Algorithm for the Comparison of Human Periodic Movements Using Wearable Devices.

Marlon Burbano-Fernandez,Yilton Riascos,V Venkataraman,Gustavo Ramirez-Gonzalez,Mario Muñoz-Organero,M Thilagaraj,Jhoana Sandoval-Serna,N Arunkumar,Kalidoss Rajakani

doi:10.1155/2021/8729108

Abstract

In the context of teaching-learning of motor skills in a virtual environment, videos are generally used. The person who wants to learn a certain movement watches a video and tries to perform the activity. In this sense, feedback is rarely thought of. This article proposes an algorithm in which two periodic movements are compared, the one carried out by an expert and the one carried out by the person who is learning, in order to determine how closely these two movements are performed and to provide feedback from them. The algorithm starts from the capture of data through a wearable device that yields data from an accelerometer; in this case, the data of the expert and the data of the person who is learning are captured in a dataset of salsa dance steps. Adjustments are made to the data in terms of Pearson iterations, synchronization, filtering, and normalization, and DTW, linear regression, and error analysis are used to make the corresponding comparison of the two datasets. With the above, it is possible to determine if the cycles of the two signals coincide and how closely the learner's movements resemble those of the expert.

Highlights

The process mentioned is carried out and results in vector R, which is summarized in Figure 8 that shows that the maximum value found for the Pearson iterations is given in position 120, in such a way that vector u is displaced; the 120 positions form vector uadj in such a way that both vectors coincide in their cycles
En, the Pearson analysis is carried out for each signal and, as a result, the signal is obtained resulting from the Pearson iterations and the period of the signal; that is, every few data, there is a repetition of the movement and the first recommendation, where it is found if two samples have the same period
If the period of the samples is the same, the data is synchronized from the Pearson analysis shown in the “data adjustment” section of the two signals. We proceed that both signals start at the first maximum and that the two signals have the same number of elements

Summary

Algorithm for the Comparison of Human Periodic Movements Using Wearable Devices

Marlon Burbano-Fernandez ,1 Jhoana Sandoval-Serna ,2 Yilton Riascos ,3 Mario Muñoz-Organero ,4 M. E person who wants to learn a certain movement watches a video and tries to perform the activity. In this sense, feedback is rarely thought of. Is article proposes an algorithm in which two periodic movements are compared, the one carried out by an expert and the one carried out by the person who is learning, in order to determine how closely these two movements are performed and to provide feedback from them. E algorithm starts from the capture of data through a wearable device that yields data from an accelerometer; in this case, the data of the expert and the data of the person who is learning are captured in a dataset of salsa dance steps. It is possible to determine if the cycles of the two signals coincide and how closely the learner’s movements resemble those of the expert

Introduction

Materials and Methods

Sample number

Results

Query value

Initial data combinet