可靠的設備功能對於醫療診斷步態分析必不可少。預測和健康管理策

略可通過傳感器監控設備的剩餘使用壽命及識別故障設備,以最大程

度地減少潛在的致命誤診。剩餘使用壽命的概念在航空航天工程,製

造工程和電氣工程領域得到了很好的研究。然而,其在生物力學中的

應用仍未得到開發。這項研究的目的是通過使用基於機器學習的特徵

重要性機制,通過對各種可穿戴式步態傳感器(IMU、壓力傳感器)

進行融合以得到有指標性的傳感器數據,來構建生物力學傳感器系統,

進而增強設備的剩餘使用壽命的數據表示能力。並且,使用 LSTM 的模

型架構將該數據用於預測剩餘使用壽命。實驗是根據從候選對象獲得

的數據進行的,結果顯示,與基於壓力傳感器的模型架構相比,RMSE

和加權分數分別提高了 25.68%和 49.62%

Equipment reliability is crucial for medical diagnostic gait analysis. Prognostics and
health management strategies may be employed, by monitoring the remaining useful life
of an equipment through sensors, and identifying faulty equipment to minimise potential
fatal misdiagnosis. The concept of remaining useful life is well studied in the field of
aerospace engineering, manufacturing engineering, and electrical engineering; however,
its application for biomechanics remains underexplored. The purpose of this study is to
construct a biomechanical sensor system, by performing fusion on varied wearable gait

sensors (IMU, pressure sensor) to leverage unique sensor data, using a machine learning-
based feature importance mechanism, whereby an enhanced data representation of the

equipment’s remaining useful life degradation is obtained. Then, this data is utilized to

predict the remaining useful life using an LSTM architecture. The experiment is con-
ducted on data obtained from human candidates, and results indicate RMSE and score

improvement of 25.68% and 49.62% respectively, when compared to a pressure sensor
based architecture.

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