Observational balance tests (e.g., Berg Balance Scale) are used to evaluate fall-risk. However, they tend to be subjective, and their reliability and sensitivity can be limited. The use of in-lab equipment for objective balance evaluation has not been common in clinical practice, due to the requirement of an equipped lab space. While inertial measurement units (IMUs) enable objective out-of-lab balance assessment, their accuracy has not been validated. This study aims to investigate the accuracy of IMUs against in-lab equipment for characterizing standing balance. Ten non-disabled individuals participated in a two-minute standing test on a force-plate. Four approaches were used for estimating inter-segmental moments and center of pressure (COP) position in a four-segment model: (1) camera-based bottom-up approach; (2) camera-based top-down approach; (3) IMU-based (accelerometer) top-down approach; and (4) IMU-based (accelerometer and gyroscope) top-down approach. Approaches 2 to 4 resulted in high accuracy compared to the reference, Approach 1. The root-mean-square errors in estimating the segments' orientation, ground reaction forces, COP position, and joint moments were smaller than 0.3°, 0.2 N/kg, 1.5 mm, and 0.016N·m/kg, respectively. Since no significant differences were observed between the accuracy of Approaches 3 and 4, only accelerometer recordings are needed and could be recommended for monitoring standing balance.

The Rapid Upper Limb Assessment (RULA) is frequently used to monitor body posture for early risk prevention of work-related musculoskeletal disorders. However, RULA measurements that are based on workers’ self-report or external rater observation suffer from low repeatability. Thus, the objective of this study was to investigate the accuracy and repeatability of an inertial measurement unit (IMU) system for in-field RULA score assessment during manual material handling tasks using 3D Cardan angles and 2D projection angles against reference values obtained by a motion-capture camera system. The experimental results showed that for trunk and neck joint angles, the 2D convention had significantly (p < 0.05) smaller root-mean-square error (RMSE), while for other upper-body angles, the convention with significantly smaller RMSE depended on the angle under analysis. Also, the 3D convention showed a “moderate” agreement with the reference system, while the 2D convention showed a “substantial” agreement for two tasks and a “moderate” agreement for one task. Moreover, the intraclass correlation coefficients ranged from 0.82 to 0.94 for the 3D convention and 0.87 to 0.95 for the 2D convention for repeated trials performed by each participant. Therefore, the wearable IMU system, along with the 2D convention, could be considered as an accurate and repeatable ergonomic risk assessment tool.

This study presents a novel method for the detection and classification of a wide range of physical activities, including standing, sitting, lying, level walking, and walking upstairs and downstairs using a single chest-mounted accelerometer. The trunk inclination angle and variation of the gravitational component of the accelerometer recording were used for detection and classification of postural transitions and walking modalities. In addition, biomechanical features of each transition were used to reject false detections. To validate the accuracy of the presented method, two studies were performed, first in the (1) laboratory environment, where a motion capture system was the reference system (ten healthy subjects), and second (2) in the free-living environment where a handheld camera was the reference system (ten healthy subjects). The first study showed that the proposed method obtained higher accuracy, sensitivity, and specificity in detection of postural transitions and walking modalities compared to other methods in the literature when implemented on the same dataset. The second study obtained (1) the sensitivity and specificity of 100% for detection of sit-to-lie, lie-to-sit, and stand-to-sit, and 100% and 97%, respectively, for detection of sit-to-stand, and (2) the accuracy of 99%, 99%, and 95% for detection of slow, normal, and fast level walking, and 97% and 96% for detection of walking upstairs and downstairs. The proposed method enabled detection and classification of postural transitions and walking modalities with high sensitivity and specificity using only one chest-mounted accelerometer. This approach can be used for convenient and reliable assessment of physical activities in long-term.

Lower extremity kinematic alterations associated with sport-related knee injuries may contribute to an unsuccessful return to sport or early-onset post-traumatic osteoarthritis. Also, without access to sophisticated motion-capture systems, temporospatial monitoring of horizontal hop tests during clinical assessments is limited. By applying an alternative measurement system of two inertial measurement units (IMUs) per limb, we obtained and validated flying/landing times and hop distances of triple single-leg hop (TSLH) test against motion-capture cameras, assessed these temporospatial parameters amongst injured and uninjured groups, and investigated their association with the Knee Injury and Osteoarthritis Outcome Score (KOOS). Using kinematic features of IMU recordings, strap-down integration, and velocity correction techniques, temporospatial parameters were validated for 10 able-bodied participants and compared between 22 youth with sport-related knee injuries and 10 uninjured youth. With median (interquartile range) errors less than 10(16) ms for flying/landing times, and less than 4.4(5.6)% and 2.4(3.0)% of reference values for individual hops and total TSLH progression, differences between hopping biomechanics of study groups were highlighted. For injured participants, second flying time and all hop distances demonstrated moderate to strong correlations with KOOS Symptom and Function in Daily Living scores. Detailed temporospatial monitoring of hop tests is feasible using the proposed IMUs system.