A toolbox for the identification of foot-floor contact sequences to analyze atypical gait cycles in a real-life scenario: application on patients after proximal femur fracture and healthy elderly.

Abstract

Background: The detection of gait subphases is pivotal for a comprehensive assessment of gait quality, playing a key role in different applications such as rehabilitation programs, movement disorder diagnostics, and fall prevention strategies. However, few methods provide dynamic subphase segmentation relying solely on plantar pressure signals in real-life, unsupervised conditions. This work aims to present an open-source, flexible toolbox for the automatic detection of gait subphases, and to introduce novel digital gait biomarkers derived from subphase analysis, enabling effective monitoring of frail patients in real-world, challenging environments.

Methods: A novel MATLAB toolbox for decoding gait subphases from plantar pressure signals (PIN2GPI - from Pressure INsoles to Gait Phase Identification) is described and made publicly available. To test our algorithm, the open database provided by the Mobilise-D consortium is used, focusing on walking bouts recorded through pressure insoles in an unsupervised setting during free activities of daily living (lasting approximately 2.5 h). We extracted relevant gait parameters from a population of 32 elderly subjects: 14 frail patients after Proximal Femur Fracture (PFF) and 18 older Healthy Adults (HA).

Results: On average, PFF patients showed, with respect to HA, a reduced number of gait cycles (1059 ± 201 vs. 2076 ± 246; p = 0.006), percentage of time spent walking (9.1 ± 1.7% vs. 15.0 ± 1.9%; p = 0.04), and cadence (39.2 ± 2.0 cycles/min vs. 45.7 ± 1.2 cycles/min; p = 0.007), as well as an increased percentage of atypical gait cycles on the worst side (8.8 ± 4.1%/min vs. 0.8 ± 0.1%/min; p = 0.007), interlimb gait asymmetries in flat-foot contact (6.9 ± 1.2% of the Gait Cycle (%GC) vs. 2.5 ± 0.4%GC; p = 0.007) and swing subphase durations (6.5 ± 1.6%GC vs. 1.6 ± 0.3%GC; p = 0.0003).

Conclusion: These findings highlight the potential of gait subphase analysis as a valuable tool for pinpointing key factors related to walking quality from real-life measurements collected during unsupervised monitoring of frail subjects, paving the way to more precise and objective gait assessment in real-life scenarios.