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A Review on Scaling Mobile Sensing Platforms for Human Activity Recognition: Challenges and Recommendations for Future Research

by 1,*,† and 2,†
1
COPELABS, University Lusofona, 1990-124 Lisboa, Portugal
2
Fortiss GmbH—Research Institute of the Free State of Bavaria Associated with Technical University of Munich, 80805 Munich, Germany
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
IoT 2020, 1(2), 451-473; https://doi.org/10.3390/iot1020025
Received: 28 September 2020 / Revised: 19 November 2020 / Accepted: 21 November 2020 / Published: 29 November 2020
Mobile sensing has been gaining ground due to the increasing capabilities of mobile and personal devices that are carried around by citizens, giving access to a large variety of data and services based on the way humans interact. Mobile sensing brings several advantages in terms of the richness of available data, particularly for human activity recognition. Nevertheless, the infrastructure required to support large-scale mobile sensing requires an interoperable design, which is still hard to achieve today. This review paper contributes to raising awareness of challenges faced today by mobile sensing platforms that perform learning and behavior inference with respect to human routines: how current solutions perform activity recognition, which classification models they consider, and which types of behavior inferences can be seamlessly provided. The paper provides a set of guidelines that contribute to a better functional design of mobile sensing infrastructures, keeping scalability as well as interoperability in mind. View Full-Text
Keywords: mobile sensing; cloud–edge computing; human behavior; human behavior inference; activity recognition; context awareness mobile sensing; cloud–edge computing; human behavior; human behavior inference; activity recognition; context awareness
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MDPI and ACS Style

Carvalho, L.I.; Sofia, R.C. A Review on Scaling Mobile Sensing Platforms for Human Activity Recognition: Challenges and Recommendations for Future Research. IoT 2020, 1, 451-473. https://doi.org/10.3390/iot1020025

AMA Style

Carvalho LI, Sofia RC. A Review on Scaling Mobile Sensing Platforms for Human Activity Recognition: Challenges and Recommendations for Future Research. IoT. 2020; 1(2):451-473. https://doi.org/10.3390/iot1020025

Chicago/Turabian Style

Carvalho, Liliana I., and Rute C. Sofia. 2020. "A Review on Scaling Mobile Sensing Platforms for Human Activity Recognition: Challenges and Recommendations for Future Research" IoT 1, no. 2: 451-473. https://doi.org/10.3390/iot1020025

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