SkinGrip: An Adaptive Soft Robotic Manipulator with Capacitive Sensing for Whole-Limb Bed Bathing Assistance

TL;DR

This work addresses the challenge of safe, effective bed bathing amidst aging populations and caregiver shortages by introducing SkinGrip, a soft, tendon-driven robotic manipulator with integrated capacitive sensing. The system, mounted on a Stretch RE1, uses capacitive proximity cues to achieve dynamic, full-circumference contact and to control limb bathing via capacitive servoing. In a human study with 12 participants across 96 trials, SkinGrip achieved markedly higher cleaning effectiveness than a baseline rigid end effector (88.8% arms, 81.4% legs vs 63.4% arms, 55.4% legs), while participants reported superior safety, comfort, and cleaning area and preferred the soft approach. The results demonstrate the potential of soft, capacitive-sensing end effectors to reduce caregiver burden and improve the quality of robotic bathing assistance, with clear avenues for future improvements such as higher-DOF designs and targeted cleaning via vision-guided strategies.

Abstract

Robotics presents a promising opportunity for enhancing bathing assistance, potentially to alleviate labor shortages and reduce care costs, while offering consistent and gentle care for individuals with physical disabilities. However, ensuring flexible and efficient cleaning of the human body poses challenges as it involves direct physical contact between the human and the robot, and necessitates simple, safe, and effective control. In this paper, we introduce a soft, expandable robotic manipulator with embedded capacitive proximity sensing arrays, designed for safe and efficient bathing assistance. We conduct a thorough evaluation of our soft manipulator, comparing it with a baseline rigid end effector in a human study involving 12 participants across $96$ bathing trails. Our soft manipulator achieves an an average cleaning effectiveness of 88.8% on arms and 81.4% on legs, far exceeding the performance of the baseline. Participant feedback further validates the manipulator's ability to maintain safety, comfort, and thorough cleaning.

Figures (7)

• Figure 1: The soft manipulator developed in this work, mounted on the Stretch RE1 mobile manipulator, cleaning a person's right arm.
• Figure 2: 3D model and prototype of the proposed SkinGrip, as well as the baseline end effector. (a) The SkinGrip is mounted on a Stretch RE1 robot for cleaning tasks. (b) 3D model of the SkinGrip. (c) Real-world SkinGrip, equipped with eight copper foils labeled as: $1$-left top, $2$-left middle, $3$-left bottom, $4$-right top, $5$-right middle, $6$-right bottom, $7$-left inner, and $8$-right inner. (d) & (e) The SkinGrip equipped with a sponge and then wrapped with a washcloth. (f) 3D model of the baseline end effector. (g) Real-world baseline end effector, equipped with six copper foils labeled as: $1$-left top, $2$-left middle, $3$-left bottom, $4$-right top, $5$-right middle, and $6$-right bottom. (h) & (i) The baseline end effector equipped with a sponge and then wrapped with a washcloth. For both the SkinGrip and the baseline end effector, the capacitive sensors are encased in a protective plastic film to prevent direct contact with the washcloth and human skin.
• Figure 3: Capacitance measurements from all eight capacitive electrodes as the robot maneuvers the SkinGrip around an individual's limbs. (a) & (b) Capacitance values of six capacitive electrodes during various displacements of the tendon line as the manipulator wraps around the arm and lower leg, respectively. (c) & (d) Capacitance values of the eight capacitive electrodes as the SkinGrip approaches, wraps around, and maintains dynamic full contact with the arm or lower leg, respectively. We normalize all signals to the range of [0, 1].
• Figure 4: Real-world human study setup and cleaning performance of the two end effectors. (a) The system comprises a medical bed for participants to lie on, a Stretch RE1 robot equipped with either the soft or baseline end effector for cleaning the human limb, three webcams positioned to capture the limb from top, side, and bottom perspectives, and a phone camera for video recording. (b) Demonstrations of the cleaning capabilities of both the baseline end effector and our SkinGrip, shown in top, side, and bottom views before and after the cleaning process.
• Figure 5: Evaluation of cleaning performance using the SkinGrip and baseline end effector: (a) Cleaning percentage as observed from three different views of the arm and leg. (b) and (c) Impact of limb diameter and (d) and (e) limb length on cleaning percentage, evaluated separately for the arm (b, d) and leg (c, e).