Abstract
Exoskeletons are wearable devices for enhancing human physical performance and for studying actions and movements. They are worn on the body for additional power and load-carrying capacity. Exoskeletons can be controlled using signals from the muscles. In recent years, gait analysis has attracted increasing attention from fields such as animation, athletic performance analysis, and robotics. Gait patterns are unique, and each individual has his or her own distinct gait pattern characteristics. Gait analysis can monitor activity in sensitive areas. This paper uses various machine learning algorithms to predict the activity of subjects using exoskeletons. Here, localization data from the UIC machine learning repository are used to recognize activities with gait positions. The study also compares five machine learning methods and examines their efficiency and accuracy in activity prediction for three different subjects. The results for the various machine learning methods along with efficiency and accuracy results are discussed.
Highlights
Exoskeletons are wearable devices for enhancing human performance
There is a wide range of applications for exoskeletons, including walking assistance for physically impaired people
7 different machine learning algorithms were used in this study, namely Naive Bayes, KNN, support vector machine, J48decision tree, radial basis function network classifier, multi-layer perceptron neural network, and random forest
Summary
Exoskeletons are wearable devices for enhancing human performance. They can supplement or increase the wearer’s speed, strength, and endurance. The wearer applies some force that acts to send control signals to the robotic system. This signal is sensed and amplified by the exoskeleton to exert the requisite force for the desired movement [1]. Exoskeletons have gained increasing attention in recent years when the U.S Defense department allotted funds for exoskeletons for human performance augmentation. There is a wide range of applications for exoskeletons, including walking assistance for physically impaired people
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