ArticleIEEE journal of biomedical and health informatics2014
Highly accurate recognition of human postures and activities through classification with rejection.
Article in IEEE journal of biomedical and health informatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 3 of them syntheses 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed, 3 syntheses or guidelines pooled it, 70 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
- Estimating Biomechanical Time-Series with Wearable Sensors: A Systematic Review of Machine Learning Techniques.Sensors (Basel, Switzerland) · 2019Pooled it
- Progress in Biomedical Knowledge Discovery: A 25-year Retrospective.Yearbook of medical informatics · 2016Pooled it
- Classification of daily activities using a wireless instrumented insole (WalkinSense) in a semi free-living setting.PloS one · 2026Article
- Validity of a Novel Research-Grade Physical Activity and Sleep Monitor for Continuous Remote Patient Monitoring.Sensors (Basel, Switzerland) · 2021Article
- Design and Validation of an E-Textile-Based Wearable Sock for Remote Gait and Postural Assessment.Sensors (Basel, Switzerland) · 2020Article
- Validity of Instrumented Insoles for Step Counting, Posture and Activity Recognition: A Systematic Review.Sensors (Basel, Switzerland) · 2019Review
- Classification and characterization of postural transitions using instrumented shoes.Medical & biological engineering & computing · 2018Article
- Automatic Recognition of Activities of Daily Living Utilizing Insole-Based and Wrist-Worn Wearable Sensors.IEEE journal of biomedical and health informatics · 2018Article
- Electronic Assessment of Physical Decline in Geriatric Cancer Patients.Current oncology reports · 2018Review
- The Role of Heart-Rate Variability Parameters in Activity Recognition and Energy-Expenditure Estimation Using Wearable Sensors.Sensors (Basel, Switzerland) · 2017Article
- Wearable Sensors for Remote Health Monitoring.Sensors (Basel, Switzerland) · 2017Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Monitoring of postures and activities is used in many clinical and research applications, some of which may require highly reliable posture and activity recognition with desired accuracy well above 99% mark. This paper suggests a method for performing highly accurate recognition of postures and activities from data collected by a wearable shoe monitor (SmartShoe) through classification with rejection. Signals from pressure and acceleration sensors embedded in SmartShoe are used either as raw sensor data or after feature extraction. The Support vector machine (SVM) and multilayer perceptron (MLP) are used to implement classification with rejection. Unreliable observations are rejected by measuring the distance from the decision boundary and eliminating those observations that reside below rejection threshold. The results show a significant improvement (from 97.3% ± 2.3% to 99.8% ± 0.1%) in the classification accuracy after the rejection, using MLP with raw sensor data and rejecting 31.6% of observations. The results also demonstrate that MLP outperformed the SVM, and the classification accuracy based on raw sensor data was higher than the accuracy based on extracted features. The proposed approach will be especially beneficial in applications where high accuracy of recognition is desired while not all observations need to be assigned a class label.
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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.