Improving Tandem Fluency Through Utilization of Deep Learning to Predict Human Motion in Exoskeleton

Abstract

first_pagesettingsOrder Article Reprints Open AccessArticle Improving Tandem Fluency Through Utilization of Deep Learning to Predict Human Motion in Exoskeleton by Bon Ho Koo 1ORCID,Ho Chit Siu 2ORCID,Luke Apostolides 1ORCID,Sangbae Kim 1 andLonnie G. Petersen 3,4,*ORCID 1 Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA 2 MIT Lincoln Laboratory, Lexington, MA 02421, USA 3 Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA 4 Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA * Author to whom correspondence should be addressed. Actuators 2025, 14(6), 260; https://doi.org/10.3390/act14060260 Submission received: 8 April 2025 / Revised: 13 May 2025 / Accepted: 20 May 2025 / Published: 23 May 2025 (This article belongs to the Special Issue Recent Advances in Soft Actuators, Robotics and Intelligence) Downloadkeyboard_arrow_down Browse Figures Versions Notes Abstract Today’s exoskeletons face challenges with low fluency (a quantifiable alternative to “seamlessness”), hypothesized to be caused by a lag in active control innate in many leader–follower paradigms seen in contemporary systems, leading to inefficiencies and discomfort. Furthermore, tandem fluency, a variation of fluency specific for tandem robots systems as exoskeletons, is yet to be rigorously tested in practice. This study aims to utilize metrics of tandem fluency in order to demonstrate improved human–robot interaction (HRI) in exoskeletons through human subject testing of a prototype 1 degree of freedom (DoF) exoskeleton using a motion prediction bidirectional long short-term memory (bi-LSTM) deep learning network. Subjects were recruited to conduct various upper body exercises about the elbow joint, and the collected sEMG, goniometer, and gas exchange data was used to design, test, optimize, and assess the performance of the 1 DoF exoskeleton using tandem fluency metrics. We found that the correlation between I-ACT, a metric of tandem fluency, the subjective survey responses, and metabolic data suggest that the use of a predictive bi-LSTM network to control a 1 DoF exoskeleton about the elbow results in an overall positive trend, which may correlate to high tandem fluency.