SEMG-Based Human In-Hand Motion Recognition Using Nonlinear Time Series Analysis and Random Forest

Xue, Yaxu; Ji, Xiaofei; Zhou, Dalin; Li, Jing; Ju, Zhaojie

doi:10.1109/access.2019.2957668

Cited by 20 publications

(14 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Previous studies on physiological signal classification have used SVM [ 47 ], RF [ 48 ], and other algorithms [ 49 ] for classification, while in this paper convolutional neural network is used to classify sEMGs. Figure 3(f) shows the accuracy of five classification algorithms in facial expression and intensity classification, among which convolution neural network is the algorithm with the highest accuracy.…”

Section: Resultsmentioning

confidence: 99%

Highly Robust and Wearable Facial Expression Recognition via Deep-Learning-Assisted, Soft Epidermal Electronics

et al. 2021

View full text Add to dashboard Cite

The facial expressions are a mirror of the elusive emotion hidden in the mind, and thus, capturing expressions is a crucial way of merging the inward world and virtual world. However, typical facial expression recognition (FER) systems are restricted by environments where faces must be clearly seen for computer vision, or rigid devices that are not suitable for the time-dynamic, curvilinear faces. Here, we present a robust, highly wearable FER system that is based on deep-learning-assisted, soft epidermal electronics. The epidermal electronics that can fully conform on faces enable high-fidelity biosignal acquisition without hindering spontaneous facial expressions, releasing the constraint of movement, space, and light. The deep learning method can significantly enhance the recognition accuracy of facial expression types and intensities based on a small sample. The proposed wearable FER system is superior for wide applicability and high accuracy. The FER system is suitable for the individual and shows essential robustness to different light, occlusion, and various face poses. It is totally different from but complementary to the computer vision technology that is merely suitable for simultaneous FER of multiple individuals in a specific place. This wearable FER system is successfully applied to human-avatar emotion interaction and verbal communication disambiguation in a real-life environment, enabling promising human-computer interaction applications.

show abstract

Section: Resultsmentioning

confidence: 99%

Highly Robust and Wearable Facial Expression Recognition via Deep-Learning-Assisted, Soft Epidermal Electronics

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Lempel-Ziv complexity (LZC), sampling entropy (SampEn), approximate entropy (ApEn), fuzzy entropy (FuzzyEn), distribution entropy (DistEn), box-counting fractal dimension (FD), and largest Lyapunov exponent (LyapExp) were calculated as nonlinear dynamics features. The details of the specific nonlinear dynamics feature extraction method can be found in [50][51][52][53][54]. As a result, we extracted 25 dimensional features from each segment, with a total of 75 dimensional features for the three channels.…”

Section: Feature Extractionmentioning

confidence: 99%

Estimation of Knee Extension Force Using Mechanomyography Signals Based on GRA and ICS-SVR

Gao

et al. 2022

Sensors

View full text Add to dashboard Cite

During lower-extremity rehabilitation training, muscle activity status needs to be monitored in real time to adjust the assisted force appropriately, but it is a challenging task to obtain muscle force noninvasively. Mechanomyography (MMG) signals offer unparalleled advantages over sEMG, reflecting the intention of human movement while being noninvasive. Therefore, in this paper, based on MMG, a combined scheme of gray relational analysis (GRA) and support vector regression optimized by an improved cuckoo search algorithm (ICS-SVR) is proposed to estimate the knee joint extension force. Firstly, the features reflecting muscle activity comprehensively, such as time-domain features, frequency-domain features, time–frequency-domain features, and nonlinear dynamics features, were extracted from MMG signals, and the relational degree was calculated using the GRA method to obtain the correlation features with high relatedness to the knee joint extension force sequence. Then, a combination of correlated features with high relational degree was input into the designed ICS-SVR model for muscle force estimation. The experimental results show that the evaluation indices of the knee joint extension force estimation obtained by the combined scheme of GRA and ICS-SVR were superior to other regression models and could estimate the muscle force with higher estimation accuracy. It is further demonstrated that the proposed scheme can meet the need of muscle force estimation required for rehabilitation devices, powered prostheses, etc.

show abstract

“…In this method, the extreme point of joint angular velocity is taken as the candidate point of the segmentation rhythm, and then it is refitted into the action characteristic curve according to the distribution of the candidate points [ 15 ]. In terms of music feature analysis, Xue et al comprehensively analyzed the rhythm of music with features such as note onset, chord change, and drum pattern and used information such as music playback speed and tempo as the main music features [ 16 ]. In order to synthesize dance movements, Zhu et al first analyzed the music and extracted the music beats, and then, based on the original movement database, they used the movement generation method to generate new movement data.…”

Section: Related Workmentioning

confidence: 99%

Matching Model of Dance Movements and Music Rhythm Features Using Human Posture Estimation

Jiang

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

The essential issue in music understanding and dance synthesis research is how to improve the degree of matching between dance motions and music rhythm elements. The matching model of dance movements and music rhythm features is created in this study based on human posture estimation research to tackle the problems that existing matching methods are prone to rapid changes in movements and cannot keep the original movement features. The rhythm properties of movement and music data are first analyzed, and then the degree of matching between movement and music pieces is measured using a dynamic time warping technique. A constraint-based dynamic planning approach is also used to synthesize the dance action sequence that best matches the supplied music. Experiments reveal that this model has a matching accuracy of 95.1 percent, which is higher than the two comparative models’ matching accuracy of 5.2 percent and 7.6 percent, respectively. The matching score of this paper is high, reaching around 94 percent, according to the user research results. The method suggested in this work has apparent advantages in that it may efficiently help users in the arrangement of desired dance moves or backing music, and it can be applied in the field of actual choreography and scoring.

show abstract

SEMG-Based Human In-Hand Motion Recognition Using Nonlinear Time Series Analysis and Random Forest

Cited by 20 publications

References 26 publications

Highly Robust and Wearable Facial Expression Recognition via Deep-Learning-Assisted, Soft Epidermal Electronics

Highly Robust and Wearable Facial Expression Recognition via Deep-Learning-Assisted, Soft Epidermal Electronics

Estimation of Knee Extension Force Using Mechanomyography Signals Based on GRA and ICS-SVR

Matching Model of Dance Movements and Music Rhythm Features Using Human Posture Estimation

Contact Info

Product

Resources

About