IMU-based classification of resistive exercises for real-time training monitoring on board the international space station with potential telemedicine spin-off

PLOS ONE RESEARCH ARTICLE IMU-based classification of resistive exercises for real-time training monitoring on board the international space station with potential telemedicine spin-off Martina Ravizza1¤a, Laura Giani1¤b, Francesco Jamal Sheiban ID1, Alessandra Pedrocchi ID1, John DeWitt2, Giancarlo Ferrigno ID1* a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 1 NearLab, Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milano, Italy, 2 Independent Researcher, Italy ¤a Current address: Xsens (Xsens Technologies B.V.), Enschede, Netherlands ¤b Current address: Ab.Acus s.r.l., Milano (MI), Italy * Abstract OPEN ACCESS Citation: Ravizza M, Giani L, Sheiban FJ, Pedrocchi A, DeWitt J, Ferrigno G (2023) IMU-based classification of resistive exercises for real-time training monitoring on board the international space station with potential telemedicine spin-off. PLoS ONE 18(8): e0289777. https://doi.org/ 10.1371/journal.pone.0289777 Editor: Andrea Tigrini, Polytechnic University of Marche: Universita Politecnica delle Marche, ITALY Received: March 31, 2023 Accepted: July 26, 2023 Published: August 10, 2023 Copyright: © 2023 Ravizza et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: All relevant data are within the manuscript and its Supporting information files. The microgravity exposure that astronauts undergo during space missions lasting up to 6 months induces biochemical and physiological changes potentially impacting on their health. As a countermeasure, astronauts perform an in-flight training program consisting in different resistive exercises. To train optimally and safely, astronauts need guidance by on-ground specialists via a real-time audio/video system that, however, is subject to a communication delay that increases in proportion to the distance between sender and receiver. The aim of this work was to develop and validate a wearable IMU-based biofeedback system to monitor astronauts in-flight training displaying real-time feedback on exercises execution. Such a system has potential spin-offs also on personalized home/remote training for fitness and rehabilitation. 29 subjects were recruited according to their physical shape and performance criteria to collect kinematics data under ethical committee approval. Tests were conducted to (i) compare the signals acquired with our system to those obtained with the current state-of-the-art inertial sensors and (ii) to assess the exercises classification performance. The magnitude square coherence between the signals collected with the two different systems shows good agreement between the data. Multiple classification algorithms were tested and the best accuracy was obtained using a MultiLayer Perceptron (MLP). MLP was also able to identify mixed errors during the exercise execution, a scenario that is quite common during training. The resulting system represents a novel low-cost training monitor tool that has space application, but also potential use on Earth for individuals working-out at home or remotely thanks to its ease of use and portability. Funding: G.F. received a grant (grant n˚: DC-VUM2017-006 MARS-PRE) from ASI (Italian Space Agency, https://www.asi.it/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. PLOS ONE | https://doi.org/10.1371/journal.pone.0289777 August 10, 2023 1 / 11 PLOS ONE Competing interests: The authors have declared that no competing interests exist. IMU-based classification of resistive exercises for real-time training monitoring Introduction Prolonged exposure to microgravity during long term spaceflights has been addressed as one of the main stress factors for the astronauts, as it is responsible for several physiological alterations affecting mostly the cardio-vascular and musculoskeletal systems, with consequences getting worse as the time of the mission increases. Over the last decades of space explorations, Long Duration Mission (LDM) allowed to collect data indicating a loss of bone mineral density of 5%, a reduction of muscle mass up to 35–40% and cardiovascular functions alteration [1]. To reduce these health problems, several countermeasure training programs have been implemented. The protocol considered in this paper is based on resistive exercises performed with the Advance Resistive Exercises Device (ARED) currently used on the International Space Station (ISS), which allows astronauts to perform typical gym exercises. Currently, ISS crew-members receive feedback from on ground specialists by using a real-time audio/video system to ensure optimal and safe performance [2]. However, as the distance from the Earth to the space vehicle increases, communication delays increase and loss of communication can occur. Consequently, training monitoring during future planned LDMs to the Moon and Mars could be problematic. It has been assessed that non-optimal exercise performance, especially by using high loads, may reduce training efficacy and can involve risk of injuries [3–5]. To overcome reduced opportunity for human coaching, the introduction of motion tracking technologies could be useful. Several studies in literature used motion capture systems to analyze motion during training exercises [6–9] and movement in microgravity conditions [10–14]. These solutions are not suitable for real-time monitoring on the ISS since they require bulky technology and complex system setup and operation. Conversely, Inertial Measurement Units (IMUs) are small and inexpensive devices that can be used to quantify human motion. IMUs have indeed been used for daily human activities recognition [15–17] as well as gait analysis [18], elderlies fall detection [19], medical monitoring [20] and stereotypical motor movements recognition in autism spectrum disorder [21]. Data from IMUs can also be used to classify typical gym exercises with supervised learning methods [22, 23]. Lee et al. [24] compared conventional machine learning (CML) and deep learning (DL) algorithms for detecting five induced deviations of squat by using five IMUs placed on the body. They obtained accuracies equal to 75.4% for CML and 91.7% for DL. Similarly, O’Reilly et al. [25] classified five induced deviations of deadlift performance starting from data collected by five IMUs with accuracies of 60% and 81%, respectively. Other studies used data collected from inertial sensors to classify various types of exercises performed in the same experimental session. De Villa et al. [26] used four IMUs to classify a set of seven exercises of upper and lower limbs frequently proposed in physical therapy routines (including squats, hip abduction, knee flex-extension, gait, elbow flex-extension, ex (...truncated)