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Knee Osteoarthritis Classification System Examination on Wearable Daily-Use IMU Layout

Knee Osteoarthritis Classification System Examination on Wearable Daily-Use IMU Layout

Knee Osteoarthritis Classification System Examination on Wearable Daily-Use IMU Layout

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Knee Osteoarthritis Classification System Examination
on Wearable Daily-Use IMU Layout
ACM ISWC 2022
Chengshuo Xia1, Tsubasa Maruyama2, Haruki Toda2, Mitsunori Tada2, Koji Fujita3, Yuta Sugiura1
1: Keio University, Japan
2: National Institute of Advanced Industrial Science and Technology (AIST), Japan
3: Tokyo Medical and Dental University, Japan
2
Background: Knee Osteoarthritis (OA)
• Over 60 years old,
about 10% of males and 18% of females [1].
[1] https://en.wikipedia.org/wiki/Osteoarthritis]
[2] https://www.arthritis-health.com/treatment/injections/hyaluronic-acid-injections-knee-osteoarthritis
[3] https://springloadedtechnology.com/guide-to-severe-knee-osteoarthritis/
Pain with knee [2] Analysis [3]
3
Requirement of daily-life screening for Knee OA
A daily-life screening system for knee OA:
 Helpful to indicate the risk of knee OA disease.
 Can be accessible and used easily.
 Benefit to enable the patient to prevent the severity.
4
Possible solution: IMU
IMU
senssor
Motion kinematic data Knee OA
Future of IMU:
• Cheap and tiny
• Can be fused in multiple daily accessaries
• Wear much
• Have more IMU information from more body limbs
• Can be used in many applications:
• Human activity recognition
• Entertainment
• Sports training
5
Our goal
• Goal
• Screening Knee OA by IMUs embedded into daily objects
• Contributions
• Find the best location of IMUs combination embedded into daily objects.
6
Related Work: Knee OA diagonsis – Medical image
Normal knee Osteoarthritis knee
Magnetic Resonance Imaging (MRI)-based classification [1]
[1] En, Chuah Zhi, and Tan Tian Swee. "Computer-aided knee osteoarthritis classification system using artificial neural network (ANN)." Journal of Medical Imaging and Health Informatics 3.4 (2013): 561-565.
[2] Pingjun Chen, Linlin Gao, Xiaoshuang Shi, Kyle Allen, and Lin Yang. 2019. Fully automatic knee osteoarthritis severity grading using deep neural networks with a novel ordinal loss. Computerized Medical Imaging and Graphics 75
(2019), 84–92.
X-Ray images[2]
• Medical image-based approach: expensive device

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Knee Osteoarthritis Classification System Examination on Wearable Daily-Use IMU Layout

  • 1. Knee Osteoarthritis Classification System Examination on Wearable Daily-Use IMU Layout ACM ISWC 2022 Chengshuo Xia1, Tsubasa Maruyama2, Haruki Toda2, Mitsunori Tada2, Koji Fujita3, Yuta Sugiura1 1: Keio University, Japan 2: National Institute of Advanced Industrial Science and Technology (AIST), Japan 3: Tokyo Medical and Dental University, Japan
  • 2. 2 Background: Knee Osteoarthritis (OA) • Over 60 years old, about 10% of males and 18% of females [1]. [1] https://en.wikipedia.org/wiki/Osteoarthritis] [2] https://www.arthritis-health.com/treatment/injections/hyaluronic-acid-injections-knee-osteoarthritis [3] https://springloadedtechnology.com/guide-to-severe-knee-osteoarthritis/ Pain with knee [2] Analysis [3]
  • 3. 3 Requirement of daily-life screening for Knee OA A daily-life screening system for knee OA:  Helpful to indicate the risk of knee OA disease.  Can be accessible and used easily.  Benefit to enable the patient to prevent the severity.
  • 4. 4 Possible solution: IMU IMU senssor Motion kinematic data Knee OA Future of IMU: • Cheap and tiny • Can be fused in multiple daily accessaries • Wear much • Have more IMU information from more body limbs • Can be used in many applications: • Human activity recognition • Entertainment • Sports training
  • 5. 5 Our goal • Goal • Screening Knee OA by IMUs embedded into daily objects • Contributions • Find the best location of IMUs combination embedded into daily objects.
  • 6. 6 Related Work: Knee OA diagonsis – Medical image Normal knee Osteoarthritis knee Magnetic Resonance Imaging (MRI)-based classification [1] [1] En, Chuah Zhi, and Tan Tian Swee. "Computer-aided knee osteoarthritis classification system using artificial neural network (ANN)." Journal of Medical Imaging and Health Informatics 3.4 (2013): 561-565. [2] Pingjun Chen, Linlin Gao, Xiaoshuang Shi, Kyle Allen, and Lin Yang. 2019. Fully automatic knee osteoarthritis severity grading using deep neural networks with a novel ordinal loss. Computerized Medical Imaging and Graphics 75 (2019), 84–92. X-Ray images[2] • Medical image-based approach: expensive device
  • 7. 7 Related Work: Knee OA diagnosis – other analysis [1] Kotti, Margarita, et al. "The complexity of human walking: a knee osteoarthritis study." PloS one 9.9 (2014): e107325. [2] Lim, Jihye, Jungyoon Kim, and Songhee Cheon. "A deep neural network-based method for early detection of osteoarthritis using statistical data." International journal of environmental research and public health 16.7 (2019): 1281. Ground reaction force (GRF) [1] Statistical Data [2] Demographic Personal characteristics • Other kinematic methods: laboratory-based
  • 8. 8 Related Work: IMU-based motion assistance system Parkinson classification [1] Knee OA-related [2] [1] Carlotta Caramia, Diego Torricelli, Maurizio Schmid, Adriana Munoz-Gonzalez, Jose Gonzalez-Vargas, Francisco Grandas, and Jose L Pons. 2018. IMU-based classification of Parkinson’s disease from gait: A sensitivity analysis on sensor location and feature selection. IEEE journal of biomedical and health informatics 22, 6 (2018), 1765–1774. [2] Hafer, Jocelyn F., et al. "IMU-derived kinematics detect gait differences with age or knee osteoarthritis but differ from marker-derived inverse kinematics." medRxiv (2022). • Using the IMU raw data to design the Knee OA screening system
  • 9. 9 Data collection Knee OA patient Healthy people 36 (KL >2) 14 average age:73.4; 9 male and 27 female average age: 69.4; 2 male and 12 female The experimental protocol was approved by the local institutional review board (M2018-123). • 13 placement • Xsens MTw. • Walk on a 12m-length straight walkway • 60 Hz sampling  120 Hz up-sample. • 55-s data segmentation. • Data augmentation method used to balance the dataset [1] • Training data: Patient dataset (KL > 2): 6 (axis) * 55 (time length) * 120 (Hz) * 36 (people) and Healthy dataset: 6 (axis) * 55 (time length) * 120 (Hz) * 14 (people) * 2 (augmentation).
  • 10. 10 Classification system Accleration & Angular velocity Handcrafted- features CNN model Models Principle Component Analysis
  • 11. 11 Detail of Classification Building • Test classifiers: SVM, Decision Tree, Random Forest, Majority Voting Baseline CNN classifier. • Method: Leave-one-subject-out cross validation(LOOCV) • Handcrafted features: • Deep learning model: convert the time-series data into grayscale image Features Description Time-domain Mean, variance, standard variance, 75th percentile, inter-percentile Frequency-domain Mean value of power spectrum Median value of power spectrum, Mean frequency of power spectrum, Median frequency of power spectrum, Shannon entropy
  • 12. 12 Method of fining best sensor location • Test the optimal sensor layout: Iterative approach: • One sensor: 13 • Two sensors: 𝐶13 2 • Three sensors: 𝐶13 3 • Metrics: • AUC (Area Under the ROC Curve) • Specificity (true negative rate) • Sensitivity (true positive rate) Select sensor placement (s) Build the classification system Obtain the test result e.g., Head + Foot AUC value Binary classification
  • 13. 13 Result of one sensor used Classifier Best sensor position AUC Sensitivity Specificity Random Forest Left foot 0.53 72% 36% SVM Left foot 0.61 72% 50% Decision Tree Right lower arm 0.65 66% 64% Majority voting Right lower leg 0.62 50% 50% CNN Pelvis 0.65 72% 57% • Low AUC • Low performance
  • 14. 14 Result of two-sensor used Classifier Best sensor position AUC Sensitivity Specificity Random Forest Pelvis + Right lower arm 0.69 75% 64% SVM Right upper arm + Left foot 0.73 75% 71% Decision Tree Right upper arm + Left lower arm 0.71 71% 71% Majority voting Pelvis + Neck 0.71 71% 72% CNN Right foot + Neck 0.68 85% 50% • With more sensors, increased data dimension • Improved performance
  • 15. 16 Result of Three-sensor used Classifier Best sensor position AUC Sensitivity Specificity Random Forest Pelvis + Right upper leg + Left foot 0.67 78% 57% SVM Pelvis + Left upper arm + Right upper leg 0.74 78% 71% Decision Tree Right lower arm + Left lower arm + Right foot 0.81 83% 78% Majority voting Right lower arm + Left lower arm + Right foot 0.82 86% 78% CNN Pelvis + Left lower leg + Left foot 0.71 93% 50%
  • 16. 17 4. Experiment and Result Three IMUs layout • End limbs • Upper body + lower body
  • 17. 18 5. Discussion More sensors-used layout Features for machine learning Extended dataset Intensive data Daily data accumulation
  • 18. 19 Summary Background Build a daily-use Knee OA screening system Related Work Medical image-based/kinematic-based diagnosis Proposed Scheme IMUs used for whole body Details Method Machine learning-based screening system Experiment Optimization sensor placement examination Result 2 & 3 IMUs layout Limitation Limited sensor number/dataset/algorithm
  • 19. Thank you very much!