ArticleSensors (Basel, Switzerland)2021
The Role of Surface Electromyography in Data Fusion with Inertial Sensors to Enhance Locomotion Recognition and Prediction.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed, 38 citations in OpenAlex.
- Multi-phase and Multi-mode Gait Recognition with Few Channels and Few Features Based on the Fusion of sEMG and Acceleration.Annals of biomedical engineering · 2026Article
- Multimodal Information Fusion for Control of Rehabilitation Robots in Motor Dysfunction: A Review.Bioengineering (Basel, Switzerland) · 2026Review
- Multimodal machine learning mobility assessment in Parkinson's disease within supervised and unsupervised settings.Journal of neuroengineering and rehabilitation · 2026Article
- Subject-independent gait activity recognition using DSAF: dual-stream IMU-EMG attention fusion with asymmetric temporal encoding and physiological complementarity weighting.Frontiers in neurorobotics · 2026Article
- Real-time locomotion mode detection in individuals with transfemoral amputation and osseointegration.Journal of neuroengineering and rehabilitation · 2025Article
- Editorial: Assessment and monitoring of human movement.Frontiers in physiology · 2025Article
- Deep Learning and IoT-Based Ankle-Foot Orthosis for Enhanced Gait Optimization.Healthcare (Basel, Switzerland) · 2024Article
- A SE-DenseNet-LSTM model for locomotion mode recognition in lower limb exoskeleton.PeerJ. Computer science · 2024Article
- A hybrid linear discriminant analysis and genetic algorithm to create a linear model of aging when performing motor tasks through inertial sensors positioned on the hand and forearm.Biomedical engineering online · 2023Article
- Low limb prostheses and complex human prosthetic interaction: A systematic literature review.Frontiers in robotics and AI · 2023Review
- Human lower limb activity recognition techniques, databases, challenges and its applications using sEMG signal: an overview.Biomedical engineering letters · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
Locomotion recognition and prediction is essential for real-time human-machine interactive control. The integration of electromyography (EMG) with mechanical sensors could improve the performance of locomotion recognition. However, the potential of EMG in motion prediction is rarely discussed. This paper firstly investigated the effect of surface EMG on the prediction of locomotion while integrated with inertial data. We collected EMG signals of lower limb muscle groups and linear acceleration data of lower limb segments from ten healthy participants in seven locomotion activities. Classification models were built based on four machine learning methods-support vector machine (SVM), k-nearest neighbor (KNN), artificial neural network (ANN), and linear discriminant analysis (LDA)-where a major vote strategy and a content constraint rule were utilized for improving the online performance of the classification decision. We compared four classifiers and further investigated the effect of data fusion on the online locomotion classification. The results showed that the SVM model with a sliding window size of 80 ms achieved the best recognition performance. The fusion of EMG signals does not only improve the recognition accuracy of steady-state locomotion activity from 90% (using acceleration data only) to 98% (using data fusion) but also enables the prediction of the next steady locomotion (∼370 ms). The study demonstrates that the employment of EMG in locomotion recognition could enhance online prediction performance.
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.