PrimSeq: A deep learning-based pipeline to quantitate rehabilitation training.
Stroke rehabilitation seeks to accelerate motor recovery by training functional activities, but may have minimal impact because of insufficient training doses. In animals, training hundreds of functional motions in the first weeks after stroke can substantially boost upper extremity recovery. The op...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2022-01-01
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| Series: | PLOS Digital Health |
| Online Access: | https://doi.org/10.1371/journal.pdig.0000044 |
| _version_ | 1797704248095408128 |
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| author | Avinash Parnandi Aakash Kaku Anita Venkatesan Natasha Pandit Audre Wirtanen Haresh Rajamohan Kannan Venkataramanan Dawn Nilsen Carlos Fernandez-Granda Heidi Schambra |
| author_facet | Avinash Parnandi Aakash Kaku Anita Venkatesan Natasha Pandit Audre Wirtanen Haresh Rajamohan Kannan Venkataramanan Dawn Nilsen Carlos Fernandez-Granda Heidi Schambra |
| author_sort | Avinash Parnandi |
| collection | DOAJ |
| description | Stroke rehabilitation seeks to accelerate motor recovery by training functional activities, but may have minimal impact because of insufficient training doses. In animals, training hundreds of functional motions in the first weeks after stroke can substantially boost upper extremity recovery. The optimal quantity of functional motions to boost recovery in humans is currently unknown, however, because no practical tools exist to measure them during rehabilitation training. Here, we present PrimSeq, a pipeline to classify and count functional motions trained in stroke rehabilitation. Our approach integrates wearable sensors to capture upper-body motion, a deep learning model to predict motion sequences, and an algorithm to tally motions. The trained model accurately decomposes rehabilitation activities into elemental functional motions, outperforming competitive machine learning methods. PrimSeq furthermore quantifies these motions at a fraction of the time and labor costs of human experts. We demonstrate the capabilities of PrimSeq in previously unseen stroke patients with a range of upper extremity motor impairment. We expect that our methodological advances will support the rigorous measurement required for quantitative dosing trials in stroke rehabilitation. |
| first_indexed | 2024-03-12T05:17:31Z |
| format | Article |
| id | doaj.art-3a31e0609a8e4dfba73e3d52b9b83593 |
| institution | Directory Open Access Journal |
| issn | 2767-3170 |
| language | English |
| last_indexed | 2024-03-12T05:17:31Z |
| publishDate | 2022-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLOS Digital Health |
| spelling | doaj.art-3a31e0609a8e4dfba73e3d52b9b835932023-09-03T08:00:15ZengPublic Library of Science (PLoS)PLOS Digital Health2767-31702022-01-0116e000004410.1371/journal.pdig.0000044PrimSeq: A deep learning-based pipeline to quantitate rehabilitation training.Avinash ParnandiAakash KakuAnita VenkatesanNatasha PanditAudre WirtanenHaresh RajamohanKannan VenkataramananDawn NilsenCarlos Fernandez-GrandaHeidi SchambraStroke rehabilitation seeks to accelerate motor recovery by training functional activities, but may have minimal impact because of insufficient training doses. In animals, training hundreds of functional motions in the first weeks after stroke can substantially boost upper extremity recovery. The optimal quantity of functional motions to boost recovery in humans is currently unknown, however, because no practical tools exist to measure them during rehabilitation training. Here, we present PrimSeq, a pipeline to classify and count functional motions trained in stroke rehabilitation. Our approach integrates wearable sensors to capture upper-body motion, a deep learning model to predict motion sequences, and an algorithm to tally motions. The trained model accurately decomposes rehabilitation activities into elemental functional motions, outperforming competitive machine learning methods. PrimSeq furthermore quantifies these motions at a fraction of the time and labor costs of human experts. We demonstrate the capabilities of PrimSeq in previously unseen stroke patients with a range of upper extremity motor impairment. We expect that our methodological advances will support the rigorous measurement required for quantitative dosing trials in stroke rehabilitation.https://doi.org/10.1371/journal.pdig.0000044 |
| spellingShingle | Avinash Parnandi Aakash Kaku Anita Venkatesan Natasha Pandit Audre Wirtanen Haresh Rajamohan Kannan Venkataramanan Dawn Nilsen Carlos Fernandez-Granda Heidi Schambra PrimSeq: A deep learning-based pipeline to quantitate rehabilitation training. PLOS Digital Health |
| title | PrimSeq: A deep learning-based pipeline to quantitate rehabilitation training. |
| title_full | PrimSeq: A deep learning-based pipeline to quantitate rehabilitation training. |
| title_fullStr | PrimSeq: A deep learning-based pipeline to quantitate rehabilitation training. |
| title_full_unstemmed | PrimSeq: A deep learning-based pipeline to quantitate rehabilitation training. |
| title_short | PrimSeq: A deep learning-based pipeline to quantitate rehabilitation training. |
| title_sort | primseq a deep learning based pipeline to quantitate rehabilitation training |
| url | https://doi.org/10.1371/journal.pdig.0000044 |
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