A Transfer Learning Based Cross-Subject Generic Model for Continuous Estimation of Finger Joint Angles From a New User

Long, Yucheng; Geng, Yanjuan; Dai, Chenyun; Li, Guanglin

doi:10.1109/jbhi.2023.3234989

Cited by 9 publications

(6 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Firstly, we deliberately avoid unreasonable or poor data caused by collection errors in NinaproDB2. In comparison to the subject adversarial knowledge (SAK) transfer learning strategy proposed by Long et al (2023) , we adopted the more efficient RoFormer model as the main body of the cross-subject model and utilized the μ -law normalization method to achieve more accurate estimation. Furthermore, we also simplify the structure of the domain discriminator.…”

Section: Discussionmentioning

confidence: 99%

A rotary transformer cross-subject model for continuous estimation of finger joints kinematics and a transfer learning approach for new subjects

Lin,

2024

Front. Neurosci.

View full text Add to dashboard Cite

IntroductionSurface Electromyographic (sEMG) signals are widely utilized for estimating finger kinematics continuously in human-machine interfaces (HMI), and deep learning approaches are crucial in constructing the models. At present, most models are extracted on specific subjects and do not have cross-subject generalizability. Considering the erratic nature of sEMG signals, a model trained on a specific subject cannot be directly applied to other subjects. Therefore, in this study, we proposed a cross-subject model based on the Rotary Transformer (RoFormer) to extract features of multiple subjects for continuous estimation kinematics and extend it to new subjects by adversarial transfer learning (ATL) approach.MethodsWe utilized the new subject’s training data and an ATL approach to calibrate the cross-subject model. To improve the performance of the classic transformer network, we compare the impact of different position embeddings on model performance, including learnable absolute position embedding, Sinusoidal absolute position embedding, and Rotary Position Embedding (RoPE), and eventually selected RoPE. We conducted experiments on 10 randomly selected subjects from the NinaproDB2 dataset, using Pearson correlation coefficient (CC), normalized root mean square error (NRMSE), and coefficient of determination (R2) as performance metrics.ResultsThe proposed model was compared with four other models including LSTM, TCN, Transformer, and CNN-Attention. The results demonstrated that both in cross-subject and subject-specific cases the performance of RoFormer was significantly better than the other four models. Additionally, the ATL approach improves the generalization performance of the cross-subject model better than the fine-tuning (FT) transfer learning approach.DiscussionThe findings indicate that the proposed RoFormer-based method with an ATL approach has the potential for practical applications in robot hand control and other HMI settings. The model’s superior performance suggests its suitability for continuous estimation of finger kinematics across different subjects, addressing the limitations of subject-specific models.

show abstract

Section: Discussionmentioning

confidence: 99%

A rotary transformer cross-subject model for continuous estimation of finger joints kinematics and a transfer learning approach for new subjects

Lin,

2024

Front. Neurosci.

View full text Add to dashboard Cite

show abstract

“…Moreover, based on the fact that extracting highly correlated inter-subject features from multi-subject training can improve model generalizability, and current MB and MF methods' lengthy training times preclude the rapid deployment across subjects [68], the capability for effective transfer learning becomes crucial. Current transfer learning strategies include retraining only the fully connected layers in CNN, Bi-LSTM, and CNN-LSTM networks for parameter sharing [30], [140], [192], adjusting CNN weights based on domain-invariant features and loss functions in dual-stream CNN [101], as well as the subject adversarial knowledge (SAK) strategy in [152].…”

Section: ) Transfer Learningmentioning

confidence: 99%

Continuous Motion Intention Prediction Using sEMG for Upper-Limb Rehabilitation: A Systematic Review of Model-Based and Model-Free Approaches

Wei,

Zhang,

Xie

2024

IEEE Trans. Neural Syst. Rehabil. Eng.

View full text Add to dashboard Cite

Upper limb functional impairments persisting after stroke significantly affect patients' quality of life. Precise adjustment of robotic assistance levels based on patients' motion intention using sEMG signals is crucial for active rehabilitation. This paper systematically reviews studies over the past decade on continuous prediction of upper limb single joint and multi-joint combinations motion intention using Model-Based (MB) and Model-Free (MF) approaches, based on 186 relevant studies screened from six major electronic databases. The findings indicate ongoing challenges in terms of subject composition, algorithm robustness and generalization, and feasibility for practical applications. Moreover, it suggests integrating the strengths of both MB and MF approaches to improve existing algorithms. Therefore, future research should further explore personalized MB-MF combination methods incorporating deep learning, attention mechanisms, muscle synergy features, motor unit features, and closed-loop feedback to achieve precise, real-time, and long-duration prediction of multi-joint complex movements, while further refining the transfer learning strategy for rapid algorithm deployment across days and subjects. In summary, this review summarizes the current state and challenges of research, aiming to provide inspiration for future research on predicting upper limb motion intention based on sEMG.

show abstract

“…Recently, TL has been proven to be beneficial in classificationbased myoelectric control for model calibration (inter-session) [16], [18], for multi-subject models (subject-independent) [19], adapting a pretrained model to a previously unseen end-user with minimal data (cross-subject) [20], and success in other EMG-based applications [21]. Ameri et al demonstrated model calibration using TL, which lessened the performance degradation caused by confounding factors such as electrode shift and physiological parameters changing over multiple sessions [16], [18].…”

Section: Introductionmentioning

confidence: 99%

“…Campbell et al then extended this subjectindependent scenario to a cross-subject scenario by adapting to a novel end-user with minimal subject-supplied data, even outperforming the intra-subject scenario despite having less end-user data [20]. Likewise, Long et al have shown this TL strategy was valid outside EMG gesture recognition by outperforming within-subject continuous finger kinematic prediction models using subject adversarial transfer learning [21].…”

Section: Introductionmentioning

confidence: 99%

Generalizing Upper Limb Force Modeling With Transfer Learning: A Multimodal Approach Using EMG and IMU for New Users and Conditions

Hajian,

Campbell,

Ansari

et al. 2024

IEEE Trans. Neural Syst. Rehabil. Eng.

View full text Add to dashboard Cite

In the field of EMG-based force modeling, the ability to generalize models across individuals could play a significant role in its adoption across a range of applications, including assistive devices, robotic and rehabilitation devices. However, current studies have predominately focused on intra-subject modeling, largely neglecting the burden of end-user data acquisition. In this work, we propose the use of transfer learning (TL) to generalize force modeling to a new user by first establishing a baseline model trained using other users' data, and then adapting to the enduser using a small amount of new data (only 10%, 20%, and 40% of the new user data). Using a deep multimodal convolutional neural network, consisting of two CNN models, one with highdensity (HD) EMG and one with motion data recorded by an Inertial Measurement Unit (IMU), our proposed TL technique significantly improved force modeling compared to leave-one-subjectout (LOSO) and even intra-subject scenarios. The TL approach increased the average R squared values of the force modeling task by 60.81%, 190.53%, and 199.79% compared to the LOSO case, and by 13.4%, 36.88%, and 45.51% compared to the intra-subject case for isotonic, isokinetic and dynamic conditions, respectively. These results show that it is possible to adapt to a new user with minimal data while improving performance significantly compared to the intra-subject scenario. We also show that TL can be used to generalize on a new experimental condition for a new user.

show abstract

A Transfer Learning Based Cross-Subject Generic Model for Continuous Estimation of Finger Joint Angles From a New User

Cited by 9 publications

References 39 publications

A rotary transformer cross-subject model for continuous estimation of finger joints kinematics and a transfer learning approach for new subjects

A rotary transformer cross-subject model for continuous estimation of finger joints kinematics and a transfer learning approach for new subjects

Continuous Motion Intention Prediction Using sEMG for Upper-Limb Rehabilitation: A Systematic Review of Model-Based and Model-Free Approaches

Generalizing Upper Limb Force Modeling With Transfer Learning: A Multimodal Approach Using EMG and IMU for New Users and Conditions

Contact Info

Product

Resources

About