Kinematic analysis of gait in an underwater treadmill using land-based Vicon T 40s motion capture cameras arranged externally

Improving the reliability of underwater gait analysis using wearable pressure and inertial sensors

This work addresses the lack of reliable wearable methods to assess walking gaits in underwater environments by evaluating the lateral hydrodynamic pressure exerted on lower limbs. Sixteen healthy adults were outfitted with waterproof wearable inertial and pressure sensors. Gait analysis was conducted on land in a motion analysis laboratory using an optoelectronic system as reference, and subsequently underwater in a rehabilitation swimming pool. Differences between the normalized land and underwater gaits were evaluated using temporal gait parameters, knee joint angles and the total water pressure on the lower limbs. The proposed method was validated against the optoelectronic system on land; gait events were identified with low bias (0.01s) using Bland-Altman plots for the stride time, and an acceptable error was observed when estimating the knee angle (10.96° RMSE, Bland-Altman bias -2.94°). The kinematic differences between the land and underwater environments were quantified, where it was observed that the temporal parameters increased by more than a factor of two underwater (p<0.001). The subdivision of swing and stance phases remained consistent between land and water trials. A higher variability of the knee angle was observed in water (CV = 60.75%) as compared to land (CV = 31.02%). The intra-subject variability of the hydrodynamic pressure on the foot ([Formula: see text] = 39.65%) was found to be substantially lower than that of the knee angle (CVz = 67.69%). The major finding of this work is that the hydrodynamic pressure on the lower limbs may offer a new and more reliable parameter for underwater motion analysis as it provided a reduced intra-subject variability as compared to conventional gait parameters applied in land-based studies.

Automated Gait Analysis Based on a Marker-Free Pose Estimation Model

Gait analysis is an essential tool for detecting biomechanical irregularities, designing personalized rehabilitation plans, and enhancing athletic performance. Currently, gait assessment depends on either visual observation, which lacks consistency between raters and requires clinical expertise, or instrumented evaluation, which is costly, invasive, time-consuming, and requires specialized equipment and trained personnel. Markerless gait analysis using 2D pose estimation techniques has emerged as a potential solution, but it still requires significant computational resources and human involvement, making it challenging to use. This research proposes an automated method for temporal gait analysis that employs the MediaPipe Pose, a low-computational-resource pose estimation model. The study validated this approach against the Vicon motion capture system to evaluate its reliability. The findings reveal that this approach demonstrates good (ICC_(2,1) > 0.75) to excellent (ICC_(2,1) > 0.90) agreement in all temporal gait parameters except for double support time (right leg switched to left leg) and swing time (right), which only exhibit a moderate (ICC_(2,1) > 0.50) agreement. Additionally, this approach produces temporal gait parameters with low mean absolute error. It will be useful in monitoring changes in gait and evaluating the effectiveness of interventions such as rehabilitation or training programs in the community.

Mechanics Model-Based Motion Design for a Piggyback Nursing-Care Robot

To conduct a comfortable lift for the care-receiver, it takes a lot of time and operations to design the motion trajectory for each care-receiver before transfer tasks. To solve this problem, this paper proposed a method to design a lift trajectory for a piggyback transfer robot. The robot, which can lift and move a person from a wheelchair to a bed or a pedestal pan, has been developed. The trajectory obtained by this method could make the robot conduct a comfort lift for the care-receiver, according to the weight and height of the care-receiver. A human-robot mechanics model and the relationship between the comfortable lift trajectory and the care-receiver’s weight and height were also contributed. According to the test results of 20 subjects, the force parameters used for trajectory design were determined, and the trajectory design method was optimized. The results of three subjects demonstrated that this method could conveniently and quickly provide a robot lift trajectory based on the subject’s weight and height, and this trajectory also achieved a similar lift as the trajectory designed by relying on the opinion of the subject. This method can be used for the design of the reference trajectory in the compliant control of the piggyback robot, which realizes the comfortable lifting of the care-receiver.