ArticleIEEE journal of biomedical and health informatics2019
Lower Limb Motion Estimation Using Ultrasound Imaging: A Framework for Assistive Device Control.
Article in IEEE journal of biomedical and health informatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Insights into motor impairment assessment using myographic signals with artificial intelligence: a scoping review.Biomedical engineering letters · 2025Review
- AnkleImage - An ultrafast ultrasound image dataset to understand the ankle joint muscle contractility.Scientific data · 2024Article
- Wearable sensing for understanding and influencing human movement in ecological contexts.Current opinion in biomedical engineering · 2023Article
- A comparison of point-tracking algorithms in ultrasound videos from the upper limb.Biomedical engineering online · 2023Article
- Controlling Upper Limb Prostheses Using Sonomyography (SMG): A Review.Sensors (Basel, Switzerland) · 2023Review
- A-Mode Ultrasound-Based Prediction of Transfemoral Amputee Prosthesis Walking Kinematics via an Artificial Neural Network.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2023Article
- A clustering-based method for estimating pennation angle from B-mode ultrasound images.Wearable technologies · 2023Article
- Deep-Learning to Map a Benchmark Dataset of Non-amputee Ambulation for Controlling an Open Source Bionic Leg.IEEE robotics and automation letters · 2022Article
- Fused ultrasound and electromyography-driven neuromuscular model to improve plantarflexion moment prediction across walking speeds.Journal of neuroengineering and rehabilitation · 2022Article
- Discernment on assistive technology for the care and support requirements of older adults and differently-abled individuals.Frontiers in public health · 2022Review
- A deep learning method to predict ankle joint moment during walking at different speeds with ultrasound imaging: A framework for assistive devices control.Wearable technologies · 2022Article
- Evaluating Electromyography and Sonomyography Sensor Fusion to Estimate Lower-Limb Kinematics Using Gaussian Process Regression.Frontiers in robotics and AI · 2022Article
- Ultrasound Measurement of Skeletal Muscle Contractile Parameters Using Flexible and Wearable Single-Element Ultrasonic Sensor.Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
objectivePowered assistive devices need improved control intuitiveness to enhance their clinical adoption. Therefore, the intent of individuals should be identified and the device movement should adhere to it. Skeletal muscles contract synergistically to produce defined lower limb movements, so unique contraction patterns in lower extremity musculature may provide a means of device joint control. Ultrasound (US) imaging enables direct measurement of the local deformation of muscle segments. Hence, the objective of this study was to assess the feasibility of using US to estimate human lower limb movements.
methodsA novel algorithm was developed to calculate US features of the rectus femoris muscle during a non-weight-bearing knee flexion/extension experiment by nine able-bodied subjects. Five US features of the skeletal muscle tissue were studied, namely thickness, angle between aponeuroses, pennation angle, fascicle length, and echogenicity. A multiscale ridge filter was utilized to extract the structures in the image and a random sample consensus (RANSAC) model was used to segment muscle aponeuroses and fascicles. A localization scheme further guided RANSAC to enable tracking in a US image sequence. Gaussian process regression models were trained using segmented features to estimate both knee joint angle and angular velocity.
resultsThe proposed segmentation-estimation approach could estimate knee joint angle and angular velocity with an average root mean square error value of 7.45° and 0.262 rad/s, respectively. The average processing rate was 3-6 frames/s that is promising toward real-time implementation.
conclusionExperimental results demonstrate the feasibility of using US to estimate human lower extremity motion. The ability of the algorithm to work in real time may enable the use of US as a neural interface for lower limb applications. SIGNIFICANCE: Intuitive intent recognition of human lower extremity movements using wearable US imaging may enable volitional assistive device control and enhance locomotor outcomes for those with mobility impairments.
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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.