Quantifying the Uncertainty of Human Activity Recognition Using a Bayesian Machine Learning Method: A Prediction Study Article Swipe
Hiroshi Mamiya
,
Daniel Fuller
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1101/2023.08.16.23294126
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1101/2023.08.16.23294126
Background Machine learning methods accurately predict physical activity outcomes using accelerometer data generated by wearable devices, thus allowing the investigation of the impact of built environment on population physical activity. While traditional machine learning methods do not provide prediction uncertainty, a new method, Bayesian Additive Regression Trees (BART) can quantify such uncertainty as posterior predictive distribution. We evaluated the performance of BART in predicting physical activity status. Methods We applied multinomial BART and the benchmark method, random forest, to accelerometer data in 25,424 time points, which were generated by wearable devices attached to 37 participants. We evaluated prediction accuracies and confusion matrix using leave-one-out cross-validation. Results BART and random forest demonstrated comparable accuracies in prediction. Conclusions BART is a relatively novel ML method and will advance the incorporation of predicted physical activity status into built environment research. Future research includes the evaluation of the association between the built environment and predicted physical activity with and without accounting for prediction uncertainty.
Related Topics
Concepts
Random forest
Machine learning
Computer science
Artificial intelligence
Benchmark (surveying)
Bayesian probability
Accelerometer
Wearable computer
Confusion matrix
Activity recognition
Regression
Predictive modelling
Naive Bayes classifier
Support vector machine
Data mining
Statistics
Mathematics
Operating system
Geography
Embedded system
Geodesy
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2023.08.16.23294126
- https://www.medrxiv.org/content/medrxiv/early/2023/08/22/2023.08.16.23294126.full.pdf
- OA Status
- green
- Cited By
- 1
- References
- 26
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4386047552
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4386047552Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1101/2023.08.16.23294126Digital Object Identifier
- Title
-
Quantifying the Uncertainty of Human Activity Recognition Using a Bayesian Machine Learning Method: A Prediction StudyWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-08-22Full publication date if available
- Authors
-
Hiroshi Mamiya, Daniel FullerList of authors in order
- Landing page
-
https://doi.org/10.1101/2023.08.16.23294126Publisher landing page
- PDF URL
-
https://www.medrxiv.org/content/medrxiv/early/2023/08/22/2023.08.16.23294126.full.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://www.medrxiv.org/content/medrxiv/early/2023/08/22/2023.08.16.23294126.full.pdfDirect OA link when available
- Concepts
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Random forest, Machine learning, Computer science, Artificial intelligence, Benchmark (surveying), Bayesian probability, Accelerometer, Wearable computer, Confusion matrix, Activity recognition, Regression, Predictive modelling, Naive Bayes classifier, Support vector machine, Data mining, Statistics, Mathematics, Operating system, Geography, Embedded system, GeodesyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
- References (count)
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26Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.While | 31 |
| abstract_inverted_index.built | 25, 135, 148 |
| abstract_inverted_index.novel | 121 |
| abstract_inverted_index.using | 10, 103 |
| abstract_inverted_index.which | 86 |
| abstract_inverted_index.(BART) | 48 |
| abstract_inverted_index.25,424 | 83 |
| abstract_inverted_index.Future | 138 |
| abstract_inverted_index.forest | 110 |
| abstract_inverted_index.impact | 23 |
| abstract_inverted_index.matrix | 102 |
| abstract_inverted_index.method | 123 |
| abstract_inverted_index.random | 77, 109 |
| abstract_inverted_index.status | 133 |
| abstract_inverted_index.Machine | 2 |
| abstract_inverted_index.Methods | 68 |
| abstract_inverted_index.Results | 106 |
| abstract_inverted_index.advance | 126 |
| abstract_inverted_index.applied | 70 |
| abstract_inverted_index.between | 146 |
| abstract_inverted_index.devices | 91 |
| abstract_inverted_index.forest, | 78 |
| abstract_inverted_index.machine | 33 |
| abstract_inverted_index.method, | 43, 76 |
| abstract_inverted_index.methods | 4, 35 |
| abstract_inverted_index.points, | 85 |
| abstract_inverted_index.predict | 6 |
| abstract_inverted_index.provide | 38 |
| abstract_inverted_index.status. | 67 |
| abstract_inverted_index.without | 156 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.Additive | 45 |
| abstract_inverted_index.Bayesian | 44 |
| abstract_inverted_index.activity | 8, 66, 132, 153 |
| abstract_inverted_index.allowing | 18 |
| abstract_inverted_index.attached | 92 |
| abstract_inverted_index.devices, | 16 |
| abstract_inverted_index.includes | 140 |
| abstract_inverted_index.learning | 3, 34 |
| abstract_inverted_index.outcomes | 9 |
| abstract_inverted_index.physical | 7, 29, 65, 131, 152 |
| abstract_inverted_index.quantify | 50 |
| abstract_inverted_index.research | 139 |
| abstract_inverted_index.wearable | 15, 90 |
| abstract_inverted_index.activity. | 30 |
| abstract_inverted_index.benchmark | 75 |
| abstract_inverted_index.confusion | 101 |
| abstract_inverted_index.evaluated | 58, 97 |
| abstract_inverted_index.generated | 13, 88 |
| abstract_inverted_index.posterior | 54 |
| abstract_inverted_index.predicted | 130, 151 |
| abstract_inverted_index.research. | 137 |
| abstract_inverted_index.Background | 1 |
| abstract_inverted_index.Regression | 46 |
| abstract_inverted_index.accounting | 157 |
| abstract_inverted_index.accuracies | 99, 113 |
| abstract_inverted_index.accurately | 5 |
| abstract_inverted_index.comparable | 112 |
| abstract_inverted_index.evaluation | 142 |
| abstract_inverted_index.population | 28 |
| abstract_inverted_index.predicting | 64 |
| abstract_inverted_index.prediction | 39, 98, 159 |
| abstract_inverted_index.predictive | 55 |
| abstract_inverted_index.relatively | 120 |
| abstract_inverted_index.Conclusions | 116 |
| abstract_inverted_index.association | 145 |
| abstract_inverted_index.environment | 26, 136, 149 |
| abstract_inverted_index.multinomial | 71 |
| abstract_inverted_index.performance | 60 |
| abstract_inverted_index.prediction. | 115 |
| abstract_inverted_index.traditional | 32 |
| abstract_inverted_index.uncertainty | 52 |
| abstract_inverted_index.demonstrated | 111 |
| abstract_inverted_index.uncertainty, | 40 |
| abstract_inverted_index.uncertainty. | 160 |
| abstract_inverted_index.accelerometer | 11, 80 |
| abstract_inverted_index.distribution. | 56 |
| abstract_inverted_index.incorporation | 128 |
| abstract_inverted_index.investigation | 20 |
| abstract_inverted_index.leave-one-out | 104 |
| abstract_inverted_index.participants. | 95 |
| abstract_inverted_index.cross-validation. | 105 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| corresponding_author_ids | https://openalex.org/A5034543482 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 2 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/15 |
| sustainable_development_goals[0].score | 0.5199999809265137 |
| sustainable_development_goals[0].display_name | Life in Land |
| citation_normalized_percentile.value | 0.54652681 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |