Accuracy Improvement on the Measurement of Human-Joint Angles

Abstract

A measurement technique that decreases the root mean square error (RMSE) of measurements of human-joint angles using a personal wireless sensor network is reported. Its operation is based on virtual rotations of wireless sensors worn by the user, and it focuses on the arm, whose position is measured on 5 degree of freedom (DOF). The wireless sensors use inertial magnetic units that measure the alignment of the arm with the earth's gravity and magnetic fields. Due to the biomechanical properties of human tissue (e.g., skin's elasticity), the sensors' orientation is shifted, and this shift affects the accuracy of measurements. In the proposed technique, the change of orientation is first modeled from linear regressions of data collected from 15 participants at different arm positions. Then, out of eight body indices measured with dual-energy X-ray absorptiometry, the percentage of body fat is found to have the greatest correlation with the rate of change in sensors' orientation. This finding enables us to estimate the change in sensors' orientation from the user's body fat percentage. Finally, an algorithm virtually rotates the sensors using quaternion theory with the objective of reducing the error. The proposed technique is validated with experiments on five different participants. In the DOF, whose error decreased the most, the RMSE decreased from 2.20(°) to 0.87(°). This is an improvement of 60%, and in the DOF whose error decreased the least, the RMSE decreased from 1.64(°) to 1.37(°). This is an improvement of 16%. On an average, the RMSE improved by 44%.

Motion-Based Routing and Transmission Power Control in Wireless Body Area Networks

“…The algorithm in this paper also performs this type of TPC with the difference that TPC is on the basis of body postures, not received power, as initially developed in our preliminary work [13] and in [11], [15], [16] for star topologies only. 1 The related work in [14], [31], [32], [33], [34] also uses body posture for star topologies only with the difference that 1. The performance of the algorithms in [11], [13], [15], [16] is used in Section VII to benchmark our dynamic-routing-and-TPC algorithm.…”

“…The network topology can be star or multi-hop. For example, in our preliminary work [1], [2], a WBAN was implemented for the measurement of human-joint angles so that physical therapists can track therapy exercises with high accuracy from patients. In this WBAN, there were wireles sensors placed on the user's arms to track angles at the elbow and shoulder joints, and these sensors communicated with a base station connected to a desktop computer forming a star topology.…”

Validity and Reliability of the WIMUTM Inertial Device for the Assessment of Joint Angulations

“…These devices are based on inertial/magnetic systems that are used as a wearable device, allowing motion measurement. These devices data could be sent to a computer in real time to evaluate movement and give immediate feedback [5]. The use of wireless inertial measurement devices is increasing in research.…”

IMU-based sensor-to-segment multiple calibration for upper limb joint angle measurement—a proof of concept

“…What is more, the sensor misalignment is changing during the movement due to soft tissue artifact (STA) [7,[15][16][17][18][19]. STA is defined in the literature as the skin motion relative to the underlying bone [7,20] and is regarded as a major source of error that disrupts the estimation of joint angles when non-invasive measurement systems are used [21]. In OMCS measurements, STA results in markers cluster shape deformation as well as, predominantly, maker-cluster rotation and translation with respect to the underlying bone.…”

“…This is a classical assumption made in most studies on sensor-to-segment calibration. In the case of joint angle measurement by IMUs, two adjustments based on the mean misalignment were proposed by Meng et al [20] with multiple static postures related to five active movements such that only one angle was varied per movement. It is important to keep in mind that they combined both IMU and OMCS to get a statistics of the alignment difference.…”