ArticleSensors (Basel, Switzerland)2021
An Intelligent In-Shoe System for Gait Monitoring and Analysis with Optimized Sampling and Real-Time Visualization Capabilities.
Article in Sensors (Basel, Switzerland), 2021. 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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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.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it, 35 citations in OpenAlex.
- Advancements in Wearable Sensor Technologies for Health Monitoring in Terms of Clinical Applications, Rehabilitation, and Disease Risk Assessment: Systematic Review.JMIR mHealth and uHealth · 2026Pooled it
- A Smart Shoe System for Gait Analysis and Remote Monitoring in Parkinson's Disease-A Validation Study.Sensors (Basel, Switzerland) · 2026Article
- Swing-Phase Plantar Stimulation and Gait Parameters in Patients With Diabetic Neuropathy: A Preliminary Study.Foot & ankle orthopaedics · 2026Article
- Detecting artificially impaired balance in human locomotion: metrics, perturbation effects and detection thresholds.The Journal of experimental biology · 2025Article
- Enhancing Intelligent Shoes with Gait Analysis: A Review on the Spatiotemporal Estimation Techniques.Sensors (Basel, Switzerland) · 2024Review
- [Commercial devices for monitoring symptoms in Parkinson's disease: benefits, limitations and trends].Revista de neurologia · 2024Review
- Adaptive Detection in Real-Time Gait Analysis through the Dynamic Gait Event Identifier.Bioengineering (Basel, Switzerland) · 2024Article
- Recent Innovations in Footwear and the Role of Smart Footwear in Healthcare-A Survey.Sensors (Basel, Switzerland) · 2024Review
- Imbalance and Falls in Patients with Parkinson's Disease: Causes and Recent Developments in Training and Sensor-Based Assessment.Brain sciences · 2024Review
- Commercial symptom monitoring devices in Parkinson's disease: benefits, limitations, and trends.Frontiers in neurology · 2024Review
- Accuracy, concurrent validity, and test-retest reliability of pressure-based insoles for gait measurement in chronic stroke patients.Frontiers in digital health · 2024Article
- The Influence of Stride Selection on Gait Parameters Collected with Inertial Sensors.Sensors (Basel, Switzerland) · 2023Article
- Impact of Gait Events Identification through Wearable Inertial Sensors on Clinical Gait Analysis of Children with Idiopathic Toe Walking.Micromachines · 2023Article
- Article
- Accuracy of gait and posture classification using movement sensors in individuals with mobility impairment after stroke.Frontiers in physiology · 2022Article
- Absolute Reliability of Gait Parameters Acquired With Markerless Motion Capture in Living Domains.Frontiers in human neuroscience · 2022Article
- Human gait-labeling uncertainty and a hybrid model for gait segmentation.Frontiers in neuroscience · 2022Article
- Gait as a vital sign: integrating wearables and AI into vestibular and balance medicine.Frontiers in neurologyReview
Corrections and comments
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Authors and funding
12 authors at 4 institutions in 2 countries.
Funding
Abstract
The deterioration of gait can be used as a biomarker for ageing and neurological diseases. Continuous gait monitoring and analysis are essential for early deficit detection and personalized rehabilitation. The use of mobile and wearable inertial sensor systems for gait monitoring and analysis have been well explored with promising results in the literature. However, most of these studies focus on technologies for the assessment of gait characteristics, few of them have considered the data acquisition bandwidth of the sensing system. Inadequate sampling frequency will sacrifice signal fidelity, thus leading to an inaccurate estimation especially for spatial gait parameters. In this work, we developed an inertial sensor based in-shoe gait analysis system for real-time gait monitoring and investigated the optimal sampling frequency to capture all the information on walking patterns. An exploratory validation study was performed using an optical motion capture system on four healthy adult subjects, where each person underwent five walking sessions, giving a total of 20 sessions. Percentage mean absolute errors (MAE%) obtained in stride time, stride length, stride velocity, and cadence while walking were 1.19%, 1.68%, 2.08%, and 1.23%, respectively. In addition, an eigenanalysis based graphical descriptor from raw gait cycle signals was proposed as a new gait metric that can be quantified by principal component analysis to differentiate gait patterns, which has great potential to be used as a powerful analytical tool for gait disorder diagnostics.
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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.