Recognizing Detailed Human Context in the Wild from Smartphones and Smartwatches
Open accessAbstract
The ability to automatically recognize a person's behavioral context can contribute to health monitoring, aging care, and many other domains. Validating context recognition in the wild is crucial to promote practical applications that work in real-life settings. The authors collected more than 300,000 minutes of sensor data with context labels from 60 subjects. Unlike previous studies, these subjects used their own personal phone, in any way that was convenient to them, and engaged in their routine in their natural environments. Unscripted behavior and unconstrained phone usage resulted in situations that were harder to recognize. The authors demonstrate how fusion of multimodal sensors is important for resolving such cases. They present a baseline system and encourage researchers to use their public dataset to compare methods and improve context recognition in the wild.
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No consensus has been made regarding the positioning of sensors and their data acquisition details.
“Although many commercial fitness trackers or smartwatches adopt wrist-mountable form due to the ease of accessibility, no consensus has been made regarding the positioning of sensors and their data acquisition details.”
From Sensor Data Acquisition and Multimodal Sensor Fusion for Human Activity Recognition Using Deep Learning (2019) · cites this paper as [15]
The ExtraSensory dataset is not sufficient to evaluate recognition performance according to sensor position and sampling rate because it adopts heterogeneous sensors on limited positions of the human body.
“However, as the dataset adopts heterogeneous sensors on limited position of the human body, it is not sufficient to evaluate the recognition performance according to the sensor position and sampling rate.”
From Sensor Data Acquisition and Multimodal Sensor Fusion for Human Activity Recognition Using Deep Learning (2019) · cites this paper as [15]
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References 38 · Cited by 324
- Authors
- Vaizman, Y., Ellis, K., & Lanckriet, G.
- Affiliations
- University of California San Diego (US)
- Venue
- IEEE Pervasive Computing
- Published
- 2017-10
- Volume
- 16
- Issue
- 4
- Pages
- 62-74
- ISSN
- 1536-1268 (ISSN-L)
- OpenAlex
- W2523230499
- Cited by
- 324
- References
- 38
- Citations by year
- 2026: 25 · 2025: 37 · 2024: 33 · 2023: 53 · 2022: 43 · 2021: 47 · 2020: 38 · 2019: 30 · 2018: 18
- FWCI
- 9.76
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Pervasive Computing (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- 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 Science Applications › Mobile Crowdsensing and Crowdsourcing
- Keywords
- smartphones, smartwatches, multimodal sensor fusion, health monitoring, elderly care, public datasets, activity recognition
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