A Novel Method for Tracking Neck Motions Using a Skin-Conformable Wireless Accelerometer: A Pilot Study
Introduction: Cervical spine disease is a leading cause of pain and disability. Degenerative conditions of the spine can result in neurologic compression of the cervical spinal cord or nerve roots and may be surgically treated with an anterior cervical discectomy and fusion (ACDF) in up to 137,000 p...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Karger Publishers
2024-04-01
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| Series: | Digital Biomarkers |
| Subjects: | |
| Online Access: | https://beta.karger.com/Article/FullText/536473 |
| _version_ | 1797201851445149696 |
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| author | Le Huang Keum San Chun Lian Yu Jong Yoon Lee Alan Soetikno Hope Chen Hyoyoung Jeong Joshua Barrett Knute Martell Youn Kang Alpesh A Patel Shuai Xu |
| author_facet | Le Huang Keum San Chun Lian Yu Jong Yoon Lee Alan Soetikno Hope Chen Hyoyoung Jeong Joshua Barrett Knute Martell Youn Kang Alpesh A Patel Shuai Xu |
| author_sort | Le Huang |
| collection | DOAJ |
| description | Introduction: Cervical spine disease is a leading cause of pain and disability. Degenerative conditions of the spine can result in neurologic compression of the cervical spinal cord or nerve roots and may be surgically treated with an anterior cervical discectomy and fusion (ACDF) in up to 137,000 people per year in the United States. A common sequelae of ACDF is reduced cervical range of motion (CROM) with patient-based complaints of stiffness and neck pain. Currently, tools for assessment of CROM are manual, subjective, and only intermittently utilized during doctor or physical therapy visits. We propose a skin-mountable acousto-mechanic sensor (ADvanced Acousto-Mechanic sensor; ADAM) as a tool for continuous neck motion monitoring in postoperative ACDF patients. We have developed and validated a machine learning neck motion classification algorithm to differentiate between eight neck motions (right/left rotation, right/left lateral bending, flexion, extension, retraction, protraction) in healthy normal subjects and patients. Methods: Sensor data from 12 healthy normal subjects and 5 patients were used to develop and validate a Convolutional Neural Network (CNN). Results: An average algorithm accuracy of 80.0 ± 3.8% was obtained for healthy normal subjects (94% for right rotation, 98% for left rotation, 65% for right lateral bending, 87% for left lateral bending, 89% for flexion, 77% for extension, 50% for retraction, 84% for protraction). An average accuracy of 67.5 ± 5.8% was obtained for patients. Discussion: ADAM, with our algorithm, may serve as a rehabilitation tool for neck motion monitoring in postoperative ACDF patients. Sensor-captured vital signs and other events (extubation, vocalization, physical therapy, walking) are potential metrics to be incorporated into our algorithm to offer more holistic monitoring of patients after cervical spine surgery. |
| first_indexed | 2024-04-24T07:54:07Z |
| format | Article |
| id | doaj.art-25f258debfd34f30a3ccdf9004a46043 |
| institution | Directory Open Access Journal |
| issn | 2504-110X |
| language | English |
| last_indexed | 2024-04-24T07:54:07Z |
| publishDate | 2024-04-01 |
| publisher | Karger Publishers |
| record_format | Article |
| series | Digital Biomarkers |
| spelling | doaj.art-25f258debfd34f30a3ccdf9004a460432024-04-18T07:17:49ZengKarger PublishersDigital Biomarkers2504-110X2024-04-0181405110.1159/000536473536473A Novel Method for Tracking Neck Motions Using a Skin-Conformable Wireless Accelerometer: A Pilot StudyLe Huang0Keum San Chun1Lian Yu2Jong Yoon Lee3Alan Soetikno4Hope Chen5Hyoyoung Jeong6Joshua Barrett7Knute Martell8Youn Kang9Alpesh A Patel10Shuai Xu11Feinberg School of Medicine, Northwestern University, Chicago, IL, USASibel Health, Niles, IL, USASibel Health, Niles, IL, USASibel Health, Niles, IL, USAFeinberg School of Medicine, Northwestern University, Chicago, IL, USAFeinberg School of Medicine, Northwestern University, Chicago, IL, USAElectrical and Computer Engineering, University of California Davis, Davis, CA, USADepartment of Orthopaedic Surgery, Northwestern University Feinberg School of Medicine, Chicago, IL, USAQuerrey Simpson Institute for Bioelectronics, Northwestern University, Evanston, IL, USADepartment of Ocean System Engineering, Jeju National University, Jeju, Republic of KoreaDepartments of Orthopaedic Surgery and Neurosurgery, Northwestern University Feinberg School of Medicine, Chicago, IL, USASibel Health, Niles, IL, USAIntroduction: Cervical spine disease is a leading cause of pain and disability. Degenerative conditions of the spine can result in neurologic compression of the cervical spinal cord or nerve roots and may be surgically treated with an anterior cervical discectomy and fusion (ACDF) in up to 137,000 people per year in the United States. A common sequelae of ACDF is reduced cervical range of motion (CROM) with patient-based complaints of stiffness and neck pain. Currently, tools for assessment of CROM are manual, subjective, and only intermittently utilized during doctor or physical therapy visits. We propose a skin-mountable acousto-mechanic sensor (ADvanced Acousto-Mechanic sensor; ADAM) as a tool for continuous neck motion monitoring in postoperative ACDF patients. We have developed and validated a machine learning neck motion classification algorithm to differentiate between eight neck motions (right/left rotation, right/left lateral bending, flexion, extension, retraction, protraction) in healthy normal subjects and patients. Methods: Sensor data from 12 healthy normal subjects and 5 patients were used to develop and validate a Convolutional Neural Network (CNN). Results: An average algorithm accuracy of 80.0 ± 3.8% was obtained for healthy normal subjects (94% for right rotation, 98% for left rotation, 65% for right lateral bending, 87% for left lateral bending, 89% for flexion, 77% for extension, 50% for retraction, 84% for protraction). An average accuracy of 67.5 ± 5.8% was obtained for patients. Discussion: ADAM, with our algorithm, may serve as a rehabilitation tool for neck motion monitoring in postoperative ACDF patients. Sensor-captured vital signs and other events (extubation, vocalization, physical therapy, walking) are potential metrics to be incorporated into our algorithm to offer more holistic monitoring of patients after cervical spine surgery.https://beta.karger.com/Article/FullText/536473cervical spinerehabilitationdigital healthmachine learningwearable electronics |
| spellingShingle | Le Huang Keum San Chun Lian Yu Jong Yoon Lee Alan Soetikno Hope Chen Hyoyoung Jeong Joshua Barrett Knute Martell Youn Kang Alpesh A Patel Shuai Xu A Novel Method for Tracking Neck Motions Using a Skin-Conformable Wireless Accelerometer: A Pilot Study Digital Biomarkers cervical spine rehabilitation digital health machine learning wearable electronics |
| title | A Novel Method for Tracking Neck Motions Using a Skin-Conformable Wireless Accelerometer: A Pilot Study |
| title_full | A Novel Method for Tracking Neck Motions Using a Skin-Conformable Wireless Accelerometer: A Pilot Study |
| title_fullStr | A Novel Method for Tracking Neck Motions Using a Skin-Conformable Wireless Accelerometer: A Pilot Study |
| title_full_unstemmed | A Novel Method for Tracking Neck Motions Using a Skin-Conformable Wireless Accelerometer: A Pilot Study |
| title_short | A Novel Method for Tracking Neck Motions Using a Skin-Conformable Wireless Accelerometer: A Pilot Study |
| title_sort | novel method for tracking neck motions using a skin conformable wireless accelerometer a pilot study |
| topic | cervical spine rehabilitation digital health machine learning wearable electronics |
| url | https://beta.karger.com/Article/FullText/536473 |
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