ArticleSensors (Basel, Switzerland)2020
Motion Inference Using Sparse Inertial Sensors, Self-Supervised Learning, and a New Dataset of Unscripted Human Motion.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Motion-Driven Automatic IMU Orientation Calibration viaSensors (Basel, Switzerland) · 2026Article
- SparseTrack: A Physics-Informed Transformer Framework for Real-Time Human Motion Reconstruction from Sparse IMUs.Sensors (Basel, Switzerland) · 2026Article
- The Utah Manipulation and Locomotion of Large Objects (MeLLO) Data Library.Bioengineering (Basel, Switzerland) · 2025Article
- A method for blood pressure hydrostatic pressure correction using wearable inertial sensors and deep learning.Npj biosensing · 2025Article
- Human motion data expansion from arbitrary sparse sensors with shallow recurrent decoders.bioRxiv : the preprint server for biology · 2024Article
- Kinematic and Physiological Analysis of Medieval Combat Sport Using Motion Analysis, Blood Lactate Measurement, and Heart Rate Monitoring: A Case Study.Sensors (Basel, Switzerland) · 2024Article
- Fast Human Motion reconstruction from sparse inertial measurement units considering the human shape.Nature communications · 2024Article
- A Deep Learning Model with a Self-Attention Mechanism for Leg Joint Angle Estimation across Varied Locomotion Modes.Sensors (Basel, Switzerland) · 2023Article
- Using Artificial Intelligence for Assistance Systems to Bring Motor Learning Principles into Real World Motor Tasks.Sensors (Basel, Switzerland) · 2022Review
- Capturing Upper Body Kinematics and Localization with Low-Cost Sensors for Rehabilitation Applications.Sensors (Basel, Switzerland) · 2022Article
- Radar Recorded Child Vital Sign Public Dataset and Deep Learning-Based Age Group Classification Framework for Vehicular Application.Sensors (Basel, Switzerland) · 2021Article
- Dimensionality Reduction of Human Gait for Prosthetic Control.Frontiers in bioengineering and biotechnology · 2021Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
In recent years, wearable sensors have become common, with possible applications in biomechanical monitoring, sports and fitness training, rehabilitation, assistive devices, or human-computer interaction. Our goal was to achieve accurate kinematics estimates using a small number of sensors. To accomplish this, we introduced a new dataset (the Virginia Tech Natural Motion Dataset) of full-body human motion capture using XSens MVN Link that contains more than 40 h of unscripted daily life motion in the open world. Using this dataset, we conducted self-supervised machine learning to do kinematics inference: we predicted the complete kinematics of the upper body or full body using a reduced set of sensors (3 or 4 for the upper body, 5 or 6 for the full body). We used several sequence-to-sequence (Seq2Seq) and Transformer models for motion inference. We compared the results using four different machine learning models and four different configurations of sensor placements. Our models produced mean angular errors of 10-15 degrees for both the upper body and full body, as well as worst-case errors of less than 30 degrees. The dataset and our machine learning code are freely available.
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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.