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.

The purpose of this study is to develop a gait-event detection system, which is necessary for the cycle-to-cycle FES control of locomotion. Proposed gait event detection system consists of a signal measurement part and gait event detection part. The signal measurement was composed of the sensors and the LabVIEW program for the data acquisition and synchronization of the sensor signals. We also used a video camera and a motion capture system to get the reference gait events. Machine learning technique with ANN (artificial neural network) was adopted for automatic detection of gait events. 2 cycles of reference gait events were used as the teacher signals for ANN training and the remnants (2∼5 cycles) were used for the evaluation of the performance in gait-event detection. 14 combinations of sensor signals were used in the training and evaluation of ANN to examine the relationship between the number of sensors and the gait-event detection performance. The best combinations with minimum errors of event-detection time were 1）goniometer, foot-switch and 2）goniometer, foot-switch, accelerometer x(anterior-posterior) component. It is expected that the result of this study will be useful in the design of cycle-to-cycle FES controller.

This research was designed to investigate biomechanical aspects of the evolution based on the hypothesis of dynamic cooperative interactions between the locomotion pattern and the body shape in the evolution of human bipedal walking. The musculoskeletal model used in the computer simulation consisted of 12 rigid segments and 26 muscles. The nervous system was represented by 18 rhythmic pattern generators. The genetic algorithm was employed based on the natural selection theory to represent the evolutionary mechanism. Evolutionary strategy was assumed to minimize the cost function that is weighted sum of the energy consumption, the muscular fatigue and the load on the skeletal system. The simulation results showed that repeated manipulations of the genetic algorithm resulted in the change of body shape and locomotion pattern from those of chimpanzee to those of human. It was suggested that improving locomotive efficiency and the load on the musculoskeletal system are feasible factors driving the evolution of the human body shape and the bipedal locomotion pattern. The hypothetical evolution method employed in this study can be a new powerful tool for investigation of the evolution process.