Synergy-based knee angle estimation using kinematics of thigh

Liang, Feng-Yan; Gao, Fei; Liao, Wei-Hsin

doi:10.1016/j.gaitpost.2021.06.015

Cited by 14 publications

(9 citation statements)

References 20 publications

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“…The former records electrophysiological indicators of brain activity, while the latter captures walking motion to obtain close-range data. According to the relevant literature [21,22] , we install two inertial sensors and an EEG sensor on the thighs and head of each subject respectively after comprehensive consideration, which can realistically simulate the real scene of people putting their smartphones in their trouser pockets. Each inertial sensor (WIT Inc., CHN) has built-in ICM42605 (3-axis accelerometer and 3-axis gyroscope) and MMC3630 (3-axis magnetometer), which have the characteristics of small size, wearable, and low power consumption [23,24] .…”

Section: Data Collectionmentioning

confidence: 99%

Attention-Based CNN Fusion Model for Emotion Recognition During Walking Using Discrete Wavelet Transform on EEG and Inertial Signals

Zhao,

Guo,

Chen

et al. 2024

Big Data Min. Anal.

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Walking as a unique biometric tool conveys important information for emotion recognition. Individuals in different emotional states exhibit distinct walking patterns. For this purpose, this paper proposes a novel approach to recognizing emotion during walking using electroencephalogram (EEG) and inertial signals.Accurate recognition of emotion is achieved by training in an end-to-end deep learning fashion and taking into account multi-modal fusion. Subjects wear virtual reality head-mounted display (VR-HMD) equipment to immerse in strong emotions during walking. VR environment shows excellent imitation and experience ability, which plays an important role in awakening and changing emotions. In addition, the multi-modal signals acquired from EEG and inertial sensors are separately represented as virtual emotion images by discrete wavelet transform (DWT). These serve as input to the attention-based convolutional neural network (CNN) fusion model. The designed network structure is simple and lightweight while integrating the channel attention mechanism to extract and enhance features. To effectively improve the performance of the recognition system, the proposed decision fusion algorithm combines Critic method and majority voting strategy to determine the weight values that affect the final decision results. An investigation is made on the effect of diverse mother wavelet types and wavelet decomposition levels on model performance which indicates that the 2.2-order reverse biorthogonal (rbio2.2) wavelet with two-level decomposition has the best recognition performance.Comparative experiment results show that the proposed method outperforms other existing state-of-the-art works with an accuracy of 98.73%.

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Section: Data Collectionmentioning

confidence: 99%

Attention-Based CNN Fusion Model for Emotion Recognition During Walking Using Discrete Wavelet Transform on EEG and Inertial Signals

Zhao,

Guo,

Chen

et al. 2024

Big Data Min. Anal.

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show abstract

“…the joint or segment orientation or location. In this review, we found 26 works that use reference data, that can be obtained from a stereophotogrammetric system (17/26) [21], [27], [34], [49], [53], [54], [56], [58], [68], [69], [86], [90], [91], [103], [111], [114], [115], electro-goniometer and encoders (2/26) [71], [84] or inertial sensors (7/26) [29], [100], [109], [134], [147], [148], [155]. Fig.…”

Section: Adopted Algorithmsmentioning

confidence: 99%

“…Conversely, the approach in ML-based proposals focused on the whole-body posture is the optimization of the number of devices with the use of the socalled sparse IMUs, as in [29], [58], [147], [148], [155]. This approach is also used to monitor specific limbs, as legs, reducing the number of sensors [21], [100], [134].…”

Section: A General Trendsmentioning

confidence: 99%

Simultaneous exercise recognition and evaluation in prescribed routines: Approach to virtual coaches

Villa

Casillas-Pérez

Martín

et al. 2022

Expert Systems with Applications

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“…However, 5 IMUs were used. In [45], we compared our LSTM-based method with other existing methods that used different types or numbers of sensors and proved to have better esti-mation performance. Again, we also employed PCA-based linear regression and calculate simulation errors to have a fair comparison.…”

Section: Intralimb Synergy Modelingmentioning

confidence: 99%

Gait Synergy Analysis and Modeling on Amputees and Stroke Patients for Lower Limb Assistive Devices

Liang

Gao

Cao

et al. 2022

Sensors

Self Cite

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The concept of synergy has drawn attention and been applied to lower limb assistive devices such as exoskeletons and prostheses for improving human–machine interaction. A better understanding of the influence of gait kinematics on synergies and a better synergy-modeling method are important for device design and improvement. To this end, gait data from healthy, amputee, and stroke subjects were collected. First, continuous relative phase (CRP) was used to quantify their synergies and explore the influence of kinematics. Second, long short-term memory (LSTM) and principal component analysis (PCA) were adopted to model interlimb synergy and intralimb synergy, respectively. The results indicate that the limited hip and knee range of motions (RoMs) in stroke patients and amputees significantly influence their synergies in different ways. In interlimb synergy modeling, LSTM (RMSE: 0.798° (hip) and 1.963° (knee)) has lower errors than PCA (RMSE: 5.050° (hip) and 10.353° (knee)), which is frequently used in the literature. Further, in intralimb synergy modeling, LSTM (RMSE: 3.894°) enables better synergy modeling than PCA (RMSE: 10.312°). In conclusion, stroke patients and amputees perform different compensatory mechanisms to adapt to new interlimb and intralimb synergies different from healthy people. LSTM has better synergy modeling and shows a promise for generating trajectories in line with the wearer’s motion for lower limb assistive devices.

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Synergy-based knee angle estimation using kinematics of thigh

Cited by 14 publications

References 20 publications

Attention-Based CNN Fusion Model for Emotion Recognition During Walking Using Discrete Wavelet Transform on EEG and Inertial Signals

Attention-Based CNN Fusion Model for Emotion Recognition During Walking Using Discrete Wavelet Transform on EEG and Inertial Signals

Simultaneous exercise recognition and evaluation in prescribed routines: Approach to virtual coaches

Gait Synergy Analysis and Modeling on Amputees and Stroke Patients for Lower Limb Assistive Devices

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