A new strategy for parameter optimization to improve phase-dependent locomotion mode recognition

Phase-dependent recognition strategy is an effective approach for lower-limb locomotion mode recognition. However, in previous studies, classifiers, feature sets and other parameters for the classification are the same for all the phases. The potential of this method could therefore be limited, as movement characteristics of different phases are not the same. In this paper, we aim to further improve phase-dependent recognition by proposing a new parameter optimization strategy which optimizes classifier, feature set and window size individually for each phase. Seven able-bodied subjects and one transtibial amputee subject are recruited in this research and they are required to perform six kinds of locomotion tasks. Signals recorded from two inertial measurement units and one pressure insole of the measured side are used for feature set calculation. And phase-dependent recognition method with four phases defined is applied for locomotion mode identification. The proposed strategy for parameter optimization is proved to be more efficient than the conventional optimization strategy by providing better overall recognition performance and lower computation burden.