ArticleMedical & biological engineering & computing2021
Classifying sitting, standing, and walking using plantar force data.
Article in Medical & biological engineering & computing, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed, 1 synthesis or guideline pooled it, 25 citations in OpenAlex.
- Human Posture Estimation: A Systematic Review on Force-Based Methods-Analyzing the Differences in Required Expertise and Result Benefits for Their Utilization.Sensors (Basel, Switzerland) · 2023Pooled it
- A multi-pseudo-sensor fusion approach to estimating the lower limb joint moments based on deep neural network.Medical & biological engineering & computing · 2025Article
- Identifying changes in dynamic plantar pressure associated with radiological knee osteoarthritis based on machine learning and wearable devices.Journal of neuroengineering and rehabilitation · 2024Article
- HUMANISE: Human-Inspired Smart Management, towards a Healthy and Safe Industrial Collaborative Robotics.Sensors (Basel, Switzerland) · 2023Article
- A Pilot Study of Plantar Mechanics Distributions and Fatigue Profiles after Running on a Treadmill: Using a Support Vector Machine Algorithm.Journal of healthcare engineering · 2023Article
- EEG Microstate Features as an Automatic Recognition Model of High-Density Epileptic EEG Using Support Vector Machine.Brain sciences · 2022Article
- Evaluation of an ankle-foot orthosis effect on gait transitional stability during ramp ascent/descent.Medical & biological engineering & computing · 2022Article
- Assessing Impact of Sensors and Feature Selection in Smart-Insole-Based Human Activity Recognition.Methods and protocols · 2022Article
- The Development and Concurrent Validity of a Multi-Sensor-Based Frailty Toolkit for In-Home Frailty Assessment.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
Prolonged static weight-bearing at work may increase the risk of developing plantar fasciitis (PF). However, to establish a causal relationship between weight-bearing and PF, a low-cost objective measure of workplace behaviors is needed. This proof-of-concept study assesses the classification accuracy and sensitivity of low-resolution plantar pressure measurements in distinguishing workplace postures. Plantar pressure was measured using an in-shoe measurement system in eight healthy participants while sitting, standing, and walking. Data was resampled to simulate on/off characteristics of 24 plantar force sensitive resistors. The top 10 sensors were evaluated using leave-one-out cross-validation with machine learning algorithms: support vector machines (SVMs), decision tree (DT), discriminant analysis (DA), and k-nearest neighbors (KNN). SVM and DT best classified sitting, standing, and walking. High classification accuracy was obtained with five sensors (98.6% and 99.1% accuracy, respectively) and even a single sensor (98.4% and 98.4%, respectively). The central forefoot and the medial and lateral midfoot were the most important classification sensor locations. On/off plantar pressure measurements in the midfoot and central forefoot can accurately classify workplace postures. These results provide the foundation for a low-cost objective tool to classify and quantify sedentary workplace postures.
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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.