Signal Quality and Compactness of a Dual-Accelerometer System for Gyro-Free Human Motion Analysis
Open accessAbstract
There is a growing interest in measuring human activities via worn inertial sensors, and situating two accelerometers on a body segment allows accessing rotational kinematic information, at a significantly lower energy cost when compared with gyroscopes. However, the placement of sensors has not been widely considered in the literature. In practice, dual-accelerometer systems should be built as compact as possible to ensure long-term wearability. In this paper, the impact of sensor placement and nature of human activity on signal quality is quantified by individual and differential signal-to-noise ratios (SNRs). To do so, noise-free signals are described by a 2-D kinematic model of a body segment as a function of kinematic variables and sensor location on the segment. Measurements are modelled as kinematic signals disturbed by zero mean additive Gaussian noise. Depending on the accuracy needed, one can choose a minimal SNR to achieve, with such dual-accelerometer arrangement. We estimate SNR and minimal sensor separations for three data sets, two from the public domain and one collected for this paper. The data sets give arm motion profiles for reaching, inertial data collected during locomotion on a treadmill and during activities of daily life. With a dual-accelerometer arrangement, we show that it is possible to achieve a good differential SNR for the analysis of various human activities if the separation between the two sensors and their placement is well chosen.
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The quality of data can be expressed in many different ways, and data quality measures may also depend on the aims of the related analysis and project.
“the quality of data can be expressed in many different ways, and data quality measures may also depend on the aims of the related analysis and project8–13”
From Data quality evaluation in wearable monitoring (2022) · cites this paper as [13]
ACC sensors do not produce measurable artifacts with any practical relevance to the topic at hand.
“However, these sensors do not produce measurable artifacts with any practical relevance to the topic at hand13.”
From Data quality evaluation in wearable monitoring (2022) · cites this paper as [13]
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References 20 · Cited by 20
Research works printed but not linked: 1
- William Harwin is the University of Reading professor of Interactive Systems. His research interests include technology and healthcare, human-robot interactions, and haptic interfaces. Haptics research includes the control, rendering, and applications, most recently activity was work with Kings College on the hapTEL project that explore haptics as an educational tool in dental education. He has a long standing interest in assistive and rehabilitation robotics with a specific interest in neurorehabilitation for individuals following a stroke and is a past chair of the IEEE ICORR conference series. More recent research includes healthcare sensors in a residential environment and the emerging field of cognitive robotics.
- Authors
- Villeneuve, E., Harwin, W., Holderbaum, W., Sherratt, R. S., & White, R.
- Affiliations
- University of Exeter (GB)
- University of Reading (GB)
- Venue
- IEEE Sensors Journal
- Published
- 2016-08
- Volume
- 16
- Issue
- 16
- Pages
- 6261-6269
- ISSN
- 1530-437X (ISSN-L)
- 1558-1748
- 2379-9153
- OpenAlex
- W2468292170
- Cited by
- 20
- References
- 20
- Citations by year
- 2023: 1 · 2022: 3 · 2021: 3 · 2020: 3 · 2019: 2 · 2018: 5 · 2017: 3
- FWCI
- 2.27
- Type
- journal article
- Language
- en
- Open access
- green
- e-space.mmu.ac.uk
- Licence
- Locations
- IEEE Sensors Journal (publishedVersion, public-domain)
- e-space (Manchester Metropolitan University) (acceptedVersion, public-domain)
- CentAUR (University of Reading) (submittedVersion, public-domain)
- Aston Publications Explorer (Aston University) (submittedVersion, public-domain)
- Explore Bristol Research (submittedVersion, public-domain)
- Bristol Research (University of Bristol) (submittedVersion, public-domain)
- Funders
- U.K. Engineering and Physical Sciences Research Council through SPHERE IRC (EP/K031910/1)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Engineering › Aerospace Engineering › Inertial Sensor and Navigation
- Physical Sciences › Engineering › Biomedical Engineering › Gait Recognition and Analysis
- Keywords
- sensor placement, signal-to-noise ratio, wearable inertial sensors, body segment kinematics, arm movement, treadmill locomotion, activities of daily living, compact sensor design, human activity monitoring
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