ArticleSensors (Basel, Switzerland)2020
Accelerometer-Based Human Activity Recognition for Patient Monitoring Using a Deep Neural Network.
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 23 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
23 citing papers in PubMed, 76 citations in OpenAlex.
- Human Activity Recognition Dataset for Pedestrians with Mobility Disabilities.Scientific data · 2026Article
- Benchmarking Accelerometer and CNN-Based Vision Systems for Sleep Posture Classification in Healthcare Applications.Sensors (Basel, Switzerland) · 2025Article
- Detecting Equine Gaits Through Rider-Worn Accelerometers.Animals : an open access journal from MDPI · 2025Article
- Exploring Trends and Clusters in Human Posture Recognition Research: An Analysis Using CiteSpace.Sensors (Basel, Switzerland) · 2025Review
- Multimodal intelligent biosensors framework for fall disease detection and healthcare monitoring.Frontiers in bioengineering and biotechnology · 2025Article
- A comprehensive review of catheter dislodgement: prevalence, clinical impact, and innovations in securement devices.npj biomedical innovations · 2025Review
- Recent Innovations in Footwear and the Role of Smart Footwear in Healthcare-A Survey.Sensors (Basel, Switzerland) · 2024Review
- Deep-HAR: an ensemble deep learning model for recognizing the simple, complex, and heterogeneous human activities.Multimedia tools and applications · 2023Article
- Sensor-Based Activity Recognition Using Frequency Band Enhancement Filters and Model Ensembles.Sensors (Basel, Switzerland) · 2023Article
- Effect of Equipment on the Accuracy of Accelerometer-Based Human Activity Recognition in Extreme Environments.Sensors (Basel, Switzerland) · 2023Article
- Human Postures Recognition by Accelerometer Sensor and ML Architecture Integrated in Embedded Platforms: Benchmarking and Performance Evaluation.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
- Improved Spatiotemporal Framework for Human Activity Recognition in Smart Environment.Sensors (Basel, Switzerland) · 2022Article
- Human Activity Recognition: Review, Taxonomy and Open Challenges.Sensors (Basel, Switzerland) · 2022Review
- Article
- 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
- Inception-LSTM Human Motion Recognition with Channel Attention Mechanism.Computational and mathematical methods in medicine · 2022Article
- Prediction framework for upper body sedentary working behaviour by using deep learning and machine learning techniques.Soft computing · 2022Article
- AIoT-Enabled Rehabilitation Recognition System-Exemplified by Hybrid Lower-Limb Exercises.Sensors (Basel, Switzerland) · 2021Article
- Human Activity Recognition for People with Knee Osteoarthritis-A Proof-of-Concept.Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The objective of this study was to investigate the accuracy of a Deep Neural Network (DNN) in recognizing activities typical for hospitalized patients. A data collection study was conducted with 20 healthy volunteers (10 males and 10 females, age = 43 ± 13 years) in a simulated hospital environment. A single triaxial accelerometer mounted on the trunk was used to measure body movement and recognize six activity types: lying in bed, upright posture, walking, wheelchair transport, stair ascent and stair descent. A DNN consisting of a three-layer convolutional neural network followed by a long short-term memory layer was developed for this classification problem. Additionally, features were extracted from the accelerometer data to train a support vector machine (SVM) classifier for comparison. The DNN reached 94.52% overall accuracy on the holdout dataset compared to 83.35% of the SVM classifier. In conclusion, a DNN is capable of recognizing types of physical activity in simulated hospital conditions using data captured by a single tri-axial accelerometer. The method described may be used for continuous monitoring of patient activities during hospitalization to provide additional insights into the recovery process.
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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.