ArticleSensors (Basel, Switzerland)2023
Estimation of Lower Limb Joint Angles and Joint Moments during Different Locomotive Activities Using the Inertial Measurement Units and a Hybrid Deep Learning Model.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed.
- IMU- and Vision-Based Measurement Techniques for Joint Kinematics: A Narrative Review.Sensors (Basel, Switzerland) · 2026Review
- A Joint-Level Hybrid Framework for Gait Analysis Using Camera-IMU Fusion and LSTM-Based Temporal Correction.Sensors (Basel, Switzerland) · 2026Article
- Sagittal-Plane Knee Flexion Moment Estimation Using a Lightweight Deep Learning Framework Based on Sequential Surface EMG Feature Frames.Sensors (Basel, Switzerland) · 2026Article
- Individual Variability in Deep Learning-Based Joint Angle Estimation from a Single IMU: A Cross-Population Study.Sensors (Basel, Switzerland) · 2025Article
- Deep Learning Models Optimization for Gait Phase Identification from EMG Data During Exoskeleton-Assisted Walking.Biomimetics (Basel, Switzerland) · 2025Article
- Multijoint Continuous Motion Estimation for Human Lower Limb Based on Surface Electromyography.Sensors (Basel, Switzerland) · 2025Article
- Anthropometry and diagnostic aware deep learning for exercise assessment.Frontiers in medical technology · 2025Article
- Estimation of lower limb torque: a novel hybrid method based on continuous wavelet transform and deep learning approach.PeerJ. Computer science · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
Using inertial measurement units (IMUs) to estimate lower limb joint kinematics and kinetics can provide valuable information for disease diagnosis and rehabilitation assessment. To estimate gait parameters using IMUs, model-based filtering approaches have been proposed, such as the Kalman filter and complementary filter. However, these methods require special calibration and alignment of IMUs. The development of deep learning algorithms has facilitated the application of IMUs in biomechanics as it does not require particular calibration and alignment procedures of IMUs in use. To estimate hip/knee/ankle joint angles and moments in the sagittal plane, a subject-independent temporal convolutional neural network-bidirectional long short-term memory network (TCN-BiLSTM) model was proposed using three IMUs. A public benchmark dataset containing the most representative locomotive activities in daily life was used to train and evaluate the TCN-BiLSTM model. The mean Pearson correlation coefficient of joint angles and moments estimated by the proposed model reached 0.92 and 0.87, respectively. This indicates that the TCN-BiLSTM model can effectively estimate joint angles and moments in multiple scenarios, demonstrating its potential for application in clinical and daily life scenarios.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.