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

Abstract

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 Multi-Layer 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.

Fig 1. Real-time biofeedback system diagram.

Schematic representing the real-time biofeedback system: the gravitational component of the filtered signals collected from the six IMUs placed on the subject’s body is removed using the magnetometers’ data. Then, the resulting accelerometers and gyroscopes signals are segmented and organised in a dataset by extracting the most informative set of features (such as mean, standard deviation, mean frequency, temporal entropy, etc.) for each x-y-z temporal serie of each IMU. The dataset is used to train a ML classifier to monitor the execution of different resistive exercises and give a real-time visual feedback to the subject undergoing training through a GUI.

Fig 2. Signal processing workflow.

Schematic describing the method used to obtain the final dataset composed by 36 signals, including accelerations cleaned from gravity component and angular velocities of the six IMUs.

Fig 3. IMUs placement during the experiments.

(a) First session of data collection with Xsens IMUs; (b) second session of acquisition with the two sensors superimposed.