ArticleSensors (Basel, Switzerland)2021
Sensor-Based Human Activity Recognition with Spatio-Temporal Deep Learning.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed.
- A Permutation-Invariant, Missing-View-Aware Multi-Perspective Recurrent Neural Network Cell for Time-Series Fusion.Sensors (Basel, Switzerland) · 2026Article
- FPGA-Based Weighted DTW Framework with Hybrid Gait Symmetry Index for Real-Time Wearable Gait Classification.Sensors (Basel, Switzerland) · 2026Article
- Neural Network-Based Granular Activity Recognition from Accelerometers: Assessing Generalizability Across Diverse Mobility Profiles.Sensors (Basel, Switzerland) · 2026Article
- Spatiotemporal deep learning framework for predictive behavioral threat detection in surveillance footage.Frontiers in big data · 2026Article
- Augmenting a ResNet + BiLSTM Deep Learning Model with Clinical Mobility Data Helps Outperform a Heuristic Frequency-Based Model for Walking Bout Segmentation.Sensors (Basel, Switzerland) · 2025Article
- Advanced smart human activity recognition system for disabled people using artificial intelligence with snake optimizer techniques.Scientific reports · 2025Article
- Intelligent deep learning for human activity recognition in individuals with disabilities using sensor based IoT and edge cloud continuum.Scientific reports · 2025Article
- A Comprehensive Methodological Survey of Human Activity Recognition Across Diverse Data Modalities.Sensors (Basel, Switzerland) · 2025Review
- Smart indoor monitoring for disabled individuals using an ensemble of deep learning models in an IoT environment.Scientific reports · 2025Article
- Artificial intelligence-driven ensemble deep learning models for smart monitoring of indoor activities in IoT environment for people with disabilities.Scientific reports · 2025Article
- Generisch-Net: A Generic Deep Model for Analyzing Human Motion with Wearable Sensors in the Internet of Health Things.Sensors (Basel, Switzerland) · 2024Article
- Research on the Human Motion Recognition Method Based on Wearable.Biosensors · 2024Article
- A novel WGF-LN based edge driven intelligence for wearable devices in human activity recognition.Scientific reports · 2023Article
- Exploring Regularization Methods for Domain Generalization in Accelerometer-Based Human Activity Recognition.Sensors (Basel, Switzerland) · 2023Article
- Human Activity Recognition Using Attention-Mechanism-Based Deep Learning Feature Combination.Sensors (Basel, Switzerland) · 2023Article
- Deep SE-BiLSTM with IFPOA Fine-Tuning for Human Activity Recognition Using Mobile and Wearable Sensors.Sensors (Basel, Switzerland) · 2023Article
- Experimental Study: Deep Learning-Based Fall Monitoring among Older Adults with Skin-Wearable Electronics.Sensors (Basel, Switzerland) · 2023Article
- Deep Neural Network for the Detections of Fall and Physical Activities Using Foot Pressures and Inertial Sensing.Sensors (Basel, Switzerland) · 2023Article
- Human behavior recognition based on sparse transformer with channel attention mechanism.Frontiers in physiology · 2023Article
- Indoor Spatiotemporal Contact Analytics Using Landmark-Aided Pedestrian Dead Reckoning on Smartphones.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Human activity recognition (HAR) remains a challenging yet crucial problem to address in computer vision. HAR is primarily intended to be used with other technologies, such as the Internet of Things, to assist in healthcare and eldercare. With the development of deep learning, automatic high-level feature extraction has become a possibility and has been used to optimize HAR performance. Furthermore, deep-learning techniques have been applied in various fields for sensor-based HAR. This study introduces a new methodology using convolution neural networks (CNN) with varying kernel dimensions along with bi-directional long short-term memory (BiLSTM) to capture features at various resolutions. The novelty of this research lies in the effective selection of the optimal video representation and in the effective extraction of spatial and temporal features from sensor data using traditional CNN and BiLSTM. Wireless sensor data mining (WISDM) and UCI datasets are used for this proposed methodology in which data are collected through diverse methods, including accelerometers, sensors, and gyroscopes. The results indicate that the proposed scheme is efficient in improving HAR. It was thus found that unlike other available methods, the proposed method improved accuracy, attaining a higher score in the WISDM dataset compared to the UCI dataset (98.53% vs. 97.05%).
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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.