ArticleSensors (Basel, Switzerland)2020
A Study of Accelerometer and Gyroscope Measurements in Physical Life-Log Activities Detection Systems.
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 22 papers.
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Who cites it
22 citing papers in PubMed, 113 citations in OpenAlex.
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- Exploring Trends and Clusters in Human Posture Recognition Research: An Analysis Using CiteSpace.Sensors (Basel, Switzerland) · 2025Review
- Advancing Parkinson's disease detection through multi-dimensional machine learning: a comprehensive framework using wearable movement sensor analytics.Frontiers in physiology · 2025Article
- Automated Pipeline for Robust Cat Activity Detection Based on Deep Learning and Wearable Sensor Data.Sensors (Basel, Switzerland) · 2024Article
- Leveraging artificial neural networks for enhanced athlete performance evaluation through IMU data analysis.Heliyon · 2024Article
- Alzheimer Disease Detection Studies: Perspective on Multi-Modal Data.Yearbook of medical informatics · 2024Review
- TCN-attention-HAR: human activity recognition based on attention mechanism time convolutional network.Scientific reports · 2024Article
- Hybrid convolution neural network with channel attention mechanism for sensor-based human activity recognition.Scientific reports · 2023Article
- Human Activity Recognition Using Attention-Mechanism-Based Deep Learning Feature Combination.Sensors (Basel, Switzerland) · 2023Article
- Thermal Calibration of Triaxial Accelerometer for Tilt Measurement.Sensors (Basel, Switzerland) · 2023Article
- Comparison of machine learning approaches for near-fall-detection with motion sensors.Frontiers in digital health · 2023Article
- Markerless Radio Frequency Indoor Monitoring for Telemedicine: Gait Analysis, Indoor Positioning, Fall Detection, Tremor Analysis, Vital Signs and Sleep Monitoring.Sensors (Basel, Switzerland) · 2022Review
- Dataset of acceleration signals recorded while performing activities of daily living.Data in brief · 2022Article
- Privacy-Preserving Human Action Recognition with a Many-Objective Evolutionary Algorithm.Sensors (Basel, Switzerland) · 2022Article
- An Alternative Athlete Monitoring System Using Cost-Effective Inertial Sensing Instrumentation.Journal of electrical engineering & technology · 2022Article
- Physical human locomotion prediction using manifold regularization.PeerJ. Computer science · 2022Article
- Structural Health Monitoring in Composite Structures: A Comprehensive Review.Sensors (Basel, Switzerland) · 2021Review
- Multi-Person Tracking and Crowd Behavior Detection via Particles Gradient Motion Descriptor and Improved Entropy Classifier.Entropy (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Nowadays, wearable technology can enhance physical human life-log routines by shifting goals from merely counting steps to tackling significant healthcare challenges. Such wearable technology modules have presented opportunities to acquire important information about human activities in real-life environments. The purpose of this paper is to report on recent developments and to project future advances regarding wearable sensor systems for the sustainable monitoring and recording of human life-logs. On the basis of this survey, we propose a model that is designed to retrieve better information during physical activities in indoor and outdoor environments in order to improve the quality of life and to reduce risks. This model uses a fusion of both statistical and non-statistical features for the recognition of different activity patterns using wearable inertial sensors, i.e., triaxial accelerometers, gyroscopes and magnetometers. These features include signal magnitude, positive/negative peaks and position direction to explore signal orientation changes, position differentiation, temporal variation and optimal changes among coordinates. These features are processed by a genetic algorithm for the selection and classification of inertial signals to learn and recognize abnormal human movement. Our model was experimentally evaluated on four benchmark datasets: Intelligent Media Wearable Smart Home Activities (IM-WSHA), a self-annotated physical activities dataset, Wireless Sensor Data Mining (WISDM) with different sporting patterns from an IM-SB dataset and an SMotion dataset with different physical activities. Experimental results show that the proposed feature extraction strategy outperformed others, achieving an improved recognition accuracy of 81.92%, 95.37%, 90.17%, 94.58%, respectively, when IM-WSHA, WISDM, IM-SB and SMotion datasets were applied.
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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.