Sprint running performances have improved with the use of advanced footwear technology, but the mechanisms responsible are unknown. Here, we tested two hypothesized mechanisms: 1) faster maximal speeds resulting from greater ground force application, and 2) associated contact time decreases, and step length increases that could enable faster late-race velocities. Ten track and five soccer athletes (N=15) completed randomized, linear 130-meter sprint trials in conventional and prototype footwear. The prototypes were highly compliant and resilient in cushioning and stiff in forefoot bending. Instantaneous velocities were recorded with a radar device; contact and aerial times were acquired from ankle-mounted accelerometers throughout; split and final times (60, 100, 130 m) were recorded with dual-beam laser timing gates. Contact times were also determined from 960 Hz video acquired from 10-meter zones between 45 and 80 meters. Performance times for the 130-meter trials were 1.7% shorter in the prototype vs. the conventional spike. Footwear condition differences in velocity were distance-dependent, increasing from a minimum of 0.1% in the first 10 meters, to a maximum of 3.9% at the 130-meter finish line. Video capture zone contact periods were shorter (-1.9%), and estimated stance-averaged ground forces were greater (+1.6%) in the prototype vs. conventional footwear. Progressive gait and velocity effects resulted in relatively large prototype vs. conventional condition differences in the final 30-meter segment for: contact times (-2.5%), aerial times (+6.0%), step lengths (+2.0%), and velocities (+3.0%). We conclude that the prototype footwear enabled gait mechanics that increased both maximal and late-trial sprinting velocities.

Background: Light-intensity physical activity (LPA) constitutes a substantial portion of daily activities; however, it is more challenging to record and measure accurately compared to moderate- to vigorous-intensity physical activity (MVPA). Consequently, the health benefits associated with LPA are frequently overlooked. Our study aimed to investigate the associations of LPA pattern including objectively measured LPA's duration, proportion, and timing with risk of incident cardiovascular diseases (CVDs). Methods: With the UK Biobank, 83,658 participants free of CVD with valid accelerometry data were included between 2013 and 2015. Data on physical activity (PA) were collected using the Axivity AX3 wrist-based triaxial accelerometer worn for 1 week. LPA duration was estimated using machine learning models based on bouts data from wrist-worn accelerometers. The LPA proportion was calculated by dividing the amount of LPA by the sum of LPA and MVPA. LPA timing was determined using hierarchical clustering analysis based on the average duration of LPA within each time window across the 24-h cycle. CVDs of interest including coronary heart disease (CHD), stroke, heart failure (HF), and atrial fibrillation (AF) were identified through the linkage to hospital admission records and death registers. Results: Over a median follow-up duration of 7.98 (interquartile range: 7.43 to 8.47) years, a total of 2,239 CVDs, including 1,014 CHDs, 352 strokes, 796 AFs, and 194 HFs, were recorded. An L-shaped relationship was found between LPA duration and the risk of HF. Compared to individuals in the lowest quartile, those in the second (hazard ratio [HR], 0.64; 95% confidence interval [CI]: 0.44, 0.93), third (HR, 0.56; 95% CI: 0.37, 0.84), and highest quartile (HR, 0.65; 95% CI: 0.43, 0.99) of daily LPA were associated with lower risks of HF. Higher LPA in the morning, midday, and evening was associated with a higher risk of HF, whereas higher LPA in the night showed an adverse impact on HF risk. These findings were not observed for CHD, stroke, and AF. Conclusions: Our findings highlighted that maintaining sufficient duration of LPA might be beneficial for HF as a compensatory PA strategy, but it is also essential to regulate the proportion and timing of LPA.

The Velocity-Aided Navigation (VAN) method for determining latitude, longitude, and altitude is proposed when global navigation satellite system (GNSS) signals are unavailable. Currently, GNSS receivers are the primary navigation systems that meet consumer demand for location accuracy. However, GNSS receivers are not autonomous. Strapdown inertial navigation systems (SINSs), unlike GNSSs, are autonomous. Their operating principle is based on double integration of accelerometer output signals. However, they have a significant drawback: SINS errors increase significantly over time. Two approaches are used to improve accuracy. The first involves using expensive, high-precision gyroscopes and accelerometers. The other involves correcting the SINS by integrating it with navigation systems built on physical principles different from those of the SINS. An alternative method, based on VAN and an inertial measurement unit (IMU), for determining navigation parameters is proposed and does not require double integration of accelerometer output signals. Analytical expressions for the errors of the new method are derived. Calculations showed that the errors of the new method are significantly smaller than those of the autonomous SINS. Experimental testing confirmed the calculation results and demonstrated that the errors of the new method are comparable to those of the SINS integrated with GNSS using a Kalman filter. The proposed alternative VAN method for determining latitude, longitude, and altitude can be used independently, as an alternative to GNSS for integration with the SINS, and can also serve as a backup navigation system.

In accelerometer-based measurement, the inclusion of upper extremity (UE) activity during walking can overestimate real-world functional UE use in people post-stroke. This study introduces a comprehensive, accelerometer-based approach to examining real-world UE use, including the contribution of UE activity during walking, and compares UE use during walking versus ADL-based activities. People with subacute stroke wore bilateral wrist and ankle inertial measurement units (IMU) for one week (24 h/day). Agreement between UE-Total, UE-Continuous Walking and UE-ADL measures were assessed using Bland-Altman analyses. Variables were modeled using linear regression to explore step count, mobility aid use and UE motor impairment as predictors of IMU measures of real-world UE use. Agreement between UE-Total and UE-ADL measures showed minimal bias. Differences between UE-Continuous Walking and UE-ADL varied by mobility aid. UE-Continuous Walking was associated with increased UE activity and relatively greater paretic arm use compared with UE-ADL. Across UE-Continuous Walking and UE-ADL, step count was the strongest predictor of real-world UE use. UE activity during continuous walking exhibits different use patterns as compared to ADL-based activity. Accordingly, separating continuous walking-related UE activity from ADL-based UE use may improve clinical interpretation of IMU measures of real-world UE use in individuals with subacute stroke.

This paper investigates the precision and stochastic error propagation of navigation solutions obtained from a low-cost microelectromechanical system inertial measurement unit (MEMS-IMU) under static conditions. A modern smartphone equipped with an MEMS-IMU was rigidly mounted at a calibrated fixed location to establish a zero-reference scenario, and inertial measurements were collected while the device remained stationary. The dataset was divided into 75 non-overlapping segments, each comprising 30 s of data sampled at 10 Hz, to enable statistically robust analysis. For each segment, velocity and position, which are theoretically zero under static conditions, were computed using strapdown inertial mechanization. A comprehensive statistical framework was then applied to characterize the stochastic behavior of both the raw inertial measurements and the derived navigation states. The methodology first assessed data normality, stationarity using the Augmented Dickey-Fuller (ADF) test, and variance homogeneity using Bartlett's test. Subsequently, ARIMA models were identified and validated using the Ljung-Box (LB) test, while power spectral density (PSD) analysis provided complementary frequency-domain characterization. In addition, a multivariate, non-negative least squares variance component estimation (NNLS-VCE) method was employed to jointly estimate the variance components of multiple navigation state variables. The results demonstrate that the accelerometer and gyroscope measurements along all three axes are well characterized as stationary white-noise processes, with standard deviations in the order of 10-2 m/s2 and 10-4 rad/s, respectively. The estimated velocity random walk (VRW) coefficients are 0.197,0.201,0.160 m/s/h, while the corresponding angular random walk (ARW) coefficients are 0.009,0.012,0.008 rad/h. In contrast, the derived velocity and position estimates exhibit random walk behavior caused by error accumulation in the inertial mechanization process and are best represented by ARIMA(0,1,0) and ARIMA(0,2,0) models, respectively, consistent with the corresponding Allan variance analysis. After 30 s of static navigation, the average standard deviations of the ENU velocity estimates are σv=[0.77,0.44,0.29] m/s, while the corresponding position standard deviations are σp=[1.30,0.69,0.46] m. The proposed framework provides a comprehensive approach for the stochastic modeling, precision assessment, and error characterization of low-cost MEMS-IMU navigation systems.