Gait abnormalities are typically derived from neurological conditions or orthopaedic problems and can cause severe consequences such as limited mobility and falls. Gait analysis plays a crucial role in monitoring gait abnormalities and discovering underlying deficits can help develop rehabilitation programs. Contemporary gait analysis requires a multi-modal gait analysis approach where spatio-temporal, kinematic and muscle activation gait characteristics are investigated. Additionally, protocols for gait analysis are going beyond labs/clinics to provide more habitual insights, uncovering underlying reasons for limited mobility and falls during daily activities. Wearables are the most prominent technology that are reliable and allow multi-modal gait analysis beyond the labs/clinics for extended periods. There are established wearable-based algorithms for extracting informative gait characteristics and interpretation. This paper proposes a multi-layer fusion framework with sensor, data and gait characteristics. The wearable sensors consist of four units (inertial and electromyography, EMG) attached to both legs (shanks and thighs) and surface electrodes placed on four muscle groups. Inertial and EMG data are interpreted by numerous validated algorithms to extract gait characteristics in different environments. This paper also includes a pilot study to test the proposed fusion approach in a small cohort of stroke survivors. Experimental results in various terrains show healthy participants experienced the highest pace and variability along with slightly increased knee flexion angles (≈1°) and decreased overall muscle activation level during outdoor walking compared to indoor, incline walking activities. Stroke survivors experienced slightly increased pace, asymmetry, and knee flexion angles (≈4°) during outdoor walking compared to indoor. A multi-modal approach through a sensor, data and gait characteristic fusion presents a more holistic gait assessment process to identify changes in different testing environments. The utilisation of the fusion approach presented here warrants further investigation in those with neurological conditions, which could significantly contribute to the current understanding of impaired gait.

步态异常通常源于神经系统疾病或骨科问题，并可能导致严重后果，例如活动受限和跌倒。步态分析在监测步态异常方面起着至关重要的作用，发现潜在的缺陷可以帮助制定康复计划。当代步态分析需要多模式步态分析方法，其中研究时空、运动学和肌肉激活步态特征。此外，步态分析协议正在超越实验室/诊所，以提供更多的习惯性见解，揭示日常活动中活动受限和跌倒的根本原因。可穿戴设备是最突出的技术，它可靠并允许在实验室/诊所之外进行长时间的多模式步态分析。已经建立了基于可穿戴设备的算法，用于提取信息性步态特征和解释。本文提出了一种具有传感器、数据和步态特征的多层融合框架。可穿戴传感器由连接到双腿（小腿和大腿）的四个单元（惯性和肌电图，EMG）和放置在四个肌肉群上的表面电极组成。惯性和 EMG 数据由许多经过验证的算法解释，以提取不同环境中的步态特征。本文还包括一项试点研究，以在一小群中风幸存者中测试提议的融合方法。在各种地形中的实验结果表明，与室内倾斜步行活动相比，在户外步行期间，健康的参与者经历了最高的速度和可变性，膝盖弯曲角度（≈1°）略有增加，整体肌肉激活水平降低。与室内相比，中风幸存者在户外步行时的步伐、不对称和膝关节屈曲角度（≈4°）略有增加。通过传感器、数据和步态特征融合的多模式方法提供了一个更全面的步态评估过程，以识别不同测试环境中的变化。此处介绍的融合方法的使用需要对神经系统疾病患者进行进一步研究，这可能会显着促进当前对步态受损的理解。倾斜步行活动。与室内相比，中风幸存者在户外步行时的步伐、不对称性和膝关节屈曲角度（≈4°）略有增加。通过传感器、数据和步态特征融合的多模式方法提供了一个更全面的步态评估过程，以识别不同测试环境中的变化。此处介绍的融合方法的使用需要对神经系统疾病患者进行进一步研究，这可能会显着促进当前对步态受损的理解。倾斜步行活动。与室内相比，中风幸存者在户外步行时的步伐、不对称性和膝关节屈曲角度（≈4°）略有增加。通过传感器、数据和步态特征融合的多模式方法提供了一个更全面的步态评估过程，以识别不同测试环境中的变化。此处介绍的融合方法的使用需要对神经系统疾病患者进行进一步研究，这可能会显着促进当前对步态受损的理解。数据和步态特征融合提供了一个更全面的步态评估过程，以识别不同测试环境中的变化。此处介绍的融合方法的使用需要对神经系统疾病患者进行进一步研究，这可能会显着促进当前对步态受损的理解。数据和步态特征融合提供了一个更全面的步态评估过程，以识别不同测试环境中的变化。此处介绍的融合方法的使用需要对神经系统疾病患者进行进一步研究，这可能会显着促进当前对步态受损的理解。