Research graph
References from Lower limb prosthetic users’ activity classification through real-world inertial sensing. Local targets link to admitted publications; unresolved targets remain external evidence.
Unresolved reference
2017 · External reference
Estimating the prevalence of limb loss in the United States: 2005 to 2050
10.1016/j.apmr.2007.11.005 · 2008 · External reference
Do you know your k-level? An interview with interim president CEO Sue Stout about the importance of amputees knowing their k-levels
2013 · External reference
10.23970/ahrqepccer213
10.23970/ahrqepccer213 · 2018 · External reference
The use of physical activity outcomes in rehabilitation interventions for lower limb amputees: A systematic review
10.33137/cpoj.v3i1.33931 · 2020 · External reference
Unresolved reference
2025 · External reference
Gait event detection on level ground and incline walking using a rate gyroscope
10.3390/s100605683 · 2010 · External reference
Gait event detection using inertial measurement units in people with transfemoral amputation: a comparative study
10.1007/s11517-019-02098-4 · 2020 · External reference
Rules-based real-time gait event detection algorithm for lower-limb prosthesis users during level-ground and ramp walking
10.3390/s22228888 · 2022 · External reference
Real-time gait event detection for lower limb amputees using a single wearable sensor
2016 · External reference
GT3X+ accelerometer placement affects the reliability of step-counts measured during running and pedal-revolution counts measured during bicycling
10.1080/02640414.2015.1096018 · 2016 · External reference
Design and implementation of IMU-based locomotion mode recognition system on Zynq SoC
10.1016/j.micpro.2023.104927 · 2023 · External reference
Novel feature extraction and locomotion mode classification using intelligent lower-limb prosthesis
10.3390/machines11020235 · 2023 · External reference
A Classification approach based on directed acyclic graph to predict locomotion activities with one inertial sensor on the thigh
10.1109/tmrb.2021.3075096 · 2021 · External reference
Comparison of machine learning and deep learning-based methods for locomotion mode recognition using a single inertial measurement unit
10.3389/fnbot.2022.923164 · 2022 · External reference
EMG and IMU data fusion for locomotion mode classification in transtibial amputees
10.3390/prosthesis5040085 · 2023 · External reference
IMU-based deep neural networks: Prediction of locomotor and transition intentions of an osseointegrated transfemoral amputee
10.1109/tnsre.2021.3086843 · 2021 · External reference
Adaptive locomotion transition recognition with wearable sensors for lower limb robotic prosthesis
10.1109/tmech.2023.3278315 · 2024 · External reference
10.3390/s21248377
10.3390/s21248377 · External reference
Machine learning approaches for activity recognition and/or activity prediction in locomotion assistive devices-a systematic review
10.3390/s20216345 · 2020 · External reference
A comparison of four neural networks algorithms on locomotion intention recognition of lower limb exoskeleton based on multi-source information
10.1007/s42235-023-00435-w · 2023 · External reference
Early prediction of lower limb prostheses locomotion mode transition based on terrain recognition
10.1109/jsen.2023.3320274 · 2023 · External reference
10.1109/icdl55364.2023.10364513
10.1109/icdl55364.2023.10364513 · External reference
Efficient environmental context prediction for lower limb prostheses
10.1109/tsmc.2021.3084036 · 2022 · External reference
Terrain-adaptive exoskeleton control with predictive gait mode recognition: A pilot study during level walking and stair ascent
10.1109/tmrb.2024.3349624 · 2024 · External reference
Ambilateral activity recognition and continuous adaptation with a powered knee-ankle prosthesis
10.1109/tro.2025.3539206 · 2025 · External reference
A smart terrain identification technique based on electromyography, ground reaction force, and machine learning for lower limb rehabilitation
10.3390/app10082638 · 2020 · External reference
A multimodal framework based on deep belief network for human locomotion intent prediction
10.1007/s13534-024-00351-w · 2024 · External reference
Multivariate CNN model for human locomotion activity recognition with a wearable exoskeleton robot
10.3390/bioengineering10091082 · 2023 · External reference
Enhancing Locomotion-Mode Recognition and Transition Prediction With (Bio)Mechanical Sensor Fusion for Intelligent Prosthetic Knees
10.1109/jbhi.2025.3583319 · 2025 · External reference
Real-time locomotion mode detection in individuals with transfemoral amputation and osseointegration
10.1186/s12984-025-01672-2 · 2025 · External reference
IMU-based locomotion mode identification for transtibial prostheses, orthoses, and exoskeletons
10.1109/tnsre.2020.2987155 · 2020 · External reference
10.1109/rcar.2018.8621732
10.1109/rcar.2018.8621732 · External reference
Identifying classifier input signals to predict a cross-slope during transtibial amputee walking
10.1371/journal.pone.0192950 · 2018 · External reference
Applying deep neural networks and inertial measurement unit in recognizing irregular walking differences in the real world
10.1016/j.apergo.2021.103414 · 2021 · External reference
10.1109/iros45743.2020.9341183
10.1109/iros45743.2020.9341183 · External reference
Improving accelerometry-based measurement of functional use of the upper extremity after stroke: machine learning versus counts threshold method
10.1177/1545968320962483 · 2020 · External reference
10.1109/mlds.2017.11
10.1109/mlds.2017.11 · External reference
Divergent component of motion-based gait intention detection method using motion information from single leg
2023 · External reference
Classifying the walking pattern of humans on different surfaces using convolutional features and shallow machine learning classifiers
2024 · External reference
FSM-HSVM-based locomotion mode recognition for exoskeleton robot
10.3390/app12115483 · 2022 · External reference
IMU-based classification of locomotion modes, transitions, and gait phases with convolutional recurrent neural networks
10.3390/s22228871 · 2022 · External reference
Locomotion mode recognition algorithm based on Gaussian mixture model using IMU sensors
10.3390/s21082785 · 2021 · External reference
Lower-limb kinematics and kinetics during continuously varying human locomotion
10.1038/s41597-021-01057-9 · 2021 · External reference
Human activity recognition of individuals with lower limb amputation in free-living conditions: A pilot study
10.3390/s21248377 · 2021 · External reference
Smartwatches can detect walker and cane use in older adults
10.1093/geroni/igz008 · 2019 · External reference
10.1007/978-3-319-41264-1_46
10.1007/978-3-319-41264-1_46 · 2016 · External reference
Effects of walking with a cane on frontal plane hip joint loading in patients with late-stage unilateral hip osteoarthritis
2022 · External reference
Effects of cane use on the kinematic and kinetic of lower-extremity joints in inexperienced users
10.1016/j.jbiomech.2022.111426 · 2023 · External reference
Gait and kinematics of the trunk and lower extremities in stair ascent using quadricane in healthy subjects
10.1016/j.gaitpost.2008.06.001 · 2009 · External reference
Unresolved reference
External reference
Normalization techniques in training DNNs: methodology, analysis and application
10.1109/tpami.2023.3250241 · 2023 · External reference
Machine learning based abnormal gait classification with IMU considering joint impairment
10.3390/s24175571 · 2024 · External reference
Using thematic analysis in psychology
10.1191/1478088706qp063oa · 2006 · External reference
Partial weight-bearing gait using conventional assistive devices
10.1016/j.apmr.2004.03.026 · 2005 · External reference
Evaluation of a novel biomechanics-informed walking frame, developed through a Knowledge Transfer Partnership between biomechanists and design engineers
10.1186/s12877-023-04443-7 · 2023 · External reference
