The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
Multi-Sensor Mobile Platform for the Recognition of Activities of Daily Living and their Environments based on Artificial Neural Networks
Open accessAbstract
The recognition of Activities of Daily Living (ADL) and their environments based on sensors available in off-the-shelf mobile devices is an emerging topic. These devices are capable to acquire and process the sensors' data for the correct recognition of the ADL and their environments, providing a fast and reliable feedback to the user. However, the methods implemented in a mobile application for this purpose should be adapted to the low resources of these devices. This paper focuses on the demonstration of a mobile application that implements a framework, that forks their implementation in several modules, including data acquisition, data processing, data fusion and classification methods based on the sensors? data acquired from the accelerometer, gyroscope, magnetometer, microphone and Global Positioning System (GPS) receiver. The framework presented is a function of the number of sensors available in the mobile devices and implements the classification with Deep Neural Networks (DNN) that reports an accuracy between 58.02% and 89.15%.
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“One common way to improve robustness is to leverage the benefits of multiple types of sensors together to create multi-sensory systems [249,310,311,312,313,314].”
From Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances (2022) · cites this paper as [313]
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Pires, I. M., García, N. M., Pombo, N. G. C. C., & Flórez‐Revuelta, F. (2016) · Sensors · doi:10.3390/s16020184 · Cited by 155
Self-citationA Systematic Literature Review on Usability Heuristics for Mobile Phones
Salazar, L. H., Lacerda, T. C., Nunes, J. V., et al. (2013) · International Journal of Mobile Human Computer Interaction · doi:10.4018/jmhci.2013040103 · Cited by 45
Pires, I. M., García, N. M., Pombo, N. G. C. C., Flórez‐Revuelta, F., Spinsante, S., & Teixeira, M. C. (2018) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2018.05.005 · Cited by 44
Self-citationPires, I. M., Marques, G., García, N. M., Flórez‐Revuelta, F., Teixeira, M. C., Zdravevski, E., Spinsante, S., & Coimbra, M. T. (2020) · Electronics · doi:10.3390/electronics9030509 · Cited by 44
Self-citationA Roadmap to the Design of a Personal Digital Life Coach
García, N. M. (2015) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-25733-4_3 · Cited by 31
Pires, I. M., García, N. M., Pombo, N. G. C. C., et al. (2016) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-40114-0_14 · Cited by 28
Self-citationWichert, R., & Klausing, H. (2013) · Advanced technologies and societal change · doi:10.1007/978-3-642-37988-8 · Cited by 10
Pires, I. M., García, N. M., Pombo, N. G. C. C., et al. (2019) · Statistics Optimization & Information Computing · doi:10.19139/soic.v7i1.548 · Cited by 5
Self-citationPires, I. M., García, N. M., Pombo, N. G. C. C., et al. (2017) · arXiv (Cornell University) · Cited by 3
Self-citationPassi, L. E. (2012) · Routledge eBooks · doi:10.4324/9780203047385-13 · Cited by 3
Research works printed but not linked: 2
- Diane Foti and Jean S. Koketsu, "Activi- ties of daily living," Pedretti's Occupational Therapy: Practical Skills for Physical Dysfunction, vol. 7, pp. 157- 232, 2013
- Ivan Miguel Pires, Nuno M. Garcia and Francisco Flórez-Revuelta, "Multi-sensor data fusion techniques for the identification of activities of daily liv- ing using mobile devices," in Proceedings of the ECMLPKDD 2015 Doctoral Consortium, European Con- ference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Porto, Portugal, 2015.
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Sorted by citations
Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances
Zhang, S., Li, Y., Zhang, S., Shahabi, F., Xia, S., Deng, Y., & Alshurafa, N. (2022) · Sensors · doi:10.3390/s22041476 · Cited by 625
“Nowadays, multi-sensor based HAR and sensor fusion has become a trend to improve the robustness and generalization of recognition systems [4, 122, 131, 212, 244, 331].”
BackgroundDeep Learning for Sensor-based Human Activity Recognition
Chen, K., Zhang, D., Yao, L., Guo, B., Yu, Z., & Liu, Y. (2022) · ACM Computing Surveys · doi:10.1145/3447744 · Cited by 690
“s, including two smartphones and one on-node unit. The results revealed milliseconds to tens of milliseconds computational time of one prediction suggesting the possibility of real-time applications. [110] also demonstrates the combination of hand-crafted features and a neural network is a potential plan to achieve re…”
BackgroundDeep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities
Chen, K., Zhang, D., Yao, L., Guo, B., Yu, Z., & Liu, Y. (2020) · arXiv · doi:10.48550/arxiv.2001.07416 · Cited by 314
“s, including two smartphones and one on-node unit. The results revealed milliseconds to tens of milliseconds computational time of one prediction suggesting the possibility of real-time applications. [110] also demonstrates the combination of hand-crafted features and a neural network is a potential plan to achieve re…”
Background
Metadata
- Authors
- Pires, I. M., Pombo, N., Garcia, N. M., & Flórez-Revuelta, F.
- Affiliations
- University of Beira Interior (PT)
- Instituto de Telecomunicações (PT)
- Instituto de Telecomunicações, Lisbon, Portugal
- Department of Computer Technology, Universidad de Alicante, Alicante, Spain
- Venue
- Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
- Published
- 2018-07
- Pages
- 5850-5852
- OpenAlex
- W2816320080
- Cited by
- 8
- References
- 12
- Citations by year
- 2025: 1 · 2023: 2 · 2022: 1 · 2021: 2 · 2020: 2
- FWCI
- 0.30
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- International Joint Conference on Artificial Intelligence (publishedVersion)
- Funders
- Fundação para a Ciência e a Tecnologia (50008, UID/EEA/50008/2013, FCT UID/EEA/50008/2013)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Social Sciences › Transportation › Human Mobility and Location-Based Analysis
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Indoor and Outdoor Localization Technologies
- Keywords
- activities of daily living, mobile sensing, sensor fusion, deep neural network, accelerometer, gyroscope, magnetometer, GPS, microphones, sensor data classification
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