Evaluation of human gait through smartphone-based pose estimation algorithms provides an attractive alternative to costly lab-bound instrumented assessment and offers a paradigm shift with real time gait capture for clinical assessment. Systems based on smart phones, such as OpenPose and BlazePose have demonstrated potential for virtual motion assessment but still lack the accuracy and repeatability standards required for clinical viability. Seq2seq architecture offers an alternative solution to conventional deep learning techniques for predicting joint kinematics during gait. This study introduces a novel enhancement to the low-powered BlazePose algorithm by incorporating a Seq2seq autoencoder deep learning model. To ensure data accuracy and reliability, synchronized motion capture involving an RGB camera and ten Vicon cameras were employed across three distinct self-selected walking speeds. This investigation presents a groundbreaking avenue for remote gait assessment, harnessing the potential of Seq2seq architectures inspired by natural language processing (NLP) to enhance pose estimation accuracy. When comparing BlazePose alone to the combination of BlazePose and 1D convolution Long Short-term Memory Network (1D-LSTM), Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM), the average mean absolute errors decreased from 13.4° to 5.3° for fast gait, from 16.3° to 7.5° for normal gait, and from 15.5° to 7.5° for slow gait at the left ankle joint angle respectively. The strategic utilization of synchronized data and rigorous testing methodologies further bolsters the robustness and credibility of these findings.

中文翻译：

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BlazePose-Seq2Seq：利用常规 RGB 相机进行稳健的步态评估

通过基于智能手机的姿势估计算法对人类步态进行评估，为昂贵的实验室仪器评估提供了一种有吸引力的替代方案，并为临床评估提供了实时步态捕获的范式转变。基于智能手机的系统（例如 OpenPose 和 BlazePose）已展现出虚拟运动评估的潜力，但仍缺乏临床可行性所需的准确性和可重复性标准。 Seq2seq 架构为传统深度学习技术提供了一种替代解决方案，用于预测步态期间的关节运动学。本研究通过结合 Seq2seq 自动编码器深度学习模型，对低功耗 BlazePose 算法进行了新颖的增强。为了确保数据的准确性和可靠性，在三种不同的自选步行速度下采用了涉及 RGB 摄像头和十个 Vicon 摄像头的同步动作捕捉。这项研究为远程步态评估提供了一条突破性的途径，利用受自然语言处理 (NLP) 启发的 Seq2seq 架构的潜力来提高姿势估计的准确性。将单独的 BlazePose 与 BlazePose 和 1D 卷积长短期记忆网络 (1D-LSTM)、门控循环单元 (GRU) 和长短期记忆 (LSTM) 的组合进行比较时，平均绝对误差从 13.4° 下降到左踝关节角度分别为快步态5.3°、正常步态16.3°至7.5°、慢步态15.5°至7.5°。同步数据和严格测试方法的战略利用进一步增强了这些发现的稳健性和可信度。