TY - GEN
T1 - Knee Osteoarthritis Classification System Examination on Wearable Daily-Use IMU Layout
AU - Xia, Chengshuo
AU - Maruyama, Tsubasa
AU - Toda, Haruki
AU - Tada, Mitsunori
AU - Fujita, Koji
AU - Sugiura, Yuta
N1 - Funding Information:
This work was supported by JST PRESTO Grant Number JP-MJPR2134.
Publisher Copyright:
© 2022 ACM.
PY - 2022/9/11
Y1 - 2022/9/11
N2 - The diagnostic approach for knee osteoarthritis that draws on kinematic characteristics provides a solution other than imaging medicine. However, the gait-based kinematic analysis still requires a motion capture suit as a prerequisite to ensure a reliable calculation, which limits the daily screening of the end user. To further reduce the cost, in this paper we investigated a wearable inertial measurement unit (IMU)-based knee osteoarthritis classification system based on daily-use wearing IMU layout and machine learning approaches. The acceleration and angular velocity signal output from the IMU were used as the input data; the different features from the time and frequency domains were examined with different handcrafted feature classifiers, as well as the deep learning method. From the results, using three IMUs could reach a 0.82 area under the curve value, with a sensitivity of 86% and a specificity of 78%. The results showed that using daily IMU devices to establish a diagnostic system with an on-body sensor layout is feasible.
AB - The diagnostic approach for knee osteoarthritis that draws on kinematic characteristics provides a solution other than imaging medicine. However, the gait-based kinematic analysis still requires a motion capture suit as a prerequisite to ensure a reliable calculation, which limits the daily screening of the end user. To further reduce the cost, in this paper we investigated a wearable inertial measurement unit (IMU)-based knee osteoarthritis classification system based on daily-use wearing IMU layout and machine learning approaches. The acceleration and angular velocity signal output from the IMU were used as the input data; the different features from the time and frequency domains were examined with different handcrafted feature classifiers, as well as the deep learning method. From the results, using three IMUs could reach a 0.82 area under the curve value, with a sensitivity of 86% and a specificity of 78%. The results showed that using daily IMU devices to establish a diagnostic system with an on-body sensor layout is feasible.
KW - Inertial Measurement Unit
KW - Knee Osteoarthritis
KW - Layout
KW - Screening
UR - http://www.scopus.com/inward/record.url?scp=85145875383&partnerID=8YFLogxK
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U2 - 10.1145/3544794.3558459
DO - 10.1145/3544794.3558459
M3 - Conference contribution
AN - SCOPUS:85145875383
T3 - Proceedings - International Symposium on Wearable Computers, ISWC
SP - 74
EP - 78
BT - ISWC 2022 - Proceedings of the 2022 ACM International Symposium on Wearable Computers
PB - Association for Computing Machinery
T2 - 2022 ACM International Symposium on Wearable Computers, ISWC 2022
Y2 - 11 September 2022 through 15 September 2022
ER -