Nonlinear Regression Model of aLow-$g$ MEMS Accelerometer
Abstract
This paper proposes a nonlinear regression model of a microelectromechanical systems capacitive accelerometer, targeted to be used in tilt sensing and low-g motion-tracking applications. The proposed model for the accelerometer's deterministic errors includes common physical parameters used to rate an accelerometer: scale factor, bias, and misalignment. Simple experiments used to reveal the behavior and characteristics of these parameters are described. A phenomenological modeling method is used to establish mathematical representations of these parameters in relation to errors such as nonlinearity and cross-axis effect, without requiring a complete understanding of the underlying physics. Tilt and motion-sensing experiments show that the proposed model reduces sensing errors to a level close to the residual stochastic noise
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The application of machine learning to mobile health sensing poses unique challenges due to noisy sensor measurements, high-dimensional data, sparse and irregular time series, heterogeneity in data, privacy concerns, and resource constraints.
“However, the application of machine learning (ML) to mobile health sensing poses unique challenges due to noisy sensor measurements, high-dimensional data, sparse and irregular time series, heterogeneity in data, privacy concerns and resource constraints.”
From Human-centred artificial intelligence for mobile health sensing: challenges and opportunities (2023) · cites this paper as [13]
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DeepSense: A Unified Deep Learning Framework for Time-Series Mobile Sensing Data Processing
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Self-citationA Comprehensive Review of Sensors and Instrumentation Methods in Devices for Musical Expression
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Passive mobile sensing and psychological traits for large scale mood prediction
Spathis, D., Servia-Rodriguez, S., Farrahi, K., Mascolo, C., & Rentfrow, J. (2019) · Proceedings of the 13th EAI International Conference on Pervasive Computing Technologies for Healthcare · doi:10.1145/3329189.3329213 · Cited by 40 · see citing works
Human-centred artificial intelligence for mobile health sensing: challenges and opportunities
Dang, T., Spathis, D., Ghosh, A., & Mascolo, C. (2023) · Royal Society Open Science · doi:10.1098/rsos.230806 · Cited by 30 · see citing works
- Authors
- Ang, W. T., Khosla, P. K., & Riviere, C. N.
- Affiliations
- Nanyang Technological University (SG)
- Carnegie Mellon University (US)
- Venue
- IEEE Sensors Journal
- Published
- 2007-01
- Volume
- 7
- Issue
- 1
- Pages
- 81-88
- ISSN
- 1530-437X (ISSN-L)
- OpenAlex
- W2103319925
- Cited by
- 100
- References
- 11
- Citations by year
- 2026: 2 · 2025: 4 · 2024: 3 · 2023: 6 · 2022: 4 · 2021: 2 · 2020: 7 · 2019: 5 · 2018: 4 · 2017: 6 · 2016: 10 · 2015: 2 · 2014: 8 · 2013: 6 · 2012: 7
- FWCI
- 9.24
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Sensors Journal (publishedVersion)
- Topics
- Physical Sciences › Engineering › Aerospace Engineering › Inertial Sensor and Navigation
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced MEMS and NEMS Technologies
- Physical Sciences › Computer Science › Computer Networks and Communications › Sensor Technology and Measurement Systems
- Keywords
- MEMS capacitive accelerometer, nonlinear regression, scale factor, bias, misalignment, nonlinearity error, tilt sensing, motion tracking, deterministic errors, phenomenological model
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