A Locomotion Mode Adaptive Strategy for Real-Time Detection of Gait Events During Negotiating Staircases

Abstract

The joint kinematics for negotiating different locomotion modes (LMs), mainly level-ground walking (LGW) and ascending and descending staircases, are significantly different. Therefore, an algorithm that could detect gait events during the LGW would not be efficient during the stair ascent (SA) and stair descent (SD) even though the one designed for SA would not be suitable for SD, and vice versa. In this study, a novel gait event detection (GED) strategy is proposed, which is adaptive to the change in LM. This adaptability provided the flexibility of using different sets of rules for different LMs. For this purpose, an LM classification strategy was designed that supervised the selection of the rule-base for three LMs. The rules were applied to the kinematic data collected using two inertial sensors attached to the shank and foot of the subject. This method was validated with ten control subjects recruited for the experiment. The proposed method was seen to improve the overall performance of GED compared with the existing methods that used the same algorithm for all three LMs.