ArticleSensors (Basel, Switzerland)2020
Analyzing the Effectiveness and Contribution of Each Axis of Tri-Axial Accelerometer Sensor for Accurate Activity Recognition.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 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.
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Who cites it
31 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The Application of Wearable Sensors and Machine Learning Algorithms in Rehabilitation Training: A Systematic Review.Sensors (Basel, Switzerland) · 2023Pooled it
- Automatic Detection of Gait Perturbations With Everyday Wearable Technology.IEEE open journal of engineering in medicine and biology · 2025Article
- Data Analytics in Physical Activity Studies With Accelerometers: Scoping Review.Journal of medical Internet research · 2024Article
- A smart device for non-invasive ADL estimation through multi-environmental sensor fusion.Scientific reports · 2023Article
- Efficient Feature-Selection-Based Stacking Model for Stress Detection Based on Chest Electrodermal Activity.Sensors (Basel, Switzerland) · 2023Article
- Review on Human Action Recognition in Smart Living: Sensing Technology, Multimodality, Real-Time Processing, Interoperability, and Resource-Constrained Processing.Sensors (Basel, Switzerland) · 2023Review
- A multimodal facial cues based engagement detection system in e-learning context using deep learning approach.Multimedia tools and applications · 2023Article
- Inertial Sensor-Based Sport Activity Advisory System Using Machine Learning Algorithms.Sensors (Basel, Switzerland) · 2023Article
- Toward explainable AI-empowered cognitive health assessment.Frontiers in public health · 2023Article
- The Application of Internet of Things for the Elderly Health Safety: A Systematic Review.Advanced biomedical research · 2023Review
- Ensemble of RNN Classifiers for Activity Detection Using a Smartphone and Supporting Nodes.Sensors (Basel, Switzerland) · 2022Article
- Stochastic Recognition of Human Physical Activities via Augmented Feature Descriptors and Random Forest Model.Sensors (Basel, Switzerland) · 2022Article
- Human Activity Recognition: Review, Taxonomy and Open Challenges.Sensors (Basel, Switzerland) · 2022Review
- 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
- Bio-Imaging-Based Machine Learning Algorithm for Breast Cancer Detection.Diagnostics (Basel, Switzerland) · 2022Article
- Are Machine Learning Models on Wrist Accelerometry Robust against Differences in Physical Performance among Older Adults?Sensors (Basel, Switzerland) · 2022Article
- Smartphone-Based Activity Recognition Using Multistream Movelets Combining Accelerometer and Gyroscope Data.Sensors (Basel, Switzerland) · 2022Article
- Investigating the Impact of Information Sharing in Human Activity Recognition.Sensors (Basel, Switzerland) · 2022Article
- Improving Recognition of Overlapping Activities with Less Interclass Variations in Smart Homes through Clustering-Based Classification.Computational intelligence and neuroscience · 2022Article
- Falling and Drowning Detection Framework Using Smartphone Sensors.Computational intelligence and neuroscience · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recognizing human physical activities from streaming smartphone sensor readings is essential for the successful realization of a smart environment. Physical activity recognition is one of the active research topics to provide users the adaptive services using smart devices. Existing physical activity recognition methods lack in providing fast and accurate recognition of activities. This paper proposes an approach to recognize physical activities using only2-axes of the smartphone accelerometer sensor. It also investigates the effectiveness and contribution of each axis of the accelerometer in the recognition of physical activities. To implement our approach, data of daily life activities are collected labeled using the accelerometer from 12 participants. Furthermore, three machine learning classifiers are implemented to train the model on the collected dataset and in predicting the activities. Our proposed approach provides more promising results compared to the existing techniques and presents a strong rationale behind the effectiveness and contribution of each axis of an accelerometer for activity recognition. To ensure the reliability of the model, we evaluate the proposed approach and observations on standard publicly available dataset WISDM also and provide a comparative analysis with state-of-the-art studies. The proposed approach achieved 93% weighted accuracy with Multilayer Perceptron (MLP) classifier, which is almost 13% higher than the existing methods.
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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.