since many assistive tasks involve an object being moved with respect to a person’s body.
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    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.