Human locomotor activity (LA) recognition is important in the control of exoskeletons and prostheses and in patient monitoring. This article presents a practical recognition approach that can classify level walking, stair ascent, and stair descent activities across different subjects and diverse gait patterns. The thigh angle is measured and utilized in this method to construct a phase curve in an activity-specific coordinate frame during a stride. The LA is recognized by matching the curvature of its phase curve to the expected one. The factors affecting the adaptability of the proposed method to gait variations are analyzed and compensated for. The proposed method is evaluated with eight subjects who are asked to perform the three types of activity at two different cadences: 70 stepsmin and 110 stepsmin. Experimental results show that the proposed classifier outperforms an existing phase variable based classifier in all validation experiments and a }$-nearest neighbor classifier when using nonsubject-specific training data, indicating that the proposed method has superior adaptability to changes in human and in strides. Moreover, the feature used in the proposed method has demonstrated the potential in quantitatively indicating the extent of neuromotor impairments of patients.

This work in published on IEEE Transactions on Human-Machine Systems 51.6(2021):684-695.