ArticleSensors (Basel, Switzerland)2019
Mobile User Indoor-Outdoor Detection Through Physical Daily Activities.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Internet of things enabled indoor activity monitoring for visually impaired people with hybrid deep learning and optimized algorithms for enhanced safety.Scientific reports · 2025Article
- Relative importance of temporal and location features in predicting smoking events.NPJ digital medicine · 2025Article
- Classification of human walking context using a single-point accelerometer.Scientific reports · 2024Article
- Multi-Sensor Data Fusion Solutions for Blind and Visually Impaired: Research and Commercial Navigation Applications for Indoor and Outdoor Spaces.Sensors (Basel, Switzerland) · 2023Review
- A "one-size-fits-most" walking recognition method for smartphones, smartwatches, and wearable accelerometers.NPJ digital medicine · 2023Article
- Context-Aware Edge-Based AI Models for Wireless Sensor Networks-An Overview.Sensors (Basel, Switzerland) · 2022Review
- A systematic review of smartphone-based human activity recognition methods for health research.NPJ digital medicine · 2021Review
- A Review of Environmental Context Detection for Navigation Based on Multiple Sensors.Sensors (Basel, Switzerland) · 2020Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
An automatic, fast, and accurate switching method between Global Positioning System and indoor positioning systems is crucial to achieve current user positioning, which is essential information for a variety of services installed on smart devices, e.g., location-based services (LBS), healthcare monitoring components, and seamless indoor/outdoor navigation and localization (SNAL). In this study, we proposed an approach to accurately detect the indoor/outdoor environment according to six different daily activities of users including walk, skip, jog, stay, climbing stairs up and down. We select a number of features for each activity and then apply ensemble learning methods such as Random Forest, and AdaBoost to classify the environment types. Extensive model evaluations and feature analysis indicate that the system can achieve a high detection rate with good adaptation for environment recognition. Empirical evaluation of the proposed method has been verified on the HASC-2016 public dataset, and results show 99% accuracy to detect environment types. The proposed method relies only on the daily life activities data and does not need any external facilities such as the signal cell tower or Wi-Fi access points. This implies the applicability of the proposed method for the upper layer applications.
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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.