Autoencoder Composite Scoring to Evaluate Prosthetic Performance in Individuals with Lower Limb Amputation
We created an overall assessment metric using a deep learning autoencoder to directly compare clinical outcomes in a comparison of lower limb amputees using two different prosthetic devices—a mechanical knee and a microprocessor-controlled knee. Eight clinical outcomes were distilled into a single m...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Bioengineering |
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| Online Access: | https://www.mdpi.com/2306-5354/9/10/572 |
| _version_ | 1797475244918702080 |
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| author | Thasina Tabashum Ting Xiao Chandrasekaran Jayaraman Chaithanya K. Mummidisetty Arun Jayaraman Mark V. Albert |
| author_facet | Thasina Tabashum Ting Xiao Chandrasekaran Jayaraman Chaithanya K. Mummidisetty Arun Jayaraman Mark V. Albert |
| author_sort | Thasina Tabashum |
| collection | DOAJ |
| description | We created an overall assessment metric using a deep learning autoencoder to directly compare clinical outcomes in a comparison of lower limb amputees using two different prosthetic devices—a mechanical knee and a microprocessor-controlled knee. Eight clinical outcomes were distilled into a single metric using a seven-layer deep autoencoder, with the developed metric compared to similar results from principal component analysis (PCA). The proposed methods were used on data collected from ten participants with a dysvascular transfemoral amputation recruited for a prosthetics research study. This single summary metric permitted a cross-validated reconstruction of all eight scores, accounting for 83.29% of the variance. The derived score is also linked to the overall functional ability in this limited trial population, as improvements in each base clinical score led to increases in this developed metric. There was a highly significant increase in this autoencoder-based metric when the subjects used the microprocessor-controlled knee (<i>p</i> < 0.001, repeated measures ANOVA). A traditional PCA metric led to a similar interpretation but captured only 67.3% of the variance. The autoencoder composite score represents a single-valued, succinct summary that can be useful for the holistic assessment of highly variable, individual scores in limited clinical datasets. |
| first_indexed | 2024-03-09T20:41:24Z |
| format | Article |
| id | doaj.art-fce203edfacc44e8a77b5715f1304023 |
| institution | Directory Open Access Journal |
| issn | 2306-5354 |
| language | English |
| last_indexed | 2024-03-09T20:41:24Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Bioengineering |
| spelling | doaj.art-fce203edfacc44e8a77b5715f13040232023-11-23T22:58:03ZengMDPI AGBioengineering2306-53542022-10-0191057210.3390/bioengineering9100572Autoencoder Composite Scoring to Evaluate Prosthetic Performance in Individuals with Lower Limb AmputationThasina Tabashum0Ting Xiao1Chandrasekaran Jayaraman2Chaithanya K. Mummidisetty3Arun Jayaraman4Mark V. Albert5Department of Computer Science and Engineering, University of North Texas, Denton, TX 76203, USADepartment of Computer Science and Engineering, University of North Texas, Denton, TX 76203, USAMax Näder Lab for Rehabilitation Technologies and Outcomes Research, Shirley Ryan AbilityLab, Chicago, IL 60611, USAMax Näder Lab for Rehabilitation Technologies and Outcomes Research, Shirley Ryan AbilityLab, Chicago, IL 60611, USAMax Näder Lab for Rehabilitation Technologies and Outcomes Research, Shirley Ryan AbilityLab, Chicago, IL 60611, USADepartment of Computer Science and Engineering, University of North Texas, Denton, TX 76203, USAWe created an overall assessment metric using a deep learning autoencoder to directly compare clinical outcomes in a comparison of lower limb amputees using two different prosthetic devices—a mechanical knee and a microprocessor-controlled knee. Eight clinical outcomes were distilled into a single metric using a seven-layer deep autoencoder, with the developed metric compared to similar results from principal component analysis (PCA). The proposed methods were used on data collected from ten participants with a dysvascular transfemoral amputation recruited for a prosthetics research study. This single summary metric permitted a cross-validated reconstruction of all eight scores, accounting for 83.29% of the variance. The derived score is also linked to the overall functional ability in this limited trial population, as improvements in each base clinical score led to increases in this developed metric. There was a highly significant increase in this autoencoder-based metric when the subjects used the microprocessor-controlled knee (<i>p</i> < 0.001, repeated measures ANOVA). A traditional PCA metric led to a similar interpretation but captured only 67.3% of the variance. The autoencoder composite score represents a single-valued, succinct summary that can be useful for the holistic assessment of highly variable, individual scores in limited clinical datasets.https://www.mdpi.com/2306-5354/9/10/572autoencoderprincipal component analysislower limb amputation |
| spellingShingle | Thasina Tabashum Ting Xiao Chandrasekaran Jayaraman Chaithanya K. Mummidisetty Arun Jayaraman Mark V. Albert Autoencoder Composite Scoring to Evaluate Prosthetic Performance in Individuals with Lower Limb Amputation Bioengineering autoencoder principal component analysis lower limb amputation |
| title | Autoencoder Composite Scoring to Evaluate Prosthetic Performance in Individuals with Lower Limb Amputation |
| title_full | Autoencoder Composite Scoring to Evaluate Prosthetic Performance in Individuals with Lower Limb Amputation |
| title_fullStr | Autoencoder Composite Scoring to Evaluate Prosthetic Performance in Individuals with Lower Limb Amputation |
| title_full_unstemmed | Autoencoder Composite Scoring to Evaluate Prosthetic Performance in Individuals with Lower Limb Amputation |
| title_short | Autoencoder Composite Scoring to Evaluate Prosthetic Performance in Individuals with Lower Limb Amputation |
| title_sort | autoencoder composite scoring to evaluate prosthetic performance in individuals with lower limb amputation |
| topic | autoencoder principal component analysis lower limb amputation |
| url | https://www.mdpi.com/2306-5354/9/10/572 |
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