Ghislieri Marco, Leo Nicolas, Caruso Marco, Becker Clemens, Cereatti Andrea, Agostini Valentina
Department of Electronics and Telecommunications, PolitoBIOMed Lab, Politecnico di Torino, Torino, 10129, Italy.
Unit Digital Geriatrie, Geriatrisches Zentrum, Universitätsklinikum, University of Heidelberg, Heidelberg, Germany.
J Neuroeng Rehabil. 2025 Jul 14;22(1):161. doi: 10.1186/s12984-025-01683-z.
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.
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).
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).
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.
步态子阶段的检测对于全面评估步态质量至关重要，在康复计划、运动障碍诊断和跌倒预防策略等不同应用中发挥着关键作用。然而，很少有方法能在现实生活中的无监督条件下仅依靠足底压力信号进行动态子阶段分割。本研究旨在提供一个用于自动检测步态子阶段的开源、灵活工具箱，并引入从子阶段分析中得出的新型数字步态生物标志物，以便在现实世界中具有挑战性的环境中有效监测体弱患者。
描述了一种用于从足底压力信号中解码步态子阶段的新型MATLAB工具箱（PIN2GPI - 从压力鞋垫到步态阶段识别）并公开提供。为了测试我们的算法，使用了Mobilise-D联盟提供的开放数据库，重点关注在日常生活自由活动期间通过压力鞋垫在无监督环境下记录的步行时段（持续约2.5小时）。我们从32名老年受试者群体中提取了相关步态参数：14名股骨近端骨折（PFF）后的体弱患者和18名健康老年人（HA）。
平均而言，与HA相比, PFF患者的步态周期数量减少（1059±201对2076±246；p = 0.006），步行时间百分比降低（9.1±1.7%对15.0±1.9%；p = 0.04），步频降低（39.2±2.0次/分钟对45.7±1.2次/分钟；p = 0.007），以及最差侧非典型步态周期的百分比增加（8.8±4.1%/分钟对0.8±0.1%/分钟；p = 0.007），扁平足接触时的肢体间步态不对称（占步态周期（%GC）的6.9±1.2%对2.5±0.4%GC；p = 0.007）和摆动子阶段持续时间（6.5±1.6%GC对1.6±0.3%GC；p = 0.0003）。
这些发现突出了步态子阶段分析作为一种有价值工具的潜力，可从体弱受试者无监督监测期间收集的现实生活测量中确定与步行质量相关的关键因素，为现实生活场景中更精确和客观的步态评估铺平道路。
