Inertial measurement systems for segments and joints kinematics assessment: towards an understanding of the variations in sensors accuracy

Abstract

BackgroundJoints kinematics assessment based on inertial measurement systems, which include attitude and heading reference system (AHRS), are quickly gaining in popularity for research and clinical applications. The variety of the tasks and contexts they are used in require a deep understanding of the AHRS accuracy for optimal data interpretation. However, published accuracy studies on AHRS are mostly limited to a single task measured on a limited number of segments and participants. This study assessed AHRS sensors kinematics accuracy at multiple segments and joints through a variety of tasks not only to characterize the system’s accuracy in these specific conditions, but also to extrapolate the accuracy results to a broader range of conditions using the characteristics of the movements (i.e. velocity and type of motion). Twenty asymptomatic adults ( = 49.9) performed multiple 5 m timed up and go. Participants’ head, upper trunk, pelvis, thigh, shank and foot were simultaneously tracked using AHRS and an optical motion capture system (gold standard). Each trial was segmented into basic tasks (sit-to-stand, walk, turn).ResultsAt segment level, results revealed a mean root-mean-squared-difference varying between 1.1° and 5.5° according to the segment tracked and the task performed, with a good to excellent agreement between the systems. Relative sensor kinematics accuracy (i.e. joint) varied between 1.6° and 13.6° over the same tasks. On a global scheme, analysis of the effect of velocity on sensor kinematics accuracy showed that AHRS are better adapted to motions performed between 50°/s and 75°/s (roughly thigh and shank while walking).ConclusionResults confirmed that pairing of modules to obtain joint kinematics affects the accuracy compared to segment kinematics. Overall, AHRS are a suitable solution for clinical evaluation of biomechanics under the multi-segment tasks performed although the variation in accuracy should be taken into consideration when judging the clinical meaningfulness of the observed changes.

Lower Limb Kinematics Using Inertial Sensors during Locomotion: Accuracy and Reproducibility of Joint Angle Calculations with Different Sensor-to-Segment Calibrations

“…As the magnetometer was not used in the AHRS algorithm, the DRIFT was controlled to be acceptable (mean of 2.3 • ) for such short experiments. The drift was slightly higher in the more distal joints, probably due to the higher speed of the movements [26,27]. The drift was slightly lower in the sagittal plane, probably because the drift in this plane was better compensated by the sensor fusion algorithm.…”

Use of Wearable Sensor Technology in Gait, Balance, and Range of Motion Analysis

“…A common way is to place the devices over the bones and not over the muscles to reduce soft tissue artifact [58]. Additionally, using bundles and straps and having care in positioning the bundles can minimize soft tissue artifact [57].…”

“…Additionally, STA also occurs in optoelectronic systems when placing markers to the subject's segments. This needs to be taken into consideration since optoelectronic systems are often used to validate wearable sensor measurements [22,57,58,61,62]. Few ways to minimize this issue is by having the marker within the field of view of at least two cameras, markers attached to the same segment should be distributed to minimize position error propagation to bone orientation, and the movement between underlying bones and the markers should be minimal [93][94][95][96][97].…”

Kinect and wearable inertial sensors for motor rehabilitation programs at home: state of the art and an experimental comparison

“…Algorithms derived from navigation applications are adapted to infer the orientation of the body segment of interest and include the Kalman Filter (KF), its extended and unscented variations, and also several implementations of Complementary Filters (CF) [81][82][83][84]. Moreover, IMU sensor data can be exploited to analyze various features of human motion and dedicated algorithms have been developed for tasks such as activity recognition [85], exercise recognition and evaluation [86,87], gait analysis [63,88,89] and jump analysis [90,91].…”

“…Most of the previous validation studies used commercial solutions to obtain the orientation of the wearable sensors, which are used to estimate the orientation of the body segment they are attached to. Their outputs are then combined to form a partial or complete body pose estimation, based on the number of sensors in use [9,[59][60][61][62][63][64]. While there are several proposals for algorithms for the estimation of orientation from inertial sensors' data, the present work analyzes the most used ones, to provide a comparative analysis targeting robust and well-established solutions.…”