ArticleSensors (Basel, Switzerland)2019
Single Inertial Sensor-Based Neural Networks to Estimate COM-COP Inclination Angle During Walking.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The Use of Wearable Sensors and Machine Learning Methods to Estimate Biomechanical Characteristics During Standing Posture or Locomotion: A Systematic Review.Sensors (Basel, Switzerland) · 2024Pooled it
- Combining Fast Orthogonal Search with Deep Learning to Improve Low-Cost IMU Signal Accuracy.Sensors (Basel, Switzerland) · 2026Article
- Article
- High-Knee-Flexion Posture Recognition Using Multi-Dimensional Dynamic Time Warping on Inertial Sensor Data.Sensors (Basel, Switzerland) · 2025Article
- Enhancing accuracy and convenience of golf swing tracking with a wrist-worn single inertial sensor.Scientific reports · 2024Article
- Recurrent Neural Network Methods for Extracting Dynamic Balance Variables during Gait from a Single Inertial Measurement Unit.Sensors (Basel, Switzerland) · 2023Article
- Real-Time Sensor-Embedded Neural Network for Human Activity Recognition.Sensors (Basel, Switzerland) · 2023Article
- Automatic Body Segment and Side Recognition of an Inertial Measurement Unit Sensor during Gait.Sensors (Basel, Switzerland) · 2023Article
- Evaluation of center of mass estimation for obese using statically equivalent serial chain.Scientific reports · 2022Article
- Machine Learning Strategies for Low-Cost Insole-Based Prediction of Center of Gravity during Gait in Healthy Males.Sensors (Basel, Switzerland) · 2022Article
- AI Prediction of Brain Signals for Human Gait Using BCI Device and FBG Based Sensorial Platform for Plantar Pressure Measurements.Sensors (Basel, Switzerland) · 2022Article
- Deep learning approach to estimate foot pressure distribution in walking with application for a cost-effective insole system.Journal of neuroengineering and rehabilitation · 2022Article
- Article
- Predicting vertical ground reaction forces from 3D accelerometry using reservoir computers leads to accurate gait event detection.Frontiers in sports and active living · 2022Article
- Prediction of Tool Forces in Manual Grinding Using Consumer-Grade Sensors and Machine Learning.Sensors (Basel, Switzerland) · 2021Article
- The Use of Synthetic IMU Signals in the Training of Deep Learning Models Significantly Improves the Accuracy of Joint Kinematic Predictions.Sensors (Basel, Switzerland) · 2021Article
- Review of Wearable Devices and Data Collection Considerations for Connected Health.Sensors (Basel, Switzerland) · 2021Review
- IMU Data and GPS Position Information Direct Fusion Based on LSTM.Sensors (Basel, Switzerland) · 2021Article
- Estimation of Human Center of Mass Position through the Inertial Sensors-Based Methods in Postural Tasks: An Accuracy Evaluation.Sensors (Basel, Switzerland) · 2021Article
- Acceleration Magnitude at Impact Following Loss of Balance Can Be Estimated Using Deep Learning Model.Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
A biomechanical understanding of gait stability is needed to reduce falling risk. As a typical parameter, the COM-COP (center of mass-center of pressure) inclination angle (IA) could provide valuable insight into postural control and balance recovery ability. In this study, an artificial neural network (ANN) model was developed to estimate COM-COP IA based on signals using an inertial sensor. Also, we evaluated how different types of ANN and the cutoff frequency of the low-pass filter applied to input signals could affect the accuracy of the model. An inertial measurement unit (IMU) including an accelerometer, gyroscope, and magnetometer sensors was fabricated as a prototype. The COM-COP IA was calculated using a 3D motion analysis system including force plates. In order to predict the COM-COP IA, a feed-forward ANN and long-short term memory (LSTM) network was developed. As a result, the feed-forward ANN showed a relative root-mean-square error (rRMSE) of 15% while the LSTM showed an improved accuracy of 9% rRMSE. Additionally, the LSTM displayed a stable accuracy regardless of the cutoff frequency of the filter applied to the input signals. This study showed that estimating the COM-COP IA was possible with a cheap inertial sensor system. Furthermore, the neural network models in this study can be implemented in systems to monitor the balancing ability of the elderly or patients with impaired balancing ability.
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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.