Atypical Gait Cycles Measured in Free-Living Conditions for Fall Prevention of Frail Subjects

Fall-risk assessment of frail individuals is pivotal to implement fall prevention campaigns. Within the framework of the ongoing project MOVEWISE (Mobility Observation Via Wearable Integrated Sensor Evaluation), the aim of this work is to introduce digital gait biomarkers for monitoring frail patients in an ecological (but challenging) scenario. The open database provided by the Mobilise-D consortium was used to test our algorithm. We analyzed walking bouts recorded through pressure insoles in an unsupervised setting, during free activities of daily living (lasting approximately 2.5 hours). 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)). On average, PFF patients showed a reduced number of gait cycles (PFF: 524 ± 100, HA: 1030 ± 123, p=0.006), a reduced cadence (PFF: 35.5 ± 1.8 cycles/min, HA: 42.3 ± 1.3 cycles/min, p = 0.005), an increased percentage of atypical gait cycles (PFF: 0.90 ± 0.23 %/cycles/min, HA: 0.38 ± 0.06 %/cycles/min, p=0.046), and more asymmetrical gait phases, significantly different for Flat-Foot contact (PFF: 6.5 ±1.3 % of gait cycle, HA: 2.5 ± 0.4 % of gait cycle, p=0.003) and Swing (PFF: 6.6 ± 1.6 % of gait cycle, HA: 1.7 ± 0.3 % of gait cycle, p=0.002). The proposed pipeline was able to extract informative gait parameters although the recordings were performed out-of-lab in an unsupervised environment, efficiently pinpointing key factors related to fall risk.