Quantitative parameters developing for estimation of rehabilitation exercising on the basis of IMU sensor device

Ashapkina, Maria S.; Алпатов, А. В.; Sablina, Victoria A.; Chekushin, A A

doi:10.1109/meco.2018.8405987

Cited by 5 publications

(2 citation statements)

References 5 publications

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“…• obtaining the trajectory of movement at the control point of the movable link, relative to the link of the fixed support; • estimation of the measure of similarity of trajectories Q` at the control point with its reference image Q. The modern approach to the problem of controlling motor activity and determining the position of the body in space is based on a motion capture system based on video recording data [23].…”

Section: Health-improving Exercise Modelmentioning

confidence: 99%

Physical Activity Set Selection for Emotional State Harmonization Based on Facial Micro-Expression Analysis

Chernenko

Ashapkina

Sablina

et al. 2022

Proceedings of the 32nd International Conference on Computer Graphics and Vision

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A new approach to harmonize a human emotional state is proposed. It is based on timely revealing and working through hidden negative emotions. The main ideas proving the possibility to implement this approach are described. The experimental investigation results for selected methods of revealing hidden negative emotions and working them through are represented. The video sequences reproducing emotions are used as input data. The ultimate research aim is the software system construction for the human emotional state harmonization. The effect of using such a system will be maintaining the health and increasing the quality of life of the modern human. The basic functions of this system are highlighted. The problem of repressing emotions and its possible negative consequences for the health are considered. The possibility of revealing repressed emotions from facial micro-expressions is justified. The main stages of the micro-facial movement detection by hybrid methods are considered in details. Each stage results are illustrated using the software pipeline developed in the previous research. It is proposed to work through revealed negative emotions by executing the individually selected physical activity set. The problem of the execution accuracy control of these exercises when working without assistance is considered. The health-improving exercise model is represented. The possibility of recording the motion trajectory using a smartphone software solution is shown. The method of the motion execution accuracy estimation on the basis of dynamic time warping is described. The development and implementation prospects of the proposed approach based on a smartphone software solution are justified.

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Section: Health-improving Exercise Modelmentioning

confidence: 99%

Physical Activity Set Selection for Emotional State Harmonization Based on Facial Micro-Expression Analysis

Chernenko

Ashapkina

Sablina

et al. 2022

Proceedings of the 32nd International Conference on Computer Graphics and Vision

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“…In this context, some studies use wearable devices in order to capture movements for analysis [ 2 – 5 ], while others aim at making some feedback towards people [ 6 – 9 ]. These works are framed in algorithms, generally machine learning for the classification and identification of movements.…”

Section: Introductionmentioning

confidence: 99%

Algorithm for the Comparison of Human Periodic Movements Using Wearable Devices

Burbano-Fernandez

Sandoval-Serna

Forero

et al. 2021

Journal of Healthcare Engineering

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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.

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Remote Monitoring of Rehabilitation of Patients of Traumatology and Orthopedic Profile

Ashapkina

Алпатов

Sablina

et al. 2019

2019 8th Mediterranean Conference on Embedded Computing (MECO)

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Quantitative parameters developing for estimation of rehabilitation exercising on the basis of IMU sensor device

Cited by 5 publications

References 5 publications

Physical Activity Set Selection for Emotional State Harmonization Based on Facial Micro-Expression Analysis

Physical Activity Set Selection for Emotional State Harmonization Based on Facial Micro-Expression Analysis

Algorithm for the Comparison of Human Periodic Movements Using Wearable Devices

Remote Monitoring of Rehabilitation of Patients of Traumatology and Orthopedic Profile

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