ArticleInternational journal of environmental research and public health2020
Performance Analysis of Boosting Classifiers in Recognizing Activities of Daily Living.
Article in International journal of environmental research and public health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- A Comparison of Machine Learning Approaches to Activity Classification Using IMU Data Collected in a Community-Based Setting.Sensors (Basel, Switzerland) · 2026Article
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- Gallstone disease classification using SLOA-optimized CatBoost classifier with explainable AI.PloS one · 2026Article
- Comparative Analysis of High-Throughput Data in AML Detection.Advances in experimental medicine and biology · 2026Article
- What drives weight status among female university students? A machine learning analysis of sociodemographic, dietary, and lifestyle determinants.Frontiers in nutrition · 2025Article
- A Predictive Quality Inspection Framework for the Manufacturing Process in the Context of Industry 4.0.Sensors (Basel, Switzerland) · 2024Article
- Nonlinear association between PD-L1 expression levels and the risk of postoperative recurrence in non-small cell lung cancer.Scientific reports · 2024Article
- Intelligent ADL Recognition via IoT-Based Multimodal Deep Learning Framework.Sensors (Basel, Switzerland) · 2023Article
- StackDPP: Stacking-Based Explainable Classifier for Depression Prediction and Finding the Risk Factors among Clinicians.Bioengineering (Basel, Switzerland) · 2023Article
- Predictive Attributes for Developing Long COVID-A Study Using Machine Learning and Real-World Data from Primary Care Physicians in Germany.Journal of clinical medicine · 2023Article
- Genetic Risk Assessment of Nonsyndromic Cleft Lip with or without Cleft Palate by Linking Genetic Networks and Deep Learning Models.International journal of molecular sciences · 2023Article
- Ambient Assisted Living: Scoping Review of Artificial Intelligence Models, Domains, Technology, and Concerns.Journal of medical Internet research · 2022Article
- Artificial Intelligence for Risk Prediction of Rehospitalization with Acute Kidney Injury in Sepsis Survivors.Journal of personalized medicine · 2022Article
- E-CatBoost: An efficient machine learning framework for predicting ICU mortality using the eICU Collaborative Research Database.PloS one · 2022Article
- Discovering Daily Activity Patterns from Sensor Data Sequences and Activity Sequences.Sensors (Basel, Switzerland) · 2021Article
- A Novel Feature Extraction and Fault Detection Technique for the Intelligent Fault Identification of Water Pump Bearings.Sensors (Basel, Switzerland) · 2021Article
- Mortality Prediction of Patients With Cardiovascular Disease Using Medical Claims Data Under Artificial Intelligence Architectures: Validation Study.JMIR medical informatics · 2021Article
- QSAR Models for Active Substances againstMolecules (Basel, Switzerland) · 2021Article
- Wireless E-Nose Sensors to Detect Volatile Organic Gases through Multivariate Analysis.Micromachines · 2020Article
- Main Risk Factors Related to Activities of Daily Living in Non-Dialysis Patients with Chronic Kidney Disease Stage 3-5: A Case-Control Study.Clinical interventions in aging · 2020Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Physical activity is essential for physical and mental health, and its absence is highly associated with severe health conditions and disorders. Therefore, tracking activities of daily living can help promote quality of life. Wearable sensors in this regard can provide a reliable and economical means of tracking such activities, and such sensors are readily available in smartphones and watches. This study is the first of its kind to develop a wearable sensor-based physical activity classification system using a special class of supervised machine learning approaches called boosting algorithms. The study presents the performance analysis of several boosting algorithms (extreme gradient boosting-XGB, light gradient boosting machine-LGBM, gradient boosting-GB, cat boosting-CB and AdaBoost) in a fair and unbiased performance way using uniform dataset, feature set, feature selection method, performance metric and cross-validation techniques. The study utilizes the Smartphone-based dataset of thirty individuals. The results showed that the proposed method could accurately classify the activities of daily living with very high performance (above 90%). These findings suggest the strength of the proposed system in classifying activity of daily living using only the smartphone sensor's data and can assist in reducing the physical inactivity patterns to promote a healthier lifestyle and wellbeing.
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