期刊
SENSORS
卷 22, 期 7, 页码 -出版社
MDPI
DOI: 10.3390/s22072507
关键词
inertial motion capture; joint kinetics; wearable system; inverse dynamics; machine learning
资金
- Basic Science Research Program through the National Research Foundation of Korea (NRF) - Ministry of Education [2018R1D1A1B07042791]
Joint kinetics plays a vital role in biomechanics for evaluating the mechanical load and understanding the motor function of joints. Inertial motion capture (IMC) systems have gained attention in recent years due to their wearability and ubiquitous measurement capability. This review explores the methodology and results of studies that estimate joint kinetic variables using IMC systems. The selected studies employ two types of estimation methods: inverse dynamics-based and machine learning-based. Both methods have demonstrated good performance in analyzing joint kinetics during various daily activities.
In biomechanics, joint kinetics has an important role in evaluating the mechanical load of the joint and understanding its motor function. Although an optical motion capture (OMC) system has mainly been used to evaluate joint kinetics in combination with force plates, inertial motion capture (IMC) systems have recently been emerging in joint kinetic analysis due to their wearability and ubiquitous measurement capability. In this regard, numerous studies have been conducted to estimate joint kinetics using IMC-based wearable systems. However, these have not been comprehensively addressed yet. Thus, the aim of this review is to explore the methodology of the current studies on estimating joint kinetic variables by means of an IMC system. From a systematic search of the literature, 48 studies were selected. This paper summarizes the content of the selected literature in terms of the (i) study characteristics, (ii) methodologies, and (iii) study results. The estimation methods of the selected studies are categorized into two types: the inverse dynamics-based method and the machine learning-based method. While these two methods presented different characteristics in estimating the kinetic variables, it was demonstrated in the literature that both methods could be applied with good performance for the kinetic analysis of joints in different daily activities.
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