ArticleHealthcare (Basel, Switzerland)2022
Human Activity Recognition Based on Embedded Sensor Data Fusion for the Internet of Healthcare Things.
Article in Healthcare (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Technology-Enhanced Health Care in Smart Homes: Scoping Review of Sensor Technologies, Clinical Applications, Integration Challenges, and Future Directions.JMIR medical informatics · 2026Article
- NURSE-AI: A Nurse-by-Design Framework for Multi-Sensor, AI-Enabled Chronic Wound Assessment in Community Healthcare.Sensors (Basel, Switzerland) · 2026Article
- Smart home-assisted anomaly detection system for older adults: a deep learning approach with a comprehensive set of daily activities.Medical & biological engineering & computing · 2025Article
- Enhancing Connected Health Ecosystems Through IoT-Enabled Monitoring Technologies: A Case Study of the Monit4Healthy System.Sensors (Basel, Switzerland) · 2025Article
- Bibliometric Analysis of Research Articles on Embedded Internet of Health Things (IoHT) Fall Detection in the Elderly Published from 2006 to 2025.Journal of multidisciplinary healthcare · 2025Review
- Indoor Human Action Recognition Based on Dual Kinect V2 and Improved Ensemble Learning Method.Sensors (Basel, Switzerland) · 2023Article
- A novel WGF-LN based edge driven intelligence for wearable devices in human activity recognition.Scientific reports · 2023Article
- Fusion of Appearance and Motion Features for Daily Activity Recognition from Egocentric Perspective.Sensors (Basel, Switzerland) · 2023Article
- Exploring Older Adults' Willingness to Install Home Surveil-Lance Systems in Taiwan: Factors and Privacy Concerns.Healthcare (Basel, Switzerland) · 2023Article
- Computer-Aided Ankle Ligament Injury Diagnosis from Magnetic Resonance Images Using Machine Learning Techniques.Sensors (Basel, Switzerland) · 2023Article
- IoMT Meets Machine Learning: From Edge to Cloud Chronic Diseases Diagnosis System.Journal of healthcare engineering · 2023Article
- Improving Inertial Sensor-Based Activity Recognition in Neurological Populations.Sensors (Basel, Switzerland) · 2022Article
- Human Activity Recognition: Review, Taxonomy and Open Challenges.Sensors (Basel, Switzerland) · 2022Review
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Nowadays, the emerging information technologies in smart handheld devices are motivating the research community to make use of embedded sensors in such devices for healthcare purposes. In particular, inertial measurement sensors such as accelerometers and gyroscopes embedded in smartphones and smartwatches can provide sensory data fusion for human activities and gestures. Thus, the concepts of the Internet of Healthcare Things (IoHT) paradigm can be applied to handle such sensory data and maximize the benefits of collecting and analyzing them. The application areas contain but are not restricted to the rehabilitation of elderly people, fall detection, smoking control, sportive exercises, and monitoring of daily life activities. In this work, a public dataset collected using two smartphones (in pocket and wrist positions) is considered for IoHT applications. Three-dimensional inertia signals of thirteen timestamped human activities such as Walking, Walking Upstairs, Walking Downstairs, Writing, Smoking, and others are registered. Here, an efficient human activity recognition (HAR) model is presented based on efficient handcrafted features and Random Forest as a classifier. Simulation results ensure the superiority of the applied model over others introduced in the literature for the same dataset. Moreover, different approaches to evaluating such models are considered, as well as implementation issues. The accuracy of the current model reaches 98.7% on average. The current model performance is also verified using the WISDM v1 dataset.
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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.