ArticleSensors (Basel, Switzerland)2017
Location-Enhanced Activity Recognition in Indoor Environments Using Off the Shelf Smart Watch Technology and BLE Beacons.
Article in Sensors (Basel, Switzerland), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Developing a Health Support System to Promote Care for the Elderly.Sensors (Basel, Switzerland) · 2025Article
- Simplified Indoor Localization Using Bluetooth Beacons and Received Signal Strength Fingerprinting with Smartwatch.Sensors (Basel, Switzerland) · 2024Article
- The effectiveness of simple heuristic features in sensor orientation and placement problems in human activity recognition using a single smartphone accelerometer.Biomedical engineering online · 2024Article
- OHetTLAL: An Online Transfer Learning Method for Fingerprint-Based Indoor Positioning.Sensors (Basel, Switzerland) · 2022Article
- Context-Aware Edge-Based AI Models for Wireless Sensor Networks-An Overview.Sensors (Basel, Switzerland) · 2022Review
- Machine Learning Based Diabetes Classification and Prediction for Healthcare Applications.Journal of healthcare engineering · 2021Article
- Using Intelligent Personal Annotations to Improve Human Activity Recognition for Movements in Natural Environments.IEEE journal of biomedical and health informatics · 2020Article
- A Personalized Healthcare Monitoring System for Diabetic Patients by Utilizing BLE-Based Sensors and Real-Time Data Processing.Sensors (Basel, Switzerland) · 2018Article
- Smart Sensing Technologies for Personalised e-Coaching.Sensors (Basel, Switzerland) · 2018Article
- A Bluetooth Low Energy Indoor Positioning System with Channel Diversity, Weighted Trilateration and Kalman Filtering.Sensors (Basel, Switzerland) · 2017Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Activity recognition in indoor spaces benefits context awareness and improves the efficiency of applications related to personalised health monitoring, building energy management, security and safety. The majority of activity recognition frameworks, however, employ a network of specialised building sensors or a network of body-worn sensors. As this approach suffers with respect to practicality, we propose the use of commercial off-the-shelf devices. In this work, we design and evaluate an activity recognition system composed of a smart watch, which is enhanced with location information coming from Bluetooth Low Energy (BLE) beacons. We evaluate the performance of this approach for a variety of activities performed in an indoor laboratory environment, using four supervised machine learning algorithms. Our experimental results indicate that our location-enhanced activity recognition system is able to reach a classification accuracy ranging from 92% to 100%, while without location information classification accuracy it can drop to as low as 50% in some cases, depending on the window size chosen for data segmentation.
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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.