Test-retest reliability of wireless inertial-sensor derived measurements of knee joint kinematics.

Advances in sensor technology have provided an opportunity to measure gait characteristics using body-worn inertial measurement units (IMUs). Whilst research investigating the validity of IMUs in reporting gait characteristics is extensive, research investigating the reliability of IMUs is limited. This study aimed to investigate the inter-session reliability of wireless IMU derived measures of gait (i.e., knee angle, range of motion) taking multiple test administrators into account. Fifteen healthy volunteers (43 ± 15 years) completed two visits. Within each visit, participants were required to perform two sets of 6 gait trials (6-metre walk tests). IMUs were placed on the participant in 7 locations on the lower limbs and waist. A different test administrator (n = 3) applied the IMUs at each set. At visit 2, this procedure was repeated with the same test administrators as visit 1. Kinematic measures of maximum angle (Knee_Max), minimum angle (Knee_Min), and range of motion (RoM) are reported for the left and right knee. The intraclass correlation coefficients (ICC), standard error of measurement (SEM) and minimum detectable change (MDC) are reported to determine IMU reliability. The results confirmed moderate to good inter-session reliability across all features (0.73-0.87). SEM values ranged from 1.21-3.32° and MDC values ranged from 3.37 - 9.21°. Therefore, IMUs appear to be a reliable method to determine inter-session gait characteristics across multiple test administrators.

An Automatic Foot and Shank IMU Synchronization Algorithm: Proof-of-concept.

When using wearable sensors for measurement and analysis of human performance, it is often necessary to integrate and synchronise data from separate sensor systems. This paper describes a synchronization technique between IMUs attached to the shanks and insoles attached at the feet and aims to solve the need to compute the ankle joint angle, which relies on synchronized sensor data. This will additionally enable concurrent analysis using gait kinematic and kinetic features. A proof-of-concept of the algorithm, which relies on cross-correlation of gyroscope sensor data from the shank and foot, to align the sensor systems is demonstrated. The algorithm output is validated against those signals synchronized using manually annotated heel-strike and toe-off ground-truth signal landmarks, identified in both the shank and feet signals using previously published definitions. Results demonstrate that the developed algorithm is capable of synchronizing both sensor systems, based on IMU data from both healthy participants and participants suffering from knee osteoarthritis, with a mean lag time bias of 25.56ms when compared to the ground truth. A proof-of-concept of technique to synchronise IMUs attached to the shanks and insoles attached at the feet is demonstrated and offers an alternative approach to sensor system synchronisation.