ArticleSensors (Basel, Switzerland)2021
Wearable IMU-Based Human Activity Recognition Algorithm for Clinical Balance Assessment Using 1D-CNN and GRU Ensemble Model.
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 14 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
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The Use of Artificial Intelligence and Wearable Inertial Measurement Units in Medicine: Systematic Review.JMIR mHealth and uHealth · 2025Pooled it
- An Adaptive IMU-Visual Multimodal Fusion System for Real-Time Exercise Recognition and Movement Quality Assessment.Sensors (Basel, Switzerland) · 2026Article
- Deployment-Oriented Evaluation of Lightweight IMU-Based Human Activity Recognition: On-Device Efficiency and Streaming Feasibility.Sensors (Basel, Switzerland) · 2026Article
- Predicting spatial familiarity by exploiting head and eye movements during pedestrian navigation in the real world.Scientific reports · 2025Article
- AI-driven balance evaluation: a comparative study between blind and non-blind individuals using the mini-BESTest.PeerJ. Computer science · 2025Article
- Toward Improving Human Training by Combining Wearable Full-Body IoT Sensors and Machine Learning.Sensors (Basel, Switzerland) · 2024Article
- Hybrid Learning Models for IMU-Based HAR with Feature Analysis and Data Correction.Sensors (Basel, Switzerland) · 2023Article
- Human Activity Prediction Based on Forecasted IMU Activity Signals by Sequence-to-Sequence Deep Neural Networks.Sensors (Basel, Switzerland) · 2023Article
- Deep SE-BiLSTM with IFPOA Fine-Tuning for Human Activity Recognition Using Mobile and Wearable Sensors.Sensors (Basel, Switzerland) · 2023Article
- Data Valuation Algorithm for Inertial Measurement Unit-Based Human Activity Recognition.Sensors (Basel, Switzerland) · 2022Article
- Real-Time Human Activity Recognition with IMU and Encoder Sensors in Wearable Exoskeleton Robot via Deep Learning Networks.Sensors (Basel, Switzerland) · 2022Article
- Wearable Sensors Applied in Movement Analysis.Sensors (Basel, Switzerland) · 2022Article
- Inertial-Measurement-Unit-Based Novel Human Activity Recognition Algorithm Using Conformer.Sensors (Basel, Switzerland) · 2022Article
- [Assessment of mobility-Geriatric assessment instruments for mobility impairments and perspectives of instrumentation].Zeitschrift fur Gerontologie und Geriatrie · 2022Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this study, a wearable inertial measurement unit system was introduced to assess patients via the Berg balance scale (BBS), a clinical test for balance assessment. For this purpose, an automatic scoring algorithm was developed. The principal aim of this study is to improve the performance of the machine-learning-based method by introducing a deep-learning algorithm. A one-dimensional (1D) convolutional neural network (CNN) and a gated recurrent unit (GRU) that shows good performance in multivariate time-series data were used as model components to find the optimal ensemble model. Various structures were tested, and a stacking ensemble model with a simple meta-learner after two 1D-CNN heads and one GRU head showed the best performance. Additionally, model performance was enhanced by improving the dataset via preprocessing. The data were down sampled, an appropriate sampling rate was found, and the training and evaluation times of the model were improved. Using an augmentation process, the data imbalance problem was solved, and model accuracy was improved. The maximum accuracy of 14 BBS tasks using the model was 98.4%, which is superior to the results of previous studies.
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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.