ABSTRACT
BACKGROUND: Ambulatory individuals with lower-limb amputation perform a variety of locomotor activities, but the step count distribution of these activities is unknown. OBJECTIVE: To describe a novel method for activity monitoring and to use it to count steps taken while walking straight ahead on level ground, turning right and left, up and down stairs, and up and down ramps. STUDY DESIGN: This is an observational study. METHODS: A portable instrument to record leg motion was placed on or inside the prosthetic pylon of 10 individuals with unilateral transtibial amputations. Participants first walked a defined course in a hospital environment to train and validate a machine learning algorithm for classifying locomotor activity. Participants were then free to pursue their usual activities while data were continuously collected over 1-2 d. RESULTS: Overall classification accuracy was 97.5% ± 1.5%. When participants were free to walk about their home, work, and community environments, 82.8% of all steps were in a straight line, 9.0% were turning steps, 4.8% were steps on stairs, and 3.6% were steps on ramps. CONCLUSION: A novel activity monitoring method accurately classified the locomotion activities of individuals with lower-limb amputation. Nearly 1 in 5 of all steps taken involved turning or walking on stairs and ramps.
