Feasibility Study of Inertial Sensor-based Joint Moment Estimation Method During Human Movements - A Test of Multi-link Modeling of the Trunk Segment

Abstract

The conventional method of estimating joint moments needs kinematic data measured with a 3D optical motion measurement system and ground reaction forces measured with force plate. However, the conventional method is limited generally to laboratory use because of the required measurement systems. Therefore, we proposed a convenient method to estimate joint moments from measurements only with inertial sensors for application to clinical evaluation of motor function of paralyzed and elderly subjects. In this paper, multi-link modeling of the trunk was examined for reliable estimation of joint moments only from measured data with inertial sensors attached on the body. Body segment parameters (segment length and mass, center of mass location and moments of inertia) were calculated from anthropometric data. Experimental test with 3 healthy subjects showed that segmented trunk model estimated joint moments better than a rigid trunk model for squat and sit-to-stand movements. The estimation results were not different largely between the 5-link model that modeled the trunk by 3 segments and the 4-link model that modeled the trunk by 2 segments. However, trunk modeling for 4-link model was suggested to be appropriate when the upper and the middle trunk segments of the 5-link model were modeled as one segment.

Inertial Sensors for Human Motion Analysis: A Comprehensive Review

“…In the estimations of kinematic parameters, the turn rate integration results in an accumulated error from the gyroscope bias. For that reason, only 4.8% of studies use this sensor alone [11], [12], [13], [14], [15], [16], [17].…”

“…3) Other Algorithms: Over the years of research on motion analysis with inertial sensors, proposals have been based on various algorithms other than sensory fusion filters and data science methods. These proposals cover the integration of the gyroscope data to estimate the joint orientation [12], [15], [16], [17], [41] to its combination with the direct use of the data from accelerometers to estimate the orientation of joints [80], [104], [105] and segments [59], [60], [63], [81], and to estimate the orientation and location of segments [110], [112]. The measurements from gyroscopes and accelerometers are also used directly to obtain the orientation and location of joints [35] and segments [46], and to estimate the orientation of both joints and segments [48], [51], [113].…”

Simultaneous exercise recognition and evaluation in prescribed routines: Approach to virtual coaches

“…In the estimations of kinematic parameters, the turn rate integration results in an accumulated error from the gyroscope bias. For that reason, only 4.8 % of studies use this sensor alone [11]- [17].…”

“…Open-Sim [171], as in [86], [103], [148], by applying kinematic relationships from the stereophotogrammetric measurements, as in [19], [53], [56], [68], [69], [91], [103], or with data augmentation techniques [68] 3) Other algorithms: Over the years of research on motion analysis with inertial sensors, proposals have been based on various algorithms other than sensory fusion filters and data science methods. These proposals cover from the integration of the gyroscope data to estimate the joint orientation [12], [15]- [17], [41], to its combination with the direct use of the data from accelerometers to estimate the orientation of joints [80], [104], [105] and segments [59], [60], [63], [81], and to estimate the orientation and location of segments [110], [112]. The measurements from gyroscopes and accelerometers are also used directly to obtain the orientation and location of joints [35] and segments [46], and to estimate the orientation of both joints and segments [48], [51], [113].…”

“…Another common approach for the error reduction in most of biomechanical models is to take into account the anatomic parameters, such as the joints location with respect to the sensors or the segments length [8], [17], [19], [20], [23]- [26], [35], [37], [38], [46], [46], [64], [73], [76]- [79], [83], [87]- [89], [91]- [96], [98], [99], [105]- [108], [119], [129], [132], [137], [138], [143], [150], [151], [154]. Fig.…”