Human gait has been shown to provide crucial motion cues for various applications. Recognizing patterns in human gait has been widely adopted in various application areas such as security, virtual reality gaming, medical rehabilitation, and ailment identification. Furthermore, wearable inertial sensors have been widely used for not only recording gait but also to predict users' demography. Machine Learning techniques such as deep learning, combined with inertial sensor signals, have shown promising results in recognizing patterns in human gait and estimate users' demography. However, the black-box nature of such deep learning models hinders the researchers from uncovering the reasons behind the model's predictions. Therefore, we propose leveraging deep learning and Layer-Wise Relevance Propagation (LRP) to identify the important variables that play a vital role in identifying the users' demography such as age and gender. To assess the efficacy of this approach we train a deep neural network model on a large sensor-based gait dataset consisting of 745 subjects to identify users' age and gender. Using LRP we identify the variables relevant for characterizing the gait patterns. Thus, we enable interpretation of non-linear ML models which are experts in identifying the users' demography based on inertial signals. We believe this approach can not only provide clinicians information about the gait parameters relevant to age and gender but also can be expanded to analyze and diagnose gait disorders.

翻译：人类步态已被证明能够为多种应用提供关键的运动线索。步态模式识别已广泛应用于安全监控、虚拟现实游戏、医疗康复及疾病识别等领域。此外，可穿戴惯性传感器不仅用于记录步态，还被用于预测用户的人口统计学特征。深度学等机器学习技术与惯性传感器信号相结合，已在步态模式识别和用户人口统计学估计方面展现出良好效果。然而，这类深度学习模型的"黑箱"特性阻碍了研究人员揭示模型预测背后的原因。为此，我们提出利用深度学习与逐层相关性传播（LRP）方法，识别对判断用户年龄、性别等人口统计学特征起关键作用的重要变量。为评估该方法有效性，我们在包含745名受试者的大规模基于传感器的步态数据集上训练深度神经网络模型，以识别用户的年龄和性别。通过LRP方法，我们识别出与步态模式表征相关的变量，从而实现对基于惯性信号识别用户人口统计学的非线性机器学习模型的解释。我们相信，该方法不仅能为临床医生提供与年龄和性别相关的步态参数信息，还可扩展应用于步态障碍的分析与诊断。