Wearable Accelerometer and Gyroscope Sensors for Estimating the Severity of Essential Tremor
Background: Several validated clinical scales measure the severity of essential tremor (ET). Their assessments are subjective and can depend on familiarity and training with scoring systems. Method: We propose a multi-modal sensing using a wearable inertial measurement unit for estimating scores on...
| Main Authors: | , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
|
| Series: | IEEE Journal of Translational Engineering in Health and Medicine |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10304233/ |
| _version_ | 1797361489606082560 |
|---|---|
| author | Sheik Mohammed Ali Sridhar Poosapadi Arjunan James Peter Laura Perju-Dumbrava Catherine Ding Michael Eller Sanjay Raghav Peter Kempster Mohammod Abdul Motin P. J. Radcliffe Dinesh Kant Kumar |
| author_facet | Sheik Mohammed Ali Sridhar Poosapadi Arjunan James Peter Laura Perju-Dumbrava Catherine Ding Michael Eller Sanjay Raghav Peter Kempster Mohammod Abdul Motin P. J. Radcliffe Dinesh Kant Kumar |
| author_sort | Sheik Mohammed Ali |
| collection | DOAJ |
| description | Background: Several validated clinical scales measure the severity of essential tremor (ET). Their assessments are subjective and can depend on familiarity and training with scoring systems. Method: We propose a multi-modal sensing using a wearable inertial measurement unit for estimating scores on the Fahn-Tolosa-Marin tremor rating scale (FTM) and determine the classification accuracy within the tremor type. 17 ET participants and 18 healthy controls were recruited for the study. Two movement disorder neurologists who were blinded to prior clinical information viewed video recordings and scored the FTM. Participants drew a guided Archimedes spiral while wearing an inertial measurement unit placed at the mid-point between the lateral epicondyle of the humerus and the anatomical snuff box. Acceleration and gyroscope recordings were analyzed. The ratio of the power spectral density between frequency bands 0.5-4 Hz and 4–12 Hz, and the sum of power spectrum density over the entire spectrum of 2–74 Hz, for both accelerometer and gyroscope data, were computed. FTM was estimated using regression model and classification using SVM was validated using the leave-one-out method. Results: Regression analysis showed a moderate to good correlation when individual features were used, while correlation was high (<inline-formula> <tex-math notation="LaTeX">$r^{2}$ </tex-math></inline-formula> = 0.818) when suitable features of the gyro and accelerometer were combined. The accuracy for two-class classification of the combined features using SVM was 91.42% while for four-class it was 68.57%. Conclusion: Potential applications of this novel wearable sensing method using a wearable Inertial Measurement Unit (IMU) include monitoring of ET and clinical trials of new treatments for the disorder. |
| first_indexed | 2024-03-08T15:54:25Z |
| format | Article |
| id | doaj.art-2a81847374b24129b1d4e970c5f2a775 |
| institution | Directory Open Access Journal |
| issn | 2168-2372 |
| language | English |
| last_indexed | 2024-03-08T15:54:25Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Translational Engineering in Health and Medicine |
| spelling | doaj.art-2a81847374b24129b1d4e970c5f2a7752024-01-09T00:01:54ZengIEEEIEEE Journal of Translational Engineering in Health and Medicine2168-23722024-01-011219420310.1109/JTEHM.2023.332934410304233Wearable Accelerometer and Gyroscope Sensors for Estimating the Severity of Essential TremorSheik Mohammed Ali0Sridhar Poosapadi Arjunan1https://orcid.org/0000-0002-7288-0380James Peter2Laura Perju-Dumbrava3Catherine Ding4Michael Eller5Sanjay Raghav6Peter Kempster7https://orcid.org/0000-0002-6321-3930Mohammod Abdul Motin8https://orcid.org/0000-0003-1618-3772P. J. Radcliffe9Dinesh Kant Kumar10https://orcid.org/0000-0003-3602-4023Department of Electrical and Biomedical Engineering, RMIT University, Melbourne, VIC, AustraliaSRM Institute of Science and Technology, Chennai, IndiaNeurosciences Department, Monash Health, Clayton, VIC, AustraliaNeurosciences Department, Monash Health, Clayton, VIC, AustraliaNeurosciences Department, Monash Health, Clayton, VIC, AustraliaNeurosciences Department, Monash Health, Clayton, VIC, AustraliaDepartment of Electrical and Biomedical Engineering, RMIT University, Melbourne, VIC, AustraliaNeurosciences Department, Monash Health, Clayton, VIC, AustraliaDepartment of Electrical and Biomedical Engineering, RMIT University, Melbourne, VIC, AustraliaDepartment of Electrical and Biomedical Engineering, RMIT University, Melbourne, VIC, AustraliaDepartment of Electrical and Biomedical Engineering, RMIT University, Melbourne, VIC, AustraliaBackground: Several validated clinical scales measure the severity of essential tremor (ET). Their assessments are subjective and can depend on familiarity and training with scoring systems. Method: We propose a multi-modal sensing using a wearable inertial measurement unit for estimating scores on the Fahn-Tolosa-Marin tremor rating scale (FTM) and determine the classification accuracy within the tremor type. 17 ET participants and 18 healthy controls were recruited for the study. Two movement disorder neurologists who were blinded to prior clinical information viewed video recordings and scored the FTM. Participants drew a guided Archimedes spiral while wearing an inertial measurement unit placed at the mid-point between the lateral epicondyle of the humerus and the anatomical snuff box. Acceleration and gyroscope recordings were analyzed. The ratio of the power spectral density between frequency bands 0.5-4 Hz and 4–12 Hz, and the sum of power spectrum density over the entire spectrum of 2–74 Hz, for both accelerometer and gyroscope data, were computed. FTM was estimated using regression model and classification using SVM was validated using the leave-one-out method. Results: Regression analysis showed a moderate to good correlation when individual features were used, while correlation was high (<inline-formula> <tex-math notation="LaTeX">$r^{2}$ </tex-math></inline-formula> = 0.818) when suitable features of the gyro and accelerometer were combined. The accuracy for two-class classification of the combined features using SVM was 91.42% while for four-class it was 68.57%. Conclusion: Potential applications of this novel wearable sensing method using a wearable Inertial Measurement Unit (IMU) include monitoring of ET and clinical trials of new treatments for the disorder.https://ieeexplore.ieee.org/document/10304233/Essential tremorFahn-Tolosa-Marin tremor rating scalewearablesinertial measurement unit |
| spellingShingle | Sheik Mohammed Ali Sridhar Poosapadi Arjunan James Peter Laura Perju-Dumbrava Catherine Ding Michael Eller Sanjay Raghav Peter Kempster Mohammod Abdul Motin P. J. Radcliffe Dinesh Kant Kumar Wearable Accelerometer and Gyroscope Sensors for Estimating the Severity of Essential Tremor IEEE Journal of Translational Engineering in Health and Medicine Essential tremor Fahn-Tolosa-Marin tremor rating scale wearables inertial measurement unit |
| title | Wearable Accelerometer and Gyroscope Sensors for Estimating the Severity of Essential Tremor |
| title_full | Wearable Accelerometer and Gyroscope Sensors for Estimating the Severity of Essential Tremor |
| title_fullStr | Wearable Accelerometer and Gyroscope Sensors for Estimating the Severity of Essential Tremor |
| title_full_unstemmed | Wearable Accelerometer and Gyroscope Sensors for Estimating the Severity of Essential Tremor |
| title_short | Wearable Accelerometer and Gyroscope Sensors for Estimating the Severity of Essential Tremor |
| title_sort | wearable accelerometer and gyroscope sensors for estimating the severity of essential tremor |
| topic | Essential tremor Fahn-Tolosa-Marin tremor rating scale wearables inertial measurement unit |
| url | https://ieeexplore.ieee.org/document/10304233/ |
| work_keys_str_mv | AT sheikmohammedali wearableaccelerometerandgyroscopesensorsforestimatingtheseverityofessentialtremor AT sridharpoosapadiarjunan wearableaccelerometerandgyroscopesensorsforestimatingtheseverityofessentialtremor AT jamespeter wearableaccelerometerandgyroscopesensorsforestimatingtheseverityofessentialtremor AT lauraperjudumbrava wearableaccelerometerandgyroscopesensorsforestimatingtheseverityofessentialtremor AT catherineding wearableaccelerometerandgyroscopesensorsforestimatingtheseverityofessentialtremor AT michaeleller wearableaccelerometerandgyroscopesensorsforestimatingtheseverityofessentialtremor AT sanjayraghav wearableaccelerometerandgyroscopesensorsforestimatingtheseverityofessentialtremor AT peterkempster wearableaccelerometerandgyroscopesensorsforestimatingtheseverityofessentialtremor AT mohammodabdulmotin wearableaccelerometerandgyroscopesensorsforestimatingtheseverityofessentialtremor AT pjradcliffe wearableaccelerometerandgyroscopesensorsforestimatingtheseverityofessentialtremor AT dineshkantkumar wearableaccelerometerandgyroscopesensorsforestimatingtheseverityofessentialtremor |