Deep Learning for Human Activity Recognition in Mobile Computing
Open accessAbstract
By leveraging advances in deep learning, challenging pattern recognition problems have been solved in computer vision, speech recognition, natural language processing, and more. Mobile computing has also adopted these powerful modeling approaches, delivering astonishing success in the field's core application domains, including the ongoing transformation of human activity recognition technology through machine learning.
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As discussed in [296], many methods segment time series using traditional static sliding window methods.
“As discussed in [296], many methods segment time series using traditional static sliding window methods.”
From Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances (2022) · cites this paper as [296]
Cites
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Self-citationConvolutional Neural Networks for Human Activity Recognition using Mobile Sensors
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Ensembles of Deep LSTM Learners for Activity Recognition using Wearables
Guan, Y., & Plötz, T. (2017) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3090076 · Cited by 511 · see citing works
Self-citationSqueezing Deep Learning into Mobile and Embedded Devices
Lane, N. D., Bhattacharya, S., Mathur, A., Georgiev, P., Forlivesi, C., & Kawsar, F. (2017) · IEEE Pervasive Computing · doi:10.1109/mprv.2017.2940968 · Cited by 224 · see citing works
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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 · see citing works
“As discussed in [213], many methods segment time series using traditional static sliding window methods.”
MethodAssessing the State of Self-Supervised Human Activity Recognition Using Wearables
Haresamudram, H., Essa, I., & Plötz, T. (2022) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3550299 · Cited by 107 · see citing works
“Methods, which are effective without requiring many users or hours of data are preferable [51].”
BackgroundSelf-citationOn the role of features in human activity recognition
Haresamudram, H., Anderson, D. V., & Plötz, T. (2019) · Proceedings of the 23rd International Symposium on Wearable Computers · doi:10.1145/3341163.3347727 · Cited by 90 · see citing works
Self-citationGao, Y., Long, Y., Guan, Y., Basu, A., Baggaley, J., & Ploetz, T. (2019) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3314399 · Cited by 49 · see citing works
Self-citationCiliberto, M., Fortes Rey, V., Calatroni, A., Lukowicz, P., & Roggen, D. (2021) · Frontiers in Computer Science · doi:10.3389/fcomp.2021.792065 · Cited by 35 · see citing works
“…is an important area of research in wearable, mobile and ubiquitous computing Kim et al. (2010); Bulling et al. (2014); San-Segundo et al. (2018); Plotz and Guan (2018) systems able to model human behaviour and automatically recognise speci fi c activities and situations enable new forms of…”
BackgroundPlÖtz, T. (2022) · ACM Computing Surveys · doi:10.1145/3459666 · Cited by 34 · see citing works
Investigating Enhancements to Contrastive Predictive Coding for Human Activity Recognition
Haresamudram, H., Essa, I., & Plötz, T. (2023) · 2023 IEEE International Conference on Pervasive Computing and Communications (PerCom) · doi:10.1109/percom56429.2023.10099197 · Cited by 27 · see citing works
“However, annotating this data is not as straightforward because it is typically considered privacy invasive, cumbersome, and sometimes simply impossible from a logistical perspective [10].”
BackgroundSelf-citationIf only we had more data!: Sensor-Based Human Activity Recognition in Challenging Scenarios
Plötz, T. (2023) · 2023 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops) · doi:10.1109/percomworkshops56833.2023.10150267 · Cited by 16 · see citing works
Self-citationDeriving Effective Human Activity Recognition Systems through Objective Task Complexity Assessment
Hiremath, S. K., & Plötz, T. (2020) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3432227 · Cited by 11 · see citing works
Self-citationHiremath, S. K., & Ploetz, T. (2021) · 2021 International Symposium on Wearable Computers · doi:10.1145/3460421.3478825 · Cited by 4 · see citing works
Oishi, N., Birch, P., Lago, P., & Roggen, D. (2026) · Knowledge-Based Systems · doi:10.1016/j.knosys.2026.116367 · Cited by 1 · see citing works
Investigating Enhancements to Contrastive Predictive Coding for Human Activity Recognition
Haresamudram, H., Essa, I., & Ploetz, T. (2022) · arXiv · Cited by 27 · see citing works
“However, annotating this data is not as straightforward because it is typically considered privacy invasive, cumbersome, and sometimes simply impossible from a logistical perspective [10].”
BackgroundSelf-citation
- Authors
- Plotz, T., & Guan, Y.
- Affiliations
- Georgia Tech
- Newcastle University (GB)
- ORCID
- Guan, Yu
- Venue
- Computer
- Published
- 2018-05
- Volume
- 51
- Issue
- 5
- Pages
- 50-59
- ISSN
- 0018-9162 (ISSN-L)
- 1558-0814
- OpenAlex
- W2803166421
- Cited by
- 99
- References
- 13
- Citations by year
- 2026: 3 · 2025: 7 · 2024: 6 · 2023: 9 · 2022: 20 · 2021: 17 · 2020: 20 · 2019: 14 · 2018: 3
- FWCI
- 6.76
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Computer (publishedVersion)
- Newcastle University ePrints (Newcastle Univesity)
- Funders
- Engineering and Physical Sciences Research Council (EP/M020576/1)
- 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 › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
- Keywords
- human activity recognition, deep learning, mobile computing, wearable sensors, pattern recognition
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