The Effects of Yoga Exercise on Blood Pressure and Hand Grip Strength in Chronic Stroke Patients: A Pilot Controlled Study.

BACKGROUND: We investigated the beneficial effect of add-on yoga with rehabilitation on blood pressure (BP) and hand grip strength in patients with chronic stroke (more than 90 days). METHODS: The study included patients 30-80 years of age who could stand independently for 1 min. Patients with psychiatric diseases or undergoing other therapies (like acupuncture) were excluded. The yoga group received training (1 h session twice weekly) with standard rehabilitation for 8 weeks. The control group received standard rehabilitation only. There were no differences in age, gender, hand grip strength, or BP between the two groups (16 subjects in each group) at baseline. RESULTS: The systolic BP (p = 0.01) decreased significantly, and the diastolic BP also decreased but not significantly in the yoga group (p = 0.11). For hand grip strength, both the unaffected hand (p = 0.00025) and the affected hand (p = 0.027) improved significantly. The control group showed no significant change in systolic or diastolic BP, nor did the grip strength change in both hands. Gender and age also affected the results of overall rehabilitation in that women benefited more from a decrease in BP, while men and young people (lower than the mean age of 60) benefited from hand grip strength improvement. CONCLUSIONS: Combining yoga with rehabilitation in chronic stroke patients can improve hand grip strength and decrease systolic BP.

RTLIO: Real-Time LiDAR-Inertial Odometry and Mapping for UAVs.

Most UAVs rely on GPS for localization in an outdoor environment. However, in GPS-denied environment, other sources of localization are required for UAVs to conduct feedback control and navigation. LiDAR has been used for indoor localization, but the sampling rate is usually too low for feedback control of UAVs. To compensate this drawback, IMU sensors are usually fused to generate high-frequency odometry, with only few extra computation resources. To achieve this goal, a real-time LiDAR inertial odometer system (RTLIO) is developed in this work to generate high-precision and high-frequency odometry for the feedback control of UAVs in an indoor environment, and this is achieved by solving cost functions that consist of the LiDAR and IMU residuals. Compared to the traditional LIO approach, the initialization process of the developed RTLIO can be achieved, even when the device is stationary. To further reduce the accumulated pose errors, loop closure and pose-graph optimization are also developed in RTLIO. To demonstrate the efficacy of the developed RTLIO, experiments with long-range trajectory are conducted, and the results indicate that the RTLIO can outperform LIO with a smaller drift. Experiments with odometry benchmark dataset (i.e., KITTI) are also conducted to compare the performance with other methods, and the results show that the RTLIO can outperform ALOAM and LOAM in terms of exhibiting a smaller time delay and greater position accuracy.