The purpose of this study was to validate a commercially available inertial measurement unit (IMU) system against a standard lab-based motion capture system for the measurement of shoulder elevation, elbow flexion, trunk flexion/extension, and neck flexion/extension kinematics. The validation analyses were applied to 6 surgical faculty members performing a standard, simulated surgical training task that mimics minimally invasive surgery. Three-dimensional joint kinematics were simultaneously recorded by an optical motion capture system and an IMU system with 6 sensors placed on the head, chest, and bilateral upper and lower arms. The sensor-to-segment axes alignment was accomplished manually. The IMU neck and trunk IMU flexion/extension angles were accurate to within 2.9 ± 0.9 degrees and 1.6 ± 1.1°, respectively. The IMU shoulder elevation measure was accurate to within 6.8 ± 2.7° and the elbow flexion measure was accurate to within 8.2 ± 2.8°. In the Bland-Altman analyses, there were no significant systematic errors present; however, there was a significant inversely proportional error across all joints. As the gold standard measurement increased, the IMU underestimated the magnitude of the joint angle. This study reports acceptable accuracy of a commercially available IMU system; however, results should be interpreted as protocol specific.

A subject-specific step counting method with a high accuracy level at all walking speeds is needed to assess the functional level of impaired patients. The study aim was to validate step counts and cadence calculations from acceleration data by comparison to video data during dynamic activity. Custom-built activity monitors, each containing one tri-axial accelerometer, were placed on the ankles, thigh, and waist of 11 healthy adults. ICC values were greater than 0.98 for video inter-rater reliability of all step counts. The activity monitoring system (AMS) algorithm demonstrated a median (interquartile range; IQR) agreement of 92% (8%) with visual observations during walking/jogging trials at gait velocities ranging from 0.1 to 4.8m/s, while FitBits (ankle and waist), and a Nike Fuelband (wrist) demonstrated agreements of 92% (36%), 93% (22%), and 33% (35%), respectively. The algorithm results demonstrated high median (IQR) step detection sensitivity (95% (2%)), positive predictive value (PPV) (99% (1%)), and agreement (97% (3%)) during a laboratory-based simulated free-living protocol. The algorithm also showed high median (IQR) sensitivity, PPV, and agreement identifying walking steps (91% (5%), 98% (4%), and 96% (5%)), jogging steps (97% (6%), 100% (1%), and 95% (6%)), and less than 3% mean error in cadence calculations.

A robust method for identifying movement in the free-living environment is needed to objectively measure physical activity. The purpose of this study was to validate the identification of postural orientation and movement from acceleration data against visual inspection from video recordings. Using tri-axial accelerometers placed on the waist and thigh, static orientations of standing, sitting, and lying down, as well as dynamic movements of walking, jogging and transitions between postures were identified. Additionally, subjects walked and jogged at self-selected slow, comfortable, and fast speeds. Identification of tasks was performed using a combination of the signal magnitude area, continuous wavelet transforms and accelerometer orientations. Twelve healthy adults were studied in the laboratory, with two investigators identifying tasks during each second of video observation. The intraclass correlation coefficients for inter-rater reliability were greater than 0.95 for all activities except for transitions. Results demonstrated high validity, with sensitivity and positive predictive values of greater than 85% for sitting and lying, with walking and jogging identified at greater than 90%. The greatest disagreement in identification accuracy between the algorithm and video occurred when subjects were asked to fidget while standing or sitting. During variable speed tasks, gait was correctly identified for speeds between 0.1m/s and 4.8m/s. This study included a range of walking speeds and natural movements such as fidgeting during static postures, demonstrating that accelerometer data can be used to identify orientation and movement among the general population.

Shoulder pain and pathology are common in manual wheelchair (MWC) users with paraplegia, and the biomechanical mechanism of injury is largely unknown. Establishing patterns of MRI characteristics in MWC users would help advance understanding of the mechanical etiology of rotator cuff disease, thus improving the logic for prescribed interventions. The purpose of this study was to report detailed shoulder MRI findings in a sample of 10 MWC users with anterolateral shoulder pain. The imaging assessments were performed using our standardized MRI Assessment of the Shoulder (MAS) guide. The tendon most commonly torn was the supraspinatus at the insertion site in the anterior portion in either the intrasubstance or articular region. Additionally, widespread tendinopathy, CA ligament thickening, subacromial bursitis, labral tears, and AC joint degenerative arthrosis and edema were common. Further reporting of detailed shoulder imaging findings is needed to confirm patterns of tears in MWC users regarding probable tendon tear zone, region, and portion. This investigation was a small sample observational study and did not yield data that can define patterns of pathology. However, synthesis of detailed findings from multiple studies could define patterns of pathological MRI findings allowing for associations of imaging findings to risk factors including specific activities.