Dealing with the Effects of Sensor Displacement in Wearable Activity Recognition
Open accessAbstract
Most wearable activity recognition systems assume a predefined sensor deployment that remains unchanged during runtime. However, this assumption does not reflect real-life conditions. During the normal use of such systems, users may place the sensors in a position different from the predefined sensor placement. Also, sensors may move from their original location to a different one, due to a loose attachment. Activity recognition systems trained on activity patterns characteristic of a given sensor deployment may likely fail due to sensor displacements. In this work, we innovatively explore the effects of sensor displacement induced by both the intentional misplacement of sensors and self-placement by the user. The effects of sensor displacement are analyzed for standard activity recognition techniques, as well as for an alternate robust sensor fusion method proposed in a previous work. While classical recognition models show little tolerance to sensor displacement, the proposed method is proven to have notable capabilities to assimilate the changes introduced in the sensor position due to self-placement and provides considerable improvements for large misplacements.
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The REALDISP dataset includes recordings from 17 subjects performing 33 different activities in three placement scenarios.
“This dataset includes recordings from 17 subjects performing 33 different activities in three different scenarios.”
From Feature extraction for robust physical activity recognition (2017) · cites this paper as [24]
The REALDISP dataset includes recordings from 17 subjects, seven females and ten males, ages 22 to 37.
“This dataset includes recordings from 17 subjects, seven females and ten males, with ages ranging from 22 to 37 years old.”
From Feature extraction for robust physical activity recognition (2017) · cites this paper as [24]
The REALDISP dataset includes three placement scenarios: Ideal-placement, Self-placement, and Mutual-placement.
“The dataset includes recordings in three placement scenarios:”
From Feature extraction for robust physical activity recognition (2017) · cites this paper as [24]
The REALDISP dataset includes sensor displacement such as rotation and mis-positioning from the corresponding body part.
“The most significant characteristic of this dataset is the introduction of sensor displacement, such as rotation and mis-positioning from the corresponding body part.”
From Feature extraction for robust physical activity recognition (2017) · cites this paper as [24]
The REALDISP dataset includes 33 physical activities including warm up, fitness and cool down activities.
“The experiment consisted in performing a complete set of exercises: 33 physical activities, including warm up, fitness and cool down activities (walking, jogging, cycling, jumping, etc.).”
From Feature extraction for robust physical activity recognition (2017) · cites this paper as [24]
The REALDISP dataset includes 13 inertial signals obtained from 9 on-body inertial measurement units.
“These recording include 13 inertial signals obtained from 9 on-body inertial measurement units located on different body parts.”
From Feature extraction for robust physical activity recognition (2017) · cites this paper as [24]
The REALDISP dataset samples 3D linear acceleration, 3D angular velocity, 3D magnetic field orientation and 4D quaternions every 20 ms (50 Hz).
“Using these sensors, a 3D (3-dimension) linear acceleration, a 3D angular velocity, a 3D magnetic field orientation and a 4D quaternions are sampled every 20 ms (50 Hz sample-rate).”
From Feature extraction for robust physical activity recognition (2017) · cites this paper as [24]
The REALDISP dataset includes a Null-activity label assigned to other activities and transitions between activities.
“This dataset also includes a Null-activity.This label has been assigned to other activities (different from the 33 activities considered in this study), and also, the transitions between activities.”
From Feature extraction for robust physical activity recognition (2017) · cites this paper as [24]
In the self-placement scenario, the proposed system improves accuracy by more than 10% over the original paper (from 88.4% to 98.9%).
“When comparing these results to the original paper, there is a big improvement (more than 10%, from 88.4 to 98.9%) in the self-placement scenario.”
From Feature extraction for robust physical activity recognition (2017) · cites this paper as [24]
The proposed system's accuracy in ideal placement is a 2.4% improvement over the original paper's 97% baseline.
“After introducing the signal normalization method and considering all sensors, the system accuracy goes to 99.4%, a 2.4% improvement compared to the original paper (97%).”
From Feature extraction for robust physical activity recognition (2017) · cites this paper as [24]
In the self-placement scenario, the original paper's accuracy drops 8.6% compared to ideal placement (from 97.0% to 88.4%).
“Regarding the self-placement scenario, this table shows 8.6% accuracy drop compared to the ideal-placement in the original paper (from 97.0 to 88.4%), but with our system, this reduction is lower than 0.5% (from 99.1 to 98.9%).”
From Feature extraction for robust physical activity recognition (2017) · cites this paper as [24]
In the original paper, the evaluation method was random-partitioning, and the baseline accuracy would be even lower when using subject-wise cross-validation.
“It is important to notice that, in the original paper, the evaluation method was random-partitioning and, based on the results presented in”
From Feature extraction for robust physical activity recognition (2017) · cites this paper as [24]
Including the Null-activity, the proposed system shows a 2.1% improvement over the baseline system's 97% accuracy.
“our system still maintains a high performance when the Null-activity is included: the system only loses 0.3% accuracy (from 99.4 to 99.1%) showing a significant improvement (2.1%) respect to the baseline system (97%).”
From Feature extraction for robust physical activity recognition (2017) · cites this paper as [24]
Final results show that the proposed system largely improves the performance compared to previous works on the same dataset [24].
“Final results have exhibited that the proposed system largely improves the performance compared to previous works on the same dataset [24].”
From Feature extraction for robust physical activity recognition (2017) · cites this paper as [24]
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- Authors
- Banos, O., Toth, M., Damas, M., Pomares, H., & Rojas, I.
- Affiliations
- Department of Computer Architecture and Computer Technology, Research Center for Information and Communications Technologies-University of Granada (CITIC-UGR), C/Calle Periodista RafaelGomez Montero 2, Granada E18071, Spain
- University of the Basque Country (ES)
- Universidad de Granada (ES)
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2014-06-06
- Volume
- 14
- Issue
- 6
- Pages
- 9995-10023
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC4118358
- OpenAlex
- W2157091296
- Cited by
- 159
- References
- 52
- Citations by year
- 2026: 9 · 2025: 10 · 2024: 10 · 2023: 12 · 2022: 11 · 2021: 17 · 2020: 13 · 2019: 8 · 2018: 20 · 2017: 18 · 2016: 15 · 2015: 13 · 2014: 3
- FWCI
- 14.59
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (submittedVersion, cc-by)
- Communities in ADDI (Universidad del Pais Vasco) (submittedVersion, other-oa)
- Communities in ADDI (University of the Basque Country) (submittedVersion, other-oa)
- Institutional Repository of the University of Granada (University of Granada) (submittedVersion, cc-by-nc-nd)
- LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (submittedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Hispana (submittedVersion, other-oa)
- Funders
- European Commission (264738, 228398)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Health Sciences › Medicine › Physiology › Physical Activity and Health
- Physical Sciences › Engineering › Biomedical Engineering › Advanced Sensor and Energy Harvesting Materials
- Keywords
- wearable activity recognition, sensor displacement, self-placement, sensor placement, wearable sensors
- MeSH terms
- Activities of Daily Living, Adult, Clothing, Equipment Design, Female, Humans, Male, Exercise, Monitoring, Ambulatory, Young Adult
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