ArticleSensors (Basel, Switzerland)2022
Comparing Sampling Strategies for Tackling Imbalanced Data in Human Activity Recognition.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 31 citations in OpenAlex.
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- Data augmentation alters feature importance in XGBoost for CVD prediction.Scientific reports · 2025Article
- An artificial intelligence model to predict mortality among hemodialysis patients: A retrospective validated cohort study.Scientific reports · 2025Article
- Large Language Models for Wearable Sensor-Based Human Activity Recognition, Health Monitoring, and Behavioral Modeling: A Survey of Early Trends, Datasets, and Challenges.Sensors (Basel, Switzerland) · 2024Review
- Exploring the Impact of theSensors (Basel, Switzerland) · 2024Article
- Modeling mortality prediction in older adults with dementia receiving COVID-19 vaccination.BMC geriatrics · 2024Article
- Ambient Assisted Living: Scoping Review of Artificial Intelligence Models, Domains, Technology, and Concerns.Journal of medical Internet research · 2022Article
- A Comparative Study on the Influence of Undersampling and Oversampling Techniques for the Classification of Physical Activities Using an Imbalanced Accelerometer Dataset.Healthcare (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Human activity recognition (HAR) using wearable sensors is an increasingly active research topic in machine learning, aided in part by the ready availability of detailed motion capture data from smartphones, fitness trackers, and smartwatches. The goal of HAR is to use such devices to assist users in their daily lives in application areas such as healthcare, physical therapy, and fitness. One of the main challenges for HAR, particularly when using supervised learning methods, is obtaining balanced data for algorithm optimisation and testing. As people perform some activities more than others (e.g., walk more than run), HAR datasets are typically imbalanced. The lack of dataset representation from minority classes hinders the ability of HAR classifiers to sufficiently capture new instances of those activities. We introduce three novel hybrid sampling strategies to generate more diverse synthetic samples to overcome the class imbalance problem. The first strategy, which we call the
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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.