Research graph
References from Feasibility of explainable machine learning for analyzing drop landing strategies: effects of landing height and fatigue. Local targets link to admitted publications; unresolved targets remain external evidence.
The effect of fatigue on dynamic knee valgus during landing tasks: a systematic review and meta-analysis
10.1186/s13102-025-01205-3 · 2025 · External reference
Drop landing as a neuromuscular fatigue assessment: A submaximal alternative to traditional monitoring methods
10.1519/jsc.0000000000005247 · 2026 · External reference
Reliability and validity of knee valgus angle calculation at single-leg drop landing by posture estimation using machine learning
10.1016/j.heliyon.2024.e36338 · 2024 · External reference
Impact of different landing heights on the contact force in the medial tibiofemoral compartment and the surrounding muscle force characteristics in drop jumps
10.1371/journal.pone.0307538 · 2024 · External reference
Mazzoleni MJ. Padua DA. Combining inertial sensors and machine learning to predict vGRF and knee biomechanics during a double limb jump landing task
10.3390/s21134383 · 2021 · External reference
AEpileptic seizure detection in EEG signals using deep learning: LSTM and bidirectional LSTM
10.1080/10255842.2025.2490136 · 2025 · External reference
Random forest versus logistic regression: a large-scale benchmark experiment
10.1186/s12859-018-2264-5 · 2018 · External reference
Combined effects of drop height and fatigue on landing mechanics in active females
10.1123/jab.2014-0190 · 2014 · External reference
Enhancing biomechanical machine learning with limited data: generating realistic synthetic posture data using generative artificial intelligence
10.3389/fbioe.2024.1350135 · 2024 · External reference
Deep learning for time series classification: a review
10.1007/s10618-019-00619-1 · 2019 · External reference
Effect of data and gap characteristics on the nonlinear calculation of motion during locomotor activities
10.1123/jab.2023-0283 · 2024 · External reference
Why do tree-based models still outperform deep learning on typical tabular data
10.52202/068431-0037 · 2022 · External reference
Effects of different peripheral fatigue protocol on lower limb biomechanical changes during landing and its impact on the risk of anterior cruciate ligament injury: a systematic review
10.3389/fbioe.2025.1587573 · 2025 · External reference
Ankle dorsiflexion range of motion is associated with kinematic but not kinetic variables related to bilateral drop-landing performance at various drop heights
10.1016/j.humov.2019.02.016 · 2019 · External reference
Short-term wind power forecasting based on an improved CNN-LSTM model
10.1007/s10791-026-09944-8 · 2026 · External reference
Railway dangerous goods transportation system risk identification: Comparisons among SVM, PSO-SVM, GA-SVM and GS-SVM
10.1016/j.asoc.2021.107541 · 2021 · External reference
Classification of rice varieties using hyperspectral imaging with multi-dimensional fusion convolutional neural networks
10.1016/j.jfca.2025.108389 · 2025 · External reference
Harnessing interpretable machine learning: SHapley Additive exPlanations (SHAP)-driven insights, transformative impact, and controversies in adsorption-based environmental remediation
10.1016/j.inoche.2026.116269 · 2026 · External reference
Leveraging explainable machine learning to identify gait biomechanical parameters associated with anterior cruciate ligament injury
10.1038/s41598-022-10666-2 · 2022 · External reference
Strategies to optimise machine learning classification performance when using biomechanical features
10.1016/j.jbiomech.2024.111998 · 2024 · External reference
Effects of fatigue on balance and ankle proprioception during drop landing among individuals with and without chronic ankle instability
10.1016/j.jbiomech.2022.111431 · 2023 · External reference
From local explanations to global understanding with explainable AI for trees
10.1038/s42256-019-0138-9 · 2020 · External reference
Muscle function during single leg landing
10.1038/s41598-022-15024-w · 2022 · External reference
Discriminative regularized input manifold for multilayer perceptron
10.1016/j.patcog.2024.110421 · 2024 · External reference
Lower extremity variability changes with drop landing height manipulations
10.1080/15438627.2017.1282353 · 2017 · External reference
Deep convolutional and LSTM recurrent neural networks for multimodal wearable activity recognition
10.3390/s16010115 · 2016 · External reference
Entry angle during jump landing changes biomechanical risk factors for ACL injury
10.1080/14763141.2022.2088399 · 2022 · External reference
A Perspective on explainable artificial intelligence methods: SHAP and LIME
10.1002/aisy.202400304 · 2025 · External reference
Lower limb joint coordination and coordination variability during landing: a scoping review
10.3390/app15095118 · 2025 · External reference
A seq-to-seq temporal convolutional network for volleyball jump monitoring using a waist-mounted IMU
10.1109/access.2025.3545560 · 2025 · External reference
Predictive models for delay in medical decision-making among older patients with acute ischemic stroke: a comparative study using logistic regression analysis and lightGBM algorithm
10.1186/s12889-024-18855-6 · 2024 · External reference
Support vector machine versus random forest for remote sensing image classification: A meta-analysis and systematic review
10.1109/jstars.2020.3026724 · 2020 · External reference
Bias corrections for Random Forest in regression using residual rotation
10.1016/j.jkss.2015.01.003 · 2015 · External reference
Research on feature selection methods based on random forest
10.17559/tv-20220823104912 · 2023 · External reference
Biomechanics of a drop landing: osteogenic stimulus measures may vary
10.22540/jmni-25-001 · 2025 · External reference
Effects of lower extremity muscle fatigue on knee loading during a forward drop jump to a vertical jump in female athletes
10.2478/hukin-2019-0122 · 2020 · External reference
Effects of two fatigue protocols on impact forces and lower extremity kinematics during drop landings: implications for noncontact anterior cruciate ligament injury
10.1155/2017/5690519 · 2017 · External reference
Explainable artificial intelligence for gait analysis: advances, pitfalls, and challenges - a systematic review
10.3389/fbioe.2025.1671344 · 2025 · External reference
A new method applied for explaining the landing patterns: Interpretability analysis of machine learning
10.1016/j.heliyon.2024.e26052 · 2024 · External reference
Changes in lower-limb biomechanics, soft tissue vibrations, and muscle activation during unanticipated bipedal landings
10.2478/hukin-2019-0003 · 2019 · External reference
Absorption function loss due to the history of previous ankle sprain explored by unsupervised machine learning
10.1016/j.gaitpost.2024.01.021 · 2024 · External reference
An algorithm for indoor positioning based on LightGBM plus ExtraTrees chaotic weighted ensemble: evaluation and comparison
10.1007/s11227-025-07291-x · 2025 · External reference
Optimizing stretch-shortening cycle performance: effects of drop height and landing strategy on lower-limb biomechanics in drop jumps
10.7717/peerj.19490 · 2025 · External reference
Effects of mental fatigue on biomechanical characteristics and risk associated with non-contact anterior cruciate ligament injuries during landing
10.3389/fbioe.2025.1582873 · 2025 · External reference
