ArticleIEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society2019
Automatically Evaluating Balance: A Machine Learning Approach.
Article in IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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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
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The Use of Wearable Sensors and Machine Learning Methods to Estimate Biomechanical Characteristics During Standing Posture or Locomotion: A Systematic Review.Sensors (Basel, Switzerland) · 2024Pooled it
- Automatic multi-IMU-based deep learning evaluation of intensity during static standing balance training exercises.Journal of neuroengineering and rehabilitation · 2025Article
- AI-driven balance evaluation: a comparative study between blind and non-blind individuals using the mini-BESTest.PeerJ. Computer science · 2025Article
- Distinguishing among standing postures with machine learning-based classification algorithms.Experimental brain research · 2024Article
- A machine learning approach to evaluate the impact of virtual balance/cognitive training on fall risk in older women.Frontiers in computational neuroscience · 2024Article
- Differences between physical therapist ratings, self-ratings, and posturographic measures when assessing static balance exercise intensity.Frontiers in rehabilitation sciences · 2023Article
- Automated assessment of balance: A neural network approach based on large-scale balance function data.Frontiers in public health · 2022Article
- Automatically evaluating balance using machine learning and data from a single inertial measurement unit.Journal of neuroengineering and rehabilitation · 2021Article
- Detection of Postural Control in Young and Elderly Adults Using Deep and Machine Learning Methods with Joint-Node Plots.Sensors (Basel, Switzerland) · 2021Article
- Recent Advances in the Application of Artificial Intelligence in Otorhinolaryngology-Head and Neck Surgery.Clinical and experimental otorhinolaryngology · 2020Review
- Predicting reactive stepping in response to perturbations by using a classification approach.Journal of neuroengineering and rehabilitation · 2020Article
- Sub-graph entropy based network approaches for classifying adolescent obsessive-compulsive disorder from resting-state functional MRI.NeuroImage. Clinical · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Compared to in-clinic balance training, in-home training is not as effective. This is, in part, due to the lack of feedback from physical therapists (PTs). In this paper, we analyze the feasibility of using trunk sway data and machine learning (ML) techniques to automatically evaluate balance, providing accurate assessments outside of the clinic. We recruited sixteen participants to perform standing balance exercises. For each exercise, we recorded trunk sway data and had a PT rate balance performance on a scale of 1-5. The rating scale was adapted from the Functional Independence Measure. From the trunk sway data, we extracted a 61-dimensional feature vector representing the performance of each exercise. Given these labeled data, we trained a multi-class support vector machine (SVM) to map trunk sway features to PT ratings. Evaluated in a leave-one-participant-out scheme, the model achieved a classification accuracy of 82%. Compared to participant self-assessment ratings, the SVM outputs were significantly closer to PT ratings. The results of this pilot study suggest that in the absence of PTs, ML techniques can provide accurate assessments during standing balance exercises. Such automated assessments could reduce PT consultation time and increase user compliance outside of the clinic.
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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.