A length-adjustable vacuum-powered artificial muscle for wearable physiotherapy assistance in infants

Front Robot AI. 2023 May 4:10:1190387. doi: 10.3389/frobt.2023.1190387. eCollection 2023.

Abstract

Soft pneumatic artificial muscles are increasingly popular in the field of soft robotics due to their light-weight, complex motions, and safe interfacing with humans. In this paper, we present a Vacuum-Powered Artificial Muscle (VPAM) with an adjustable operating length that offers adaptability throughout its use, particularly in settings with variable workspaces. To achieve the adjustable operating length, we designed the VPAM with a modular structure consisting of cells that can be clipped in a collapsed state and unclipped as desired. We then conducted a case study in infant physical therapy to demonstrate the capabilities of our actuator. We developed a dynamic model of the device and a model-informed open-loop control system, and validated their accuracy in a simulated patient setup. Our results showed that the VPAM maintains its performance as it grows. This is crucial in applications such as infant physical therapy where the device must adapt to the growth of the patient during a 6-month treatment regime without actuator replacement. The ability to adjust the length of the VPAM on demand offers a significant advantage over traditional fixed-length actuators, making it a promising solution for soft robotics. This actuator has potential for various applications that can leverage on demand expansion and shrinking, including exoskeletons, wearable devices, medical robots, and exploration robots.

Keywords: adaptable; artificial muscle; growing; soft robotics; wearable.

Grants and funding

This work was supported by FONDECYT Peru under contract No. 105-2021-FONDECYT Proyectos de Investigación Aplicada y Desarrollo Tecnológico, as well as an MIT MISTI (between MIT and UTEC to ER and EV), a National Science Foundation (NSF) EFRI Grant (EFRI C3 SoRo: functional-Domain Soft Robots Precisely Controlled by Quantitative Dynamic Models and Data, Award #1935291) and SG was funded by an NSF GRFP award (#1324585).