ArticleSensors (Basel, Switzerland)2018
A User-Adaptive Algorithm for Activity Recognition Based on K-Means Clustering, Local Outlier Factor, and Multivariate Gaussian Distribution.
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 18 papers.
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Who cites it
18 citing papers in PubMed.
- Machine Learning Techniques for Sensor-Based Human Activity Recognition with Data Heterogeneity-A Review.Sensors (Basel, Switzerland) · 2024Review
- Exploiting the features of deep residual network with SVM classifier for human posture recognition.PloS one · 2024Article
- Effect of Equipment on the Accuracy of Accelerometer-Based Human Activity Recognition in Extreme Environments.Sensors (Basel, Switzerland) · 2023Article
- Personalized Activity Recognition with Deep Triplet Embeddings.Sensors (Basel, Switzerland) · 2022Article
- Identifying and Monitoring the Daily Routine of Seniors Living at Home.Sensors (Basel, Switzerland) · 2022Article
- An Analysis of the Motivation Mechanism of the Formation of Corporate Health Strategic Innovation Capability Based on the K-Means Algorithm.Computational intelligence and neuroscience · 2022Article
- Wearable Sensor-Based Human Activity Recognition in the Smart Healthcare System.Computational intelligence and neuroscience · 2022Review
- COLAEVA: Visual Analytics and Data Mining Web-Based Tool for Virtual Coaching of Older Adult Populations.Sensors (Basel, Switzerland) · 2021Article
- Machine Learning Algorithms for Activity-Intensity Recognition Using Accelerometer Data.Sensors (Basel, Switzerland) · 2021Article
- Effect of Aberrant Long Noncoding RNA on the Prognosis of Clear Cell Renal Cell Carcinoma.Computational and mathematical methods in medicine · 2021Article
- An Online Classification Method for Fault Diagnosis of Railway Turnouts.Sensors (Basel, Switzerland) · 2020Article
- Evaluating the Impact of a Two-Stage Multivariate Data Cleansing Approach to Improve to the Performance of Machine Learning Classifiers: A Case Study in Human Activity Recognition.Sensors (Basel, Switzerland) · 2020Article
- Step by Step Towards Effective Human Activity Recognition: A Balance between Energy Consumption and Latency in Health and Wellbeing Applications.Sensors (Basel, Switzerland) · 2019Review
- Consequences of chronic diseases and other limitations associated with old age - a scoping review.BMC public health · 2019Article
- Classifying Diverse Physical Activities Using "Smart Garments".Sensors (Basel, Switzerland) · 2019Article
- K-means clustering of overweight and obese population using quantile-transformed metabolic data.Diabetes, metabolic syndrome and obesity : targets and therapy · 2019Article
- Wear Degree Quantification of Pin Connections Using Parameter-Based Analyses of Acoustic Emissions.Sensors (Basel, Switzerland) · 2018Article
- Automatic Groove Measurement and Evaluation with High Resolution Laser Profiling Data.Sensors (Basel, Switzerland) · 2018Article
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3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Mobile activity recognition is significant to the development of human-centric pervasive applications including elderly care, personalized recommendations, etc. Nevertheless, the distribution of inertial sensor data can be influenced to a great extent by varying users. This means that the performance of an activity recognition classifier trained by one user’s dataset will degenerate when transferred to others. In this study, we focus on building a personalized classifier to detect four categories of human activities: light intensity activity, moderate intensity activity, vigorous intensity activity, and fall. In order to solve the problem caused by different distributions of inertial sensor signals, a user-adaptive algorithm based on K-Means clustering, local outlier factor (LOF), and multivariate Gaussian distribution (MGD) is proposed. To automatically cluster and annotate a specific user’s activity data, an improved K-Means algorithm with a novel initialization method is designed. By quantifying the samples’ informative degree in a labeled individual dataset, the most profitable samples can be selected for activity recognition model adaption. Through experiments, we conclude that our proposed models can adapt to new users with good recognition performance.
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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.