GaitPy: An Open-Source Python Package for Gait Analysis Using an Accelerometer on the Lower Back

Abstract

Gait impairments are present across a broad range of conditions and often have a significant impact on the functional mobility and quality of life of an individual. Clinicians and researchers commonly assess gait using either observational scales (e.g. Unified Parkinson’s Disease Rating Scale) or performance-based tests (e.g. timed-up-and-go). However, these assessments can only be performed intermittently because of the need for a trained clinician. In contrast, wearable devices can be used for continuously capturing data from sensors (e.g. accelerometer, ECG) outside the clinic. Recently, several groups (Del Din, Godfrey, & Rochester, 2016; McCamley, Donati, Grimpampi, & Mazzà, 2012; Trojaniello, Cereatti, & Della Croce, 2014; Zijlstra & Hof, 2003) have published algorithms for deriving features of gait from data collected using inertial sensors like accelerometers. However, an implementation of these algorithms is not readily available to researchers, thus hindering progress.