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.

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 m/s to 4.8 m/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.

The purpose of this study was to validate a commercially available 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 six 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 six 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 degrees, respectively. The IMU shoulder elevation measure was accurate to within 6.8±2.7 degrees and the elbow flexion measure was accurate to within 8.2±2.8 degrees. 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 progressive reduction in muscle fiber conduction velocity is typically observed during fatiguing muscle contraction. Although the exact causes of the conduction velocity decrease have not yet been fully established, increasing evidence suggests that changes in extracellular potassium concentration may be largely responsible. In this study, a mathematical model was developed to examine the effect of extracellular potassium concentration on the muscle fiber action potential and conduction velocity. The model was used to simulate changes in extracellular potassium concentration at a range of temperatures and extracellular potassium accumulation during repetitive stimulation of the muscle fiber at 37 degrees C. The action potential broadened, and its amplitude and conduction velocity decreased as extracellular potassium concentration increased. The potassium-induced changes in action potential shape and conduction velocity were eliminated when the inward rectifier channels were removed from the model. The results support the hypothesis that accumulation of extracellular potassium ions may be a major contributor to the reduction in muscle fiber conduction velocity and loss of membrane excitability during fatiguing contractions. They additionally suggest that inward rectifier currents play a critical role in potassium-induced membrane depolarization, leading to increased sodium inactivation and resulting in the observed reduction in conduction velocity and membrane excitability.

Repeated durations of dynamic activity with high ground reaction forces (GRFs) and loading rates (LRs) can be beneficial to bone health. To fully characterize dynamic activity in relation to bone health, field-based measurements of gait kinetics are desirable to assess free-living lower-extremity loading. The study aims were to determine correlations of peak vertical GRF and peak vertical LR with ankle peak vertical accelerations, and of peak resultant GRF and peak resultant LR with ankle peak resultant accelerations and to compare them to correlations with tibia, thigh, and waist accelerations. GRF data were collected as ten healthy subjects (26 (19–34) years) performed 8–10 walking trials at velocities ranging from 0.19–3.05 m/s, wearing ankle, tibia, thigh, and waist accelerometers. While peak vertical accelerations of all locations were positively correlated with peak vertical GRF and LR (r2>0.53, P<0.001), ankle peak vertical accelerations were the most correlated (r2>0.75, P<0.001). All peak resultant accelerations were positively correlated with peak resultant GRF and LR (r2>0.57, P<0.001) with waist peak resultant acceleration being the most correlated (r2>0.70, P<0.001). The results suggest that ankle or waist accelerometers give the most accurate peak GRF and LR estimates and could be useful tools in relating physical activity to bone health.