Transfer Learning for Knee Joint Angle Prediction

Updated 17 October 2025

The paper introduces a transfer-learning framework that minimizes subject-specific data needs by adapting pre-trained models from diverse biomechanical sources.
It employs CNNs, RNNs, and transformer architectures to integrate multi-modal sensor data, ensuring robust extraction of knee kinematics.
Comparative performance metrics demonstrate improved prediction accuracy and model adaptability for applications in prosthesis control and rehabilitation.

A transfer-learning framework for knee joint angle prediction enables the use of models pre-trained on existing biomechanical datasets to efficiently estimate knee kinematics for new subjects or new measurement scenarios, while minimizing the need for extensive subject-specific data collection. Such frameworks are essential in clinical assessment, rehabilitation robotics, and biomechanical analysis, where rapid model adaptation and cross-population generalizability are required.

1. Data Sources and Preprocessing

Transfer-learning frameworks leverage diverse data modalities and sources, including synthetic image datasets, biomedical signals (EMG, IMU), and full-body kinematic measurements. For image-based approaches, synthetic datasets are often generated using 3D human-body modeling software (MakeHuman, Blender), permitting systematic sampling of knee angles and precise annotation of geometric keypoints. For sensor-based approaches, datasets encompass multi-channel surface EMG, IMU-derived joint angles, and goniometric measurements. Data preprocessing typically involves filtering, segmentation via sliding windows, normalization, and artifact removal. Cross-domain dataset repurposing is also featured, such as training models for knee joint prediction on datasets originally collected for pathology diagnosis (Nazari et al., 2022).

2. Architectural Design and Model Transferability

Framework architectures are selected for their capacity to process complex input signals and extract transferable features:

Convolutional Neural Networks (CNN): Utilized in image-based frameworks for coordinate regression of joint keypoints (Bernardino et al., 2018), as well as in physics-informed musculoskeletal models where raw sEMG signals are mapped to predicted angles and forces via convolutional and fully-connected layers (Zhang et al., 2022).
Recurrent Neural Networks (RNN): LSTM or GRU-based networks process temporal EMG/IMU signals, with separate feature extraction and prediction stages (e.g., KinPreNet (Yi et al., 2021)). Bidirectional GRU with attention modules are also used for refining temporal joint angle sequences (Peng et al., 15 Jul 2025).
Transformer-Derived Architectures: TempoNet (Saoud et al., 2023), AEPM (Wang et al., 10 Apr 2024), and FocalGatedNet (Saoud et al., 2023) employ dynamic attention modules and hierarchical contextual processing to model long-term dependencies, with modular encoders/decoders supporting fine-tuning for new subjects or motion conditions.
Spatio-Temporal CNNs: For modeling sEMG-driven knee kinematic trajectories, encoders extract gait-wide invariant motion patterns, with auxiliary branches for amplitude and muscle activation filtering (Fu et al., 2023).

The most effective transfer-learning architectures feature modular separation of feature extractor (typically frozen or inherited) and task-specific prediction layers (adapted or fine-tuned per subject, domain, or sensor configuration).

3. Transfer Learning Strategies

Transfer is operationalized through staged adaptation:

Feature Inheritance: Pre-trained generic models contribute their feature extraction weights to individualized models, which are then fine-tuned on domain-specific or subject-specific data (Zhang et al., 2022, Mollahossein et al., 15 Oct 2025).
Fine-Tuning: Selected layers, especially those responsible for inference or regression of knee angles, are updated using limited samples from the target domain—often requiring only a few gait cycles for effective adaptation (Mollahossein et al., 15 Oct 2025).
Multi-stage Transfer: Transfer includes population-level retraining before final fine-tuning on new subjects or experimental conditions, facilitated by systematic reduction in learning rates and regularization to prevent catastrophic forgetting.
Physics-Informed Transfer: Integration of soft physical constraints (e.g., equations of motion) during transfer phase improves physiological plausibility and accelerates convergence (Zhang et al., 2022).
Decoupled Learning: Training common motion-pattern branches on broad datasets followed by amplitude or timing head adaptation promotes generalization across populations while allowing personalization (Fu et al., 2023).

4. Data Augmentation and Domain Generalization

Data augmentation ensures the transferred model generalizes beyond the idiosyncrasies of its source domain:

Synthetic Manipulations: Rotations, translations, flipping, and background randomization of synthetic images increase variability, though aggressive augmentations may degrade absolute accuracy (Bernardino et al., 2018).
Signal Domain Augmentation: Windowed filtering, stride-based segmentation, and noise suppression (e.g., using muscle activation masks) improve the robustness of sensor-driven predictions (Fu et al., 2023).
Domain Adaptation: Models trained on diagnostic datasets demonstrate validity for activity recognition provided suitable input representations and augmentation are employed (Nazari et al., 2022).
Integration of Multi-modal Inputs: Flexible architectures accept EMG, IMU, historical kinematic data, or interaction force inputs, with channel-wise attention mechanisms enabling robust sensor fusion for adaptation in device-based scenarios (Mollahossein et al., 15 Oct 2025).

5. Performance Metrics and Comparative Results

Prediction accuracy is evaluated systematically:

Model/Framework	Metric	Value	Context
InceptionV3 (Transfer)	Euclidean loss	Accurate coords	Real/unseen images (Bernardino et al., 2018)
KinPreNet (LSTM)	RMSE	~3.98°	Fused EMG/IMU (Yi et al., 2021)
Gradient Boosting (GB)	AUC	0.942	Raw knee angles (Nazari et al., 2022)
AEPM (Transformer)	RMSE	3.45° (walk)	Whole-body input (Wang et al., 10 Apr 2024)
Spatio-Temporal CNN	RMSE	3.03° (avg)	50ms ahead, sEMG (Fu et al., 2023)
TempoNet	MAE (200ms)	2.515°	Outperforms Transformer (Saoud et al., 2023)
CNN-LSTM + Transfer	NMAE	1.09–3.1%	SMLE exoskeleton (Mollahossein et al., 15 Oct 2025)

Comparative analysis indicates that attention mechanisms, dynamic focus modules, and hierarchical architectures systematically improve long-term prediction accuracy, temporal adaptation, and computational efficiency for real-time applications (Saoud et al., 2023, Saoud et al., 2023). Physics-informed losses, multi-input fusion, and feature decoupling further enhance performance in challenging inter-subject or device-interaction scenarios.

6. Applications and Clinical Implications

Transfer-learning for knee joint angle prediction is applicable to:

Exoskeleton and prosthesis control: Accurate, low-latency prediction supports feed-forward mechanisms that compensate mechanical delays and enable safe, adaptive assistance across locomotion modes (Saoud et al., 2023, Wang et al., 10 Apr 2024).
Clinical monitoring and rehabilitation: Rapid model personalization allows for tailored intervention with minimal data, particularly in pathological or post-injury gait (Mollahossein et al., 15 Oct 2025).
Biomechanical analysis: Holistic transformer-based models incorporating global joint synergies provide insight for sensor placement and movement metrics beyond isolated thigh or knee signals (Wang et al., 10 Apr 2024).
Marker-free pose estimation refinement: Joint angle modeling and temporal smoothing correct occlusion-induced artifacts and jitter in multi-camera or in-the-wild scenarios, improving outlier correction rates (Peng et al., 15 Jul 2025).

7. Implementation Challenges and Future Directions

Physiological Validity: Embedding physics-based constraints and biomechanical laws ensures model outputs remain interpretable and clinically plausible (Zhang et al., 2022).
Minimal Data Transfer: Continued development focuses on reducing the number of required gait cycles for effective fine-tuning, facilitating real-time adaptation for new subjects and device types (Mollahossein et al., 15 Oct 2025).
Cross-population and multimodal generalization: Strategies such as gait-pattern decoupling, attention-based sensor integration, and probabilistic modeling enhance adaptability to diverse populations, pathologies, and movement scenarios (Fu et al., 2023, Wang et al., 10 Apr 2024).
Public Availability: Recent frameworks offer open-source code for modular architectures such as TempoNet and AEPM, supporting direct benchmarking and rapid deployment (Saoud et al., 2023, Wang et al., 10 Apr 2024).

A plausible implication is that future models will further integrate whole-body dynamics, uncertainty quantification, and physics-based transfer, advancing both the reliability and adaptability of knee joint angle prediction for biomechanical and clinical deployment.