ArticleJournal of the Royal Society, Interface2021
Revealing the unique features of each individual's muscle activation signatures.
Article in Journal of the Royal Society, Interface, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it, 32 citations in OpenAlex.
- Explainable artificial intelligence for gait analysis: advances, pitfalls, and challenges - a systematic review.Frontiers in bioengineering and biotechnology · 2025Pooled it
- Can Machines Identify Pain Effects? A Machine Learning Proof of Concept to Identify EMG Pain Signature.Bioengineering (Basel, Switzerland) · 2026Article
- Software for dataset-wide XAI: From local explanations to global insights with Zennit, CoRelAy, and ViRelAy.PloS one · 2026Article
- Insights into motor impairment assessment using myographic signals with artificial intelligence: a scoping review.Biomedical engineering letters · 2025Review
- Quantifying the diverse contributions of hierarchical muscle interactions to motor function.iScience · 2025Article
- A new method applied for explaining the landing patterns: Interpretability analysis of machine learning.Heliyon · 2024Article
- Identification of subject-specific responses to footwear during running.Scientific reports · 2023Article
- Too much information is no information: how machine learning and feature selection could help in understanding the motor control of pointing.Frontiers in big data · 2023Article
- Modeling biological individuality using machine learning: A study on human gait.Computational and structural biotechnology journal · 2023Article
- Identifying underlying individuality across running, walking, and handwriting patterns with conditional cycle-consistent generative adversarial networks.Frontiers in bioengineering and biotechnology · 2023Article
- Exploring the EMG transient: the muscular activation sequences used as novel time-domain features for hand gestures classification.Frontiers in neurorobotics · 2023Article
- High intra-task and low inter-task correlations of motor skills in humans creates an individualized behavioural pattern.Scientific reports · 2022Article
- Hamstring muscle activation strategies during eccentric contractions are related to the distribution of muscle damage.Scandinavian journal of medicine & science in sports · 2022Article
- Regional variation in lateral and medial gastrocnemius muscle fibre lengths obtained from diffusion tensor imaging.Journal of anatomy · 2022Article
- Isolating the Unique and Generic Movement Characteristics of Highly Trained Runners.Sensors (Basel, Switzerland) · 2021Article
- Gutenberg Gait Database, a ground reaction force database of level overground walking in healthy individuals.Scientific data · 2021Article
- Inter-Person Differences in Isometric Coactivations of Triceps Surae and Tibialis Anterior Decrease in Young, but Not in Older Adults After 14 Days of Bed Rest.Frontiers in physiology · 2021Article
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 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
There is growing evidence that each individual has unique movement patterns, or signatures. The exact origin of these movement signatures, however, remains unknown. We developed an approach that can identify individual muscle activation signatures during two locomotor tasks (walking and pedalling). A linear support vector machine was used to classify 78 participants based on their electromyographic (EMG) patterns measured on eight lower limb muscles. To provide insight into decision-making by the machine learning classification model, a layer-wise relevance propagation (LRP) approach was implemented. This enabled the model predictions to be decomposed into relevance scores for each individual input value. In other words, it provided information regarding which features of the time-varying EMG profiles were unique to each individual. Through extensive testing, we have shown that the LRP results, and by extent the activation signatures, are highly consistent between conditions and across days. In addition, they are minimally influenced by the dataset used to train the model. Additionally, we proposed a method for visualizing each individual's muscle activation signature, which has several potential clinical and scientific applications. This is the first study to provide conclusive evidence of the existence of individual muscle activation signatures.
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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.