ArticleSensors (Basel, Switzerland)2018
Feature Representation and Data Augmentation for Human Activity Classification Based on Wearable IMU Sensor Data Using a Deep LSTM Neural Network.
Article in Sensors (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers, 2 of them syntheses that pooled it.
What it found
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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
49 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Synthetic data generation methods for longitudinal and time series health data: a systematic review.BMC medical informatics and decision making · 2025Pooled it
- Conversion of Upper-Limb Inertial Measurement Unit Data to Joint Angles: A Systematic Review.Sensors (Basel, Switzerland) · 2023Pooled it
- Multimodal machine learning mobility assessment in Parkinson's disease within supervised and unsupervised settings.Journal of neuroengineering and rehabilitation · 2026Article
- Feasibility of mechanomyography-based fatigue classification for passive lower-limb exoskeleton evaluation: A pilot study.PloS one · 2026Article
- Advancing Wearable-Based Upper-Limb Stroke Recovery Assessment to the Clinic: A Comparison of Movement Segmentation Strategies.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2026Article
- Predicting spatial familiarity by exploiting head and eye movements during pedestrian navigation in the real world.Scientific reports · 2025Article
- Time Series Data Augmentation for Energy Consumption Data Based on Improved TimeGAN.Sensors (Basel, Switzerland) · 2025Article
- A stacked CNN and random forest ensemble architecture for complex nursing activity recognition and nurse identification.Scientific reports · 2024Article
- Prediction of medial knee contact force using multisource fusion recurrent neural network and transfer learning.Medical & biological engineering & computing · 2024Article
- Augmented drug combination dataset to improve the performance of machine learning models predicting synergistic anticancer effects.Scientific reports · 2024Article
- Exploiting the features of deep residual network with SVM classifier for human posture recognition.PloS one · 2024Article
- Augmented drug combination dataset to improve the performance of machine learning models predicting synergistic anticancer effects.Research square · 2023Article
- Using the LSTM Neural Network and the UWB Positioning System to Predict the Position of Low and High Speed Moving Objects.Sensors (Basel, Switzerland) · 2023Article
- Hybrid Learning Models for IMU-Based HAR with Feature Analysis and Data Correction.Sensors (Basel, Switzerland) · 2023Article
- Counting Finger and Wrist Movements Using Only a Wrist-Worn, Inertial Measurement Unit: Toward Practical Wearable Sensing for Hand-Related Healthcare Applications.Sensors (Basel, Switzerland) · 2023Article
- Extended Application of Inertial Measurement Units in Biomechanics: From Activity Recognition to Force Estimation.Sensors (Basel, Switzerland) · 2023Review
- A Novel Transformer-Based IMU Self-Calibration Approach through On-Board RGB Camera for UAV Flight Stabilization.Sensors (Basel, Switzerland) · 2023Article
- Development and Analysis of a CNN- and Transfer-Learning-Based Classification Model for Automated Dairy Cow Feeding Behavior Recognition from Accelerometer Data.Sensors (Basel, Switzerland) · 2023Article
- Ambient assisted living for frail people through human activity recognition: state-of-the-art, challenges and future directions.Frontiers in neuroscience · 2023Review
- Data-efficient human walking speed intent identification.Wearable technologies · 2023Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Wearable inertial measurement unit (IMU) sensors are powerful enablers for acquisition of motion data. Specifically, in human activity recognition (HAR), IMU sensor data collected from human motion are categorically combined to formulate datasets that can be used for learning human activities. However, successful learning of human activities from motion data involves the design and use of proper feature representations of IMU sensor data and suitable classifiers. Furthermore, the scarcity of labelled data is an impeding factor in the process of understanding the performance capabilities of data-driven learning models. To tackle these challenges, two primary contributions are in this article: first; by using raw IMU sensor data, a spectrogram-based feature extraction approach is proposed. Second, an ensemble of data augmentations in feature space is proposed to take care of the data scarcity problem. Performance tests were conducted on a deep long term short term memory (LSTM) neural network architecture to explore the influence of feature representations and the augmentations on activity recognition accuracy. The proposed feature extraction approach combined with the data augmentation ensemble produces state-of-the-art accuracy results in HAR. A performance evaluation of each augmentation approach is performed to show the influence on classification accuracy. Finally, in addition to using our own dataset, the proposed data augmentation technique is evaluated against the University of California, Irvine (UCI) public online HAR dataset and yields state-of-the-art accuracy results at various learning rates.
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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.