Motion quaternion-based motion estimation method of MYO using K-means algorithm and Bayesian probability

Sung, Yunsick; Guo, Hongwei; Lee, Sang-Geol

doi:10.1007/s00500-018-3379-3

Cited by 6 publications

(7 citation statements)

References 19 publications

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“…Due to the current substantial cost of wearable sensors, it is desirable to reduce the number of sensors required to estimate movements. Some traditional algorithms, such as Bayesian probability and K-means, are utilized to estimate the movements of the unmonitored parts of a body by considering the movements measured directly using sensors [14]. Bayesian probability was first used to estimate the arm movement by Kim et al [15].…”

Section: Bayesian-based Movement Estimationmentioning

confidence: 99%

“…Table 1, it can be seen that there are mainly two types of conventional and widespread motion capture methods. These methods can be classified into image-based methods [18][19][20], which estimate the movement based on convolutional neural networks (CNNs) [23], and sensor-based methods, which use Bayesian probability [11,14,15] and LSTM [23]. In [11,14,15], the movements were estimated using Bayesian probability, whereas in [23], the surface conditions of the road and age-group of the subjects were detected based on the sensor values and an LSTM network.…”

Section: Comparison Of the Bayesian-based And Deep Learning-based Movmentioning

confidence: 99%

“…These methods can be classified into image-based methods [18][19][20], which estimate the movement based on convolutional neural networks (CNNs) [23], and sensor-based methods, which use Bayesian probability [11,14,15] and LSTM [23]. In [11,14,15], the movements were estimated using Bayesian probability, whereas in [23], the surface conditions of the road and age-group of the subjects were detected based on the sensor values and an LSTM network. Owing to the significant contribution of deep learning methods in the field of motion capture, this study is expected to bring forth a deep learning-based framework, instead of the traditional methods [11,14,15], to improve the performance of VR applications using soft sensors.…”

Section: Comparison Of the Bayesian-based And Deep Learning-based Movmentioning

confidence: 99%

“…where m U * t and m F * t are the final estimated upper arm movement and forearm movement, respectively. They consist of the final estimated orientations of the upper arm and the forearm as shown in Equations (14) and (15).…”

Section: Movement Estimation Stagementioning

confidence: 99%

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Movement Estimation Using Soft Sensors Based on Bi-LSTM and Two-Layer LSTM for Human Motion Capture

Guo

Sung

2020

Sensors

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The importance of estimating human movement has increased in the field of human motion capture. HTC VIVE is a popular device that provides a convenient way of capturing human motions using several sensors. Recently, the motion of only users’ hands has been captured, thereby greatly reducing the range of motion captured. This paper proposes a framework to estimate single-arm orientations using soft sensors mainly by combining a Bi-long short-term memory (Bi-LSTM) and two-layer LSTM. Positions of the two hands are measured using an HTC VIVE set, and the orientations of a single arm, including its corresponding upper arm and forearm, are estimated using the proposed framework based on the estimated positions of the two hands. Given that the proposed framework is meant for a single arm, if orientations of two arms are required to be estimated, the estimations are performed twice. To obtain the ground truth of the orientations of single-arm movements, two Myo gesture-control sensory armbands are employed on the single arm: one for the upper arm and the other for the forearm. The proposed framework analyzed the contextual features of consecutive sensory arm movements, which provides an efficient way to improve the accuracy of arm movement estimation. In comparison with the ground truth, the proposed method estimated the arm movements using a dynamic time warping distance, which was the average of 73.90% less than that of a conventional Bayesian framework. The distinct feature of our proposed framework is that the number of sensors attached to end-users is reduced. Additionally, with the use of our framework, the arm orientations can be estimated with any soft sensor, and good accuracy of the estimations can be ensured. Another contribution is the suggestion of the combination of the Bi-LSTM and two-layer LSTM.

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Section: Bayesian-based Movement Estimationmentioning

confidence: 99%

Section: Comparison Of the Bayesian-based And Deep Learning-based Movmentioning

confidence: 99%

Section: Comparison Of the Bayesian-based And Deep Learning-based Movmentioning

confidence: 99%

Section: Movement Estimation Stagementioning

confidence: 99%

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Movement Estimation Using Soft Sensors Based on Bi-LSTM and Two-Layer LSTM for Human Motion Capture

Guo

Sung

2020

Sensors

Self Cite

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“…Choi and Song (2018) introduce a social control theory (SCT) to understand the effects of deterrence on public corporation employees' ISS compliance and elucidate employees' motivations of ISS violation and different perceptions of sanction threats, which helps information security institutions to consider deterrence and self-punishment and to manage compliance with information security policy effectively and securely. Sung et al (2018) suggest a motion estimation method to increase the accuracy of motion estimation. The orientations obtained from MYO are expressed by one quaternion and are clustered by K-means.…”

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confidence: 99%

Advanced computer science and applications for soft computing of converged IT environments

Pan

2018

Soft Comput

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IntroductionThis special issue is to gather new soft computing trends and methodological recent advances on a wide range of problems arising in different fields to handle practical data of converged environment. Many advanced computational methods have been successfully applied to a range of optimization and classification problems in soft computing, but there are still many practical problems tackled by traditional methods that are generally difficult to solve experimentally in practical converged data. More specifically, many computational problems arising in the fields of scientific programming have been addressed in AI, HPC, large-scale data mining, etc., that handles practical converged data. Submissions are welcomed on scientific programming applied to optimization in practical data. We invite researchers and practitioners to submit their original research articles and theoretical articles.This special issue is soliciting high-quality technical papers addressing research practices and challenges in the areas of ubiquitous computing. It will reflect the state of the art of the computational methods, involving theory, algorithm, numerical simulation, error and uncertainty analysis, and/or novel application of new processing techniques in engineering, science, and other disciplines related to ubiquitous computing.

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Exploring raw data transformations on inertial sensor data to model user expertise when learning psychomotor skills

Portaz,

Corbi,

Casas-Ortiz

et al. 2024

User Model User-Adap Inter

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This paper introduces a novel approach for leveraging inertial data to discern expertise levels in motor skill execution, specifically distinguishing between experts and beginners. By implementing inertial data transformation and fusion techniques, we conduct a comprehensive analysis of motor behaviour. Our approach goes beyond conventional assessments, providing nuanced insights into the underlying patterns of movement. Additionally, we explore the potential for utilising this data-driven methodology to aid novice practitioners in enhancing their performance. The findings showcase the efficacy of this approach in accurately identifying proficiency levels and lay the groundwork for personalised interventions to support skill refinement and mastery. This research contributes to the field of motor skill assessment and intervention strategies, with broad implications for sports training, physical rehabilitation, and performance optimisation across various domains.

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Motion quaternion-based motion estimation method of MYO using K-means algorithm and Bayesian probability

Cited by 6 publications

References 19 publications

Movement Estimation Using Soft Sensors Based on Bi-LSTM and Two-Layer LSTM for Human Motion Capture

Movement Estimation Using Soft Sensors Based on Bi-LSTM and Two-Layer LSTM for Human Motion Capture

Advanced computer science and applications for soft computing of converged IT environments

Exploring raw data transformations on inertial sensor data to model user expertise when learning psychomotor skills

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