ArticleFrontiers in bioengineering and biotechnology2020
A Machine Learning Approach to Estimate Hip and Knee Joint Loading Using a Mobile Phone-Embedded IMU.
Article in Frontiers in bioengineering and biotechnology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Inertial Motion Capture-Based Wearable Systems for Estimation of Joint Kinetics: A Systematic Review.Sensors (Basel, Switzerland) · 2022Pooled it
- Digital Twins in Orthopedics and Trauma: Concepts, Emerging Evidence, and Barriers to Clinical Translation.Journal of clinical medicine · 2026Review
- Comparison of deep and conventional machine learning methods in predicting joint moments in patients with cerebral palsy.Medical & biological engineering & computing · 2026Article
- Prediction of joint moments from kinematics using machine learning in children with congenital talipes equino varus and typically developing peers.Journal of orthopaedics · 2024Article
- Sensor-Based Wearable Systems for Monitoring Human Motion and Posture: A Review.Sensors (Basel, Switzerland) · 2023Review
- Estimation of Lower Limb Joint Angles and Joint Moments during Different Locomotive Activities Using the Inertial Measurement Units and a Hybrid Deep Learning Model.Sensors (Basel, Switzerland) · 2023Article
- Wearable sensor and machine learning estimate tendon load and walking speed during immobilizing boot ambulation.Scientific reports · 2023Article
- A Neural Network Approach for Inertial Measurement Unit-Based Estimation of Three-Dimensional Spinal Curvature.Sensors (Basel, Switzerland) · 2023Article
- Peak Tibiofemoral Contact Forces Estimated Using IMU-Based Approaches Are Not Significantly Different from Motion Capture-Based Estimations in Patients with Knee Osteoarthritis.Sensors (Basel, Switzerland) · 2023Article
- Extended Application of Inertial Measurement Units in Biomechanics: From Activity Recognition to Force Estimation.Sensors (Basel, Switzerland) · 2023Review
- IMU-Based Monitoring for Assistive Diagnosis and Management of IoHT: A Review.Healthcare (Basel, Switzerland) · 2022Review
- Inertial Measurement Units and Application for Remote Health Care in Hip and Knee Osteoarthritis: Narrative Review.JMIR rehabilitation and assistive technologies · 2022Review
- Inertial Sensor-to-Segment Calibration for Accurate 3D Joint Angle Calculation for Use in OpenSim.Sensors (Basel, Switzerland) · 2022Article
- Biomechanics beyond the lab: Remote technology for osteoarthritis patient data-A scoping review.Frontiers in rehabilitation sciences · 2022Article
- Wearable Inertial Sensors for Gait Analysis in Adults with Osteoarthritis-A Scoping Review.Sensors (Basel, Switzerland) · 2020Article
- Towards the Monitoring of Functional Status in a Free-Living Environment for People with Hip or Knee Osteoarthritis: Design and Evaluation of the JOLO Blended Care App.Sensors (Basel, Switzerland) · 2020Article
- Real-Time Prediction of Joint Forces by Motion Capture and Machine Learning.Sensors (Basel, Switzerland) · 2020Article
- Validity of a New 3-D Motion Analysis Tool for the Assessment of Knee, Hip and Spine Joint Angles during the Single Leg Squat.Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Hip osteoarthritis patients exhibit changes in kinematics and kinetics that affect joint loading. Monitoring this load can provide valuable information to clinicians. For example, a patient's joint loading measured across different activities can be used to determine the amount of exercise that the patient needs to complete each day. Unfortunately, current methods for measuring joint loading require a lab environment which most clinicians do not have access to. This study explores employing machine learning to construct a model that can estimate joint loading based on sensor data obtained solely from a mobile phone. In order to learn such a model, we collected a dataset from 10 patients with hip osteoarthritis who performed multiple repetitions of nine different exercises. During each repetition, we simultaneously recorded 3D motion capture data, ground reaction force data, and the inertial measurement unit data from a mobile phone attached to the patient's hip. The 3D motion and ground reaction force data were used to compute the ground truth joint loading using musculoskeletal modeling. Our goal is to estimate the ground truth loading value using only the data captured by the sensors of the mobile phone. We propose a machine learning pipeline for learning such a model based on the recordings of a phone's accelerometer and gyroscope. When evaluated for an unseen patient, the proposed pipeline achieves a mean absolute error of 29% for the left hip and 36% for the right hip. While our approach is a step in the direction of using a minimal number of sensors to estimate joint loading outside the lab, developing a tool that is accurate enough to be applicable in a clinical context still remains an open challenge. It may be necessary to use sensors at more than one location in order to obtain better estimates.
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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.