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Assessing the Effec...
Assessing the Effect of Data Quality on Distance Estimation in Smartphone-Based Outdoor 6MWT
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- Caramaschi, Sara, 1998- (författare)
- Malmö universitet,Institutionen för datavetenskap och medieteknik (DVMT),Internet of Things and People (IOTAP)
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- Olsson, Carl Magnus (författare)
- Malmö universitet,Institutionen för datavetenskap och medieteknik (DVMT),Internet of Things and People (IOTAP)
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- Orchard, Elizabeth (författare)
- Oxford Univ Hosp NHS Fdn Trust, Oxford OX3 7JX, England.
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- Molloy, Jackson (författare)
- Oxford Univ Hosp NHS Fdn Trust, Oxford OX3 7JX, England.
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- Salvi, Dario (författare)
- Malmö universitet,Internet of Things and People (IOTAP),Institutionen för datavetenskap och medieteknik (DVMT)
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(creator_code:org_t)
- MDPI, 2024
- 2024
- Engelska.
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Ingår i: Sensors. - : MDPI. - 1424-8220. ; 24:8
- Relaterad länk:
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https://doi.org/10.3...
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https://mau.diva-por... (primary) (Raw object)
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https://urn.kb.se/re...
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https://doi.org/10.3...
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Abstract
Ämnesord
Stäng
- As a result of technological advancements, functional capacity assessments, such as the 6-minute walk test, can be performed remotely, at home and in the community. Current studies, however, tend to overlook the crucial aspect of data quality, often limiting their focus to idealised scenarios. Challenging conditions may arise when performing a test given the risk of collecting poor-quality GNSS signal, which can undermine the reliability of the results. This work shows the impact of applying filtering rules to avoid noisy samples in common algorithms that compute the walked distance from positioning data. Then, based on signal features, we assess the reliability of the distance estimation using logistic regression from the following two perspectives: error-based analysis, which relates to the estimated distance error, and user-based analysis, which distinguishes conventional from unconventional tests based on users' previous annotations. We highlight the impact of features associated with walked path irregularity and direction changes to establish data quality. We evaluate features within a binary classification task and reach an F1-score of 0.93 and an area under the curve of 0.97 for the user-based classification. Identifying unreliable tests is helpful to clinicians, who receive the recorded test results accompanied by quality assessments, and to patients, who can be given the opportunity to repeat tests classified as not following the instructions.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences (hsv//eng)
Nyckelord
- 6MWT
- distance estimation
- data reliability
- physical assessment
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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