Impact of Wearable Measurement Properties and Data Quality on ADLs Classification Accuracy
Open accessAbstract
In the field of automatic recognition and classification of Activities of Daily Living (ADLs), a paramount role to determine the classification accuracy is played by sensor technologies, as the algorithms’ performance is highly affected by the nature and quality of the collected measurement data. This work aims to investigate the influence of the wearable device characteristics and measurement uncertainty on the classification accuracy. For this study, two wearables devices are considered: a top-quality smartwatch (Empatica E4) and a low-cost Arduino-based wristband prototype. These devices have been used to measure the acceleration signal at the dominant wrist of subjects performing some relevant activities in real-life conditions. The experimental evaluation of some ADLs classification algorithms shows that their accuracy fluctuates depending on the choice of the sensor, which in turn affects the amount and type of relevant features to process. As such, the combination of features’ domain, i.e. time or frequency, number and type, which leads to the best classification accuracy has to be tuned on a specific sensor basis, despite the same type of signal, i.e. acceleration, is measured and processed under identical circumstances and processed. Accuracy values of 50-99% and 66-95% in the ADLs classification, are obtained for Empatica E4 and Arduino-based prototype, respectively; the best performance among classifiers is obtained with J48 and Random Forest, confirming that, with an appropriate configuration, satisfactory accuracy may be attained, even by resorting to the use of simple sensors.
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Information coming from wearable devices and other sources can be inaccurate, inconsistent, or erroneous.
“Information coming from wearable devices and other sources can be inaccurate, inconsistent, or erroneous.”
From Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions (2023) · cites this paper as [96]
Inaccurate, inconsistent, or erroneous information from wearable devices and other sources can lead to inaccurate predictions and false warnings.
“All of this can lead to inaccurate predictions and false warnings [95,96].”
From Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions (2023) · cites this paper as [96]
Another obstacle AI algorithms face is the quality and accuracy of the data they analyze.
“Another obstacle AI algorithms face is the quality and accuracy of the data they analyze.”
From Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions (2023) · cites this paper as [96]
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- Authors
- Poli, A., Cosoli, G., Scalise, L., & Spinsante, S.
- Affiliations
- Marche Polytechnic University (IT)
- Venue
- IEEE Sensors Journal
- Published
- 2021-07-01
- Volume
- 21
- Issue
- 13
- Pages
- 14221-14231
- ISSN
- 1530-437X (ISSN-L)
- 1558-1748
- 2379-9153
- OpenAlex
- W3043128363
- Cited by
- 33
- References
- 54
- Citations by year
- 2025: 1 · 2024: 5 · 2023: 5 · 2022: 10 · 2021: 11 · 2020: 1
- FWCI
- 2.62
- Type
- journal article
- Language
- en
- Open access
- green
- ieeexplore.ieee.org
- Licence
- Locations
- IEEE Sensors Journal (publishedVersion)
- IRIS eCampus Telematic University (Università degli Studi eCampus) (submittedVersion)
- Università Politecnica delle Marche (Università Politecnica delle Marche) (submittedVersion, other-oa)
- Funders
- More Years Better Lives JPI and Italian Ministero dellIstruzione Universit e Ricerca MIUR (JPI MYBL award number: PAAL_JTC2017)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Keywords
- activities of daily living, wearable sensors, smartwatches, Empatica E4, measurement uncertainty, data quality, acceleration signals, feature selection, time and frequency domain features, low-cost sensors, classification accuracy
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