The purpose of this study was to compare ankle joint loads (Linear and Angular Impulses) while descending the stairs and ramp. Ten young male subjects participated in this study. Stairs and ramp of identical slope (30 degrees) were custom-made to include force plates in the middle of pathways. Subjects descended the stairs and ramp at a comfortable speed and posture. The stance period was divided into three phases, weight acceptance (WA), single limb stance, and pre-swing. Three-directional impulses and their sum were derived from the reaction forces and moments at the ankle joint. Differences in impulse sums (Both Linear and Angular) between stairs and ramp were significant only in the early (WA) phase, whereas those of stairs were greater than the ramp. All subjects adopted forefoot strike strategy for the stairs and 80% of the subjects adopted rearfoot strike strategy for the ramp. An increase in the GRF and moment arm of the GRF at the ankle joint in case of forefoot strike may have contributed to the increase in the linear and angular impulse in the early phase of stair descent compared to ramp descent. The results are in agreement with the preference of ramp in the elderly.
Previous studies on joint angle estimation have been restricted to slow-speed level walking conditions, even though slope walking and running elicit unique biomechanical characteristics. Measurements were mostly based on an optical motion capture system despite in-the-lab limitation of measurement technique. The contribution of this study is twofold: (i) to propose a joint angle estimation method by applying a state-of-the-art parallel Kalman filter based on an inertial measurement unit (IMU) that can overcome in-the-lab limitation, and (ii) to demonstrate its application to level walking condition as well as slope walking and running conditions to fill a gap in joint kinematics literature. In particular, this study focuses on knee flexion/extension and ankle dorsiflexion/plantarflexion angles at various speed variations. The parallel Kalman filter applied in the proposed method can compensate external acceleration through Markov-chain-based acceleration modeling, that may enhance joint estimation performance in high speed walking conditions. To validate the proposed estimation method, an optical motion capture system was used as reference. In addition, patterns for each condition were investigated to identify and evaluate presence of classifying features.
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