Can Deep Learning Revolutionize Mobile Sensing?
Open accessAbstract
Sensor-equipped smartphones and wearables are transforming a variety of mobile apps ranging from health monitoring to digital assistants. However, reliably inferring user behavior and context from noisy and complex sensor data collected under mobile device constraints remains an open problem, and a key bottleneck to sensor app development. In recent years, advances in the field of deep learning have resulted in nearly unprecedented gains in related inference tasks such as speech and object recognition. However, although mobile sensing shares many of the same data modeling challenges, we have yet to see deep learning be systematically studied within the sensing domain. If deep learning could lead to significantly more robust and efficient mobile sensor inference it would revolutionize the field by rapidly expanding the number of sensor apps ready for mainstream usage.
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This work supports the need for more research into optimal strategies for partitioning DNN inference across mobile and edge systems to improve latency, reduce power consumption, and increase the complexity of the DNNs serviceable to wearable platforms.
“also supports the need for more research into optimal strategies for partitioning DNN inference across mobile and edge systems to improve latency, reduce power consumption, and increase the complexity of the DNNs serviceable to wearable platforms.”
From Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances (2022) · cites this paper as [333]
Lane et al. performed a small-scale exploration into the performance of DNNs for HAR applications on mobile platforms in various configurations, including utilizing the phone's CPU and DSP and offloading computation onto remote devices.
“Lane et al. performed a small-scale exploration into the performance of DNNs for HAR applications on mobile platforms in various configurations, including utilizing the phone’s CPU and DSP and offloading computation onto remote devices [333].”
From Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances (2022) · cites this paper as [333]
This work demonstrates that mobile devices running DNN inference can scale gracefully across different compute resources available to the mobile platform.
“This work demonstrates that mobile devices running DNN inference can scale gracefully across different compute resources available to the mobile platform”
From Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances (2022) · cites this paper as [333]
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- Authors
- Lane, N. D., & Georgiev, P.
- Affiliations
- Microsoft Research Asia (China) (CN)
- University of Cambridge (GB)
- ORCID
- Lane, Nicholas D.
- Venue
- Proceedings of the 16th International Workshop on Mobile Computing Systems and Applications
- Publisher
- ACM
- Published
- 2015-02-12
- Pages
- 117-122
- ISSN
- 1550-6193 (ISSN-L)
- OpenAlex
- W1991539813
- Cited by
- 282
- References
- 29
- Citations by year
- 2026: 13 · 2025: 9 · 2024: 6 · 2023: 18 · 2022: 23 · 2021: 30 · 2020: 29 · 2019: 53 · 2018: 30 · 2017: 30 · 2016: 35 · 2015: 6
- FWCI
- 13.81
- Type
- conference paper
- Language
- en
- Open access
- green
- discovery.ucl.ac.uk
- Locations
- IEEE Workshop on Mobile Computing Systems and Applications (WMCSA) (publishedVersion)
- UCL Discovery (University College London) (submittedVersion)
- 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
- mobile sensing, deep learning, smartphones, wearable devices, context inference, user behavior inference, noisy sensor data
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