Objective Assessment of Upper-Extremity Motor Functions in Spinocerebellar Ataxia Using Wearable Sensors
The study presents a novel approach to objectively assessing the upper-extremity motor symptoms in spinocerebellar ataxia (SCA) using data collected via a wearable sensor worn on the patient’s wrist during upper-extremity tasks associated with the Assessment and Rating of Ataxia (SARA). Fi...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
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Multidisciplinary Digital Publishing Institute
2022
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| Online Access: | https://hdl.handle.net/1721.1/145995 |
| _version_ | 1826216506909786112 |
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| author | Mohammadi-Ghazi, Reza Nguyen, Hung Mishra, Ram Kinker Enriquez, Ana Najafi, Bijan Stephen, Christopher D. Gupta, Anoopum S. Schmahmann, Jeremy D. Vaziri, Ashkan |
| author2 | Massachusetts Institute of Technology. Department of Civil and Environmental Engineering. Laboratory for Infrastructure Science and Sustainability |
| author_facet | Massachusetts Institute of Technology. Department of Civil and Environmental Engineering. Laboratory for Infrastructure Science and Sustainability Mohammadi-Ghazi, Reza Nguyen, Hung Mishra, Ram Kinker Enriquez, Ana Najafi, Bijan Stephen, Christopher D. Gupta, Anoopum S. Schmahmann, Jeremy D. Vaziri, Ashkan |
| author_sort | Mohammadi-Ghazi, Reza |
| collection | MIT |
| description | The study presents a novel approach to objectively assessing the upper-extremity motor symptoms in spinocerebellar ataxia (SCA) using data collected via a wearable sensor worn on the patient’s wrist during upper-extremity tasks associated with the Assessment and Rating of Ataxia (SARA). First, we developed an algorithm for detecting/extracting the cycles of the finger-to-nose test (FNT). We extracted multiple features from the detected cycles and identified features and parameters correlated with the SARA scores. Additionally, we developed models to predict the severity of symptoms based on the FNT. The proposed technique was validated on a dataset comprising the seventeen (<i>n</i> = 17) participants’ assessments. The cycle detection technique showed an accuracy of 97.6% in a Bland–Altman analysis and a 94% accuracy (F1-score of 0.93) in predicting the severity of the FNT. Furthermore, the dependency of the upper-extremity tests was investigated through statistical analysis, and the results confirm dependency and potential redundancies in the upper-extremity SARA assessments. Our findings pave the way to enhance the utility of objective measures of SCA assessments. The proposed wearable-based platform has the potential to eliminate subjectivity and inter-rater variabilities in assessing ataxia. |
| first_indexed | 2024-09-23T16:48:27Z |
| format | Article |
| id | mit-1721.1/145995 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T16:48:27Z |
| publishDate | 2022 |
| publisher | Multidisciplinary Digital Publishing Institute |
| record_format | dspace |
| spelling | mit-1721.1/1459952023-06-28T20:39:31Z Objective Assessment of Upper-Extremity Motor Functions in Spinocerebellar Ataxia Using Wearable Sensors Mohammadi-Ghazi, Reza Nguyen, Hung Mishra, Ram Kinker Enriquez, Ana Najafi, Bijan Stephen, Christopher D. Gupta, Anoopum S. Schmahmann, Jeremy D. Vaziri, Ashkan Massachusetts Institute of Technology. Department of Civil and Environmental Engineering. Laboratory for Infrastructure Science and Sustainability The study presents a novel approach to objectively assessing the upper-extremity motor symptoms in spinocerebellar ataxia (SCA) using data collected via a wearable sensor worn on the patient’s wrist during upper-extremity tasks associated with the Assessment and Rating of Ataxia (SARA). First, we developed an algorithm for detecting/extracting the cycles of the finger-to-nose test (FNT). We extracted multiple features from the detected cycles and identified features and parameters correlated with the SARA scores. Additionally, we developed models to predict the severity of symptoms based on the FNT. The proposed technique was validated on a dataset comprising the seventeen (<i>n</i> = 17) participants’ assessments. The cycle detection technique showed an accuracy of 97.6% in a Bland–Altman analysis and a 94% accuracy (F1-score of 0.93) in predicting the severity of the FNT. Furthermore, the dependency of the upper-extremity tests was investigated through statistical analysis, and the results confirm dependency and potential redundancies in the upper-extremity SARA assessments. Our findings pave the way to enhance the utility of objective measures of SCA assessments. The proposed wearable-based platform has the potential to eliminate subjectivity and inter-rater variabilities in assessing ataxia. 2022-10-26T17:35:26Z 2022-10-26T17:35:26Z 2022-10-20 2022-10-26T11:07:58Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/145995 Sensors 22 (20): 7993 (2022) PUBLISHER_CC http://dx.doi.org/10.3390/s22207993 Creative Commons Attribution https://creativecommons.org/licenses/by/4.0/ application/pdf Multidisciplinary Digital Publishing Institute Multidisciplinary Digital Publishing Institute |
| spellingShingle | Mohammadi-Ghazi, Reza Nguyen, Hung Mishra, Ram Kinker Enriquez, Ana Najafi, Bijan Stephen, Christopher D. Gupta, Anoopum S. Schmahmann, Jeremy D. Vaziri, Ashkan Objective Assessment of Upper-Extremity Motor Functions in Spinocerebellar Ataxia Using Wearable Sensors |
| title | Objective Assessment of Upper-Extremity Motor Functions in Spinocerebellar Ataxia Using Wearable Sensors |
| title_full | Objective Assessment of Upper-Extremity Motor Functions in Spinocerebellar Ataxia Using Wearable Sensors |
| title_fullStr | Objective Assessment of Upper-Extremity Motor Functions in Spinocerebellar Ataxia Using Wearable Sensors |
| title_full_unstemmed | Objective Assessment of Upper-Extremity Motor Functions in Spinocerebellar Ataxia Using Wearable Sensors |
| title_short | Objective Assessment of Upper-Extremity Motor Functions in Spinocerebellar Ataxia Using Wearable Sensors |
| title_sort | objective assessment of upper extremity motor functions in spinocerebellar ataxia using wearable sensors |
| url | https://hdl.handle.net/1721.1/145995 |
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