Proposal of FIM Value Estimation Method with Emphasis on Low Frequency using Mel Spectrum Article Swipe
Ryotaro Abe
,
Yosuke Kurihara
,
Yuri Hamada
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.12792/iciae2023.044
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.12792/iciae2023.044
Long-term rehabilitation is necessary to restore motor function that has declined due to a tickle. The functional independence measure (FIM) is used as an index for the recovery of motor function. In this study, we propose a method to automatically estimate motor item scores in the FIM, focusing on trunk movements while getting up from bed. In the proposed method, features are obtained from time-series data rearranged using the Mel-frequency cepstrum coefficient, with features in the high-frequency domain diluted and those in the low-frequency domain weighted. The mel scale and the number of mel filter bank was determined using a genetic algorithm, and the FIM was estimated by applying linear regression to the features based on the model parameters. The validated experimental results demonstrated that the FIM was estimated with an average absolute error of 5.72.
Related Topics
Concepts
Frequency domain
Mel-frequency cepstrum
Functional Independence Measure
Computer science
Measure (data warehouse)
Independence (probability theory)
Filter (signal processing)
Linear regression
Mathematics
Term (time)
Pattern recognition (psychology)
Statistics
Artificial intelligence
Feature extraction
Rehabilitation
Data mining
Computer vision
Biology
Quantum mechanics
Neuroscience
Physics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.12792/iciae2023.044
- https://www2.ia-engineers.org/conference/index.php/iciae/iciae2023/paper/download/2746/1786
- OA Status
- gold
- References
- 2
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4367460376
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4367460376Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.12792/iciae2023.044Digital Object Identifier
- Title
-
Proposal of FIM Value Estimation Method with Emphasis on Low Frequency using Mel SpectrumWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-01-01Full publication date if available
- Authors
-
Ryotaro Abe, Yosuke Kurihara, Yuri HamadaList of authors in order
- Landing page
-
https://doi.org/10.12792/iciae2023.044Publisher landing page
- PDF URL
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https://www2.ia-engineers.org/conference/index.php/iciae/iciae2023/paper/download/2746/1786Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www2.ia-engineers.org/conference/index.php/iciae/iciae2023/paper/download/2746/1786Direct OA link when available
- Concepts
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Frequency domain, Mel-frequency cepstrum, Functional Independence Measure, Computer science, Measure (data warehouse), Independence (probability theory), Filter (signal processing), Linear regression, Mathematics, Term (time), Pattern recognition (psychology), Statistics, Artificial intelligence, Feature extraction, Rehabilitation, Data mining, Computer vision, Biology, Quantum mechanics, Neuroscience, PhysicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
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2Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.absolute | 132 |
| abstract_inverted_index.applying | 108 |
| abstract_inverted_index.cepstrum | 70 |
| abstract_inverted_index.declined | 10 |
| abstract_inverted_index.estimate | 40 |
| abstract_inverted_index.features | 60, 73, 113 |
| abstract_inverted_index.focusing | 47 |
| abstract_inverted_index.function | 7 |
| abstract_inverted_index.obtained | 62 |
| abstract_inverted_index.proposed | 58 |
| abstract_inverted_index.recovery | 27 |
| abstract_inverted_index.Long-term | 0 |
| abstract_inverted_index.estimated | 106, 128 |
| abstract_inverted_index.function. | 30 |
| abstract_inverted_index.movements | 50 |
| abstract_inverted_index.necessary | 3 |
| abstract_inverted_index.validated | 120 |
| abstract_inverted_index.weighted. | 85 |
| abstract_inverted_index.algorithm, | 101 |
| abstract_inverted_index.determined | 97 |
| abstract_inverted_index.functional | 16 |
| abstract_inverted_index.rearranged | 66 |
| abstract_inverted_index.regression | 110 |
| abstract_inverted_index.parameters. | 118 |
| abstract_inverted_index.time-series | 64 |
| abstract_inverted_index.coefficient, | 71 |
| abstract_inverted_index.demonstrated | 123 |
| abstract_inverted_index.experimental | 121 |
| abstract_inverted_index.independence | 17 |
| abstract_inverted_index.Mel-frequency | 69 |
| abstract_inverted_index.automatically | 39 |
| abstract_inverted_index.low-frequency | 83 |
| abstract_inverted_index.high-frequency | 76 |
| abstract_inverted_index.rehabilitation | 1 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile.value | 0.06302809 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |