Feature Learning for Activity Recognition in Ubiquitous Computing
Open accessAbstract
Feature extraction for activity recognition in context-aware ubiquitous computing applications is usually a heuristic process, informed by underlying domain knowledge. Relying on such explicit knowledge is problematic when aiming to generalize across different application domains. We investigate the potential of recent machine learning methods for discovering universal features for context-aware applications of activity recognition. We also describe an alternative data representation based on the empirical cumulative distribution function of the raw data, which effectively abstracts from absolute values. Experiments on accelerometer data from four publicly available activity recognition datasets demonstrate the significant potential of our approach to address both contemporary activity recognition tasks and next generation problems such as skill assessment and the detection of novel activities.
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Unlabeled data can be utilized to learn useful representations for wide ranging tasks.
“Previous works such as [31, 63, 69] have demonstrated how unlabeled data can be utilized to learn useful representations for wide ranging tasks, including identifying kitchen activities [11], activity tracking in car manufacturing [71], classifying every day activities such as walking or running [4, 10, 51, 66], and medical scenarios involving identifying freeze of gait in patients suffering from”
From Contrastive Predictive Coding for Human Activity Recognition (2021) · cites this paper as [63]
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Collecting complex activity datasets in highly rich networked sensor environments
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Self-citationA Comprehensive Study of Activity Recognition Using Accelerometers
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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
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Self-citationIf only we had more data!: Sensor-Based Human Activity Recognition in Challenging Scenarios
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- Authors
- Plötz, T., Hammerla, N., & Olivier, P.
- Affiliations
- Newcastle University (GB)
- ORCID
- Olivier, P
- Venue
- Newcastle University
- Publisher
- Newcastle University
- Published
- 2011-12-31
- OpenAlex
- W22482183
- Cited by
- 337
- References
- 1
- Citations by year
- 2025: 3 · 2024: 10 · 2023: 31 · 2022: 16 · 2021: 49 · 2020: 38 · 2019: 39 · 2018: 44 · 2017: 40 · 2016: 22 · 2015: 23 · 2014: 12 · 2013: 8 · 2012: 2
- FWCI
- 5.17
- Type
- conference paper
- Language
- en
- Open access
- green
- doi.org
- Locations
- Newcastle University ePrints (Newcastle Univesity)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- representation learning, activity recognition, ubiquitous computing, sensor data
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