ReviewSensors (Basel, Switzerland)2023
Extended Application of Inertial Measurement Units in Biomechanics: From Activity Recognition to Force Estimation.
Review in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Electronic Systems for Monitoring Pediatric Gait Biomechanical Parameters: A Systematic Review of Embedded Technologies and Human-Machine Interfaces.Sensors (Basel, Switzerland) · 2026Pooled it
- Multimodal algorithmic fusion model for physical education assessment: spatiotemporal comparison of inertial measurement units and traditional scales.BMC sports science, medicine & rehabilitation · 2026Article
- Spatiotemporal attention-based dance motion recognition and intelligent correction system using 3D motion capture.Scientific reports · 2026Article
- Wearable technology for athletes: material innovations, performance monitoring, and emerging paradigms.Mikrochimica acta · 2026Review
- Uniplanar aquatic exercise quantified with inertial sensors and pose estimation.Scientific reports · 2025Article
- Cycle based state of health estimation of lithium ion cells using deep learning architectures.Scientific reports · 2025Article
- AI and Smart Devices in Cardio-Oncology: Advancements in Cardiotoxicity Prediction and Cardiovascular Monitoring.Diagnostics (Basel, Switzerland) · 2025Review
- Application of Smart Watch-Based Functional Evaluation for Upper Extremity Impairment: A Preliminary Study on Older Emirati Stroke Population.Sensors (Basel, Switzerland) · 2025Article
- Detection of sleep apnea using only inertial measurement unit signals from apple watch: a pilot-study with machine learning approach.Sleep & breathing = Schlaf & Atmung · 2025Article
- Sensor-based technologies for motion analysis in sports injuries: a scoping review.BMC sports science, medicine & rehabilitation · 2025Article
- Estimation of lower limb torque: a novel hybrid method based on continuous wavelet transform and deep learning approach.PeerJ. Computer science · 2025Article
- Improving Biological Joint Moment Estimation During Real-World Tasks With EMG and Instrumented Insoles.IEEE transactions on bio-medical engineering · 2024Article
- Evaluating the Performance of Joint Angle Estimation Algorithms on an Exoskeleton Mock-Up via a Modular Testing Approach.Sensors (Basel, Switzerland) · 2024Article
- Recent Advances in Wearable Healthcare Devices: From Material to Application.Bioengineering (Basel, Switzerland) · 2024Review
- The Potential Role of Wearable Inertial Sensors in Laboring Women with Walking Epidural Analgesia.Sensors (Basel, Switzerland) · 2024Article
- Predicting Leg Forces and Knee Moments Using Inertial Measurement Units: An In Vitro Study.Journal of biomechanical engineering · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Abnormal posture or movement is generally the indicator of musculoskeletal injuries or diseases. Mechanical forces dominate the injury and recovery processes of musculoskeletal tissue. Using kinematic data collected from wearable sensors (notably IMUs) as input, activity recognition and musculoskeletal force (typically represented by ground reaction force, joint force/torque, and muscle activity/force) estimation approaches based on machine learning models have demonstrated their superior accuracy. The purpose of the present study is to summarize recent achievements in the application of IMUs in biomechanics, with an emphasis on activity recognition and mechanical force estimation. The methodology adopted in such applications, including data pre-processing, noise suppression, classification models, force/torque estimation models, and the corresponding application effects, are reviewed. The extent of the applications of IMUs in daily activity assessment, posture assessment, disease diagnosis, rehabilitation, and exoskeleton control strategy development are illustrated and discussed. More importantly, the technical feasibility and application opportunities of musculoskeletal force prediction using IMU-based wearable devices are indicated and highlighted. With the development and application of novel adaptive networks and deep learning models, the accurate estimation of musculoskeletal forces can become a research field worthy of further attention.
Indexed as
Identifiers
What Socratic holds
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 Socratic graph.