Pandey Chandrasen, Roy Diptendu Sinha, Poonia Ramesh Chandra, Altameem Ayman, Nayak Soumya Ranjan, Verma Amit, Saudagar Abdul Khader Jilani
National Institute of Technology, Meghalaya, India.
Department of Computer Science, CHRIST (Deemed to be University), Hosur Road, Bangalore, Karnataka, India.
PPAR Res. 2022 Aug 22;2022:9355015. doi: 10.1155/2022/9355015. eCollection 2022.
Walking (gait) irregularities and abnormalities are predictors and symptoms of disorder and disability. In the past, elaborate video (camera-based) systems, pressure mats, or a mix of the two has been used in clinical settings to monitor and evaluate gait. This article presents an artificial intelligence-based comprehensive investigation of ground reaction force (GRF) pattern to classify the healthy control and gait disorders using the large-scale ground reaction force. The used dataset comprised GRF measurements from different patients. The article includes machine learning- and deep learning-based models to classify healthy and gait disorder patients using ground reaction force. A deep learning-based architecture GaitRec-Net is proposed for this classification. The classification results were evaluated using various metrics, and each experiment was analysed using a fivefold cross-validation approach. Compared to machine learning classifiers, the proposed deep learning model is found better for feature extraction resulting in high accuracy of classification. As a result, the proposed framework presents a promising step in the direction of automatic categorization of abnormal gait pattern.
行走（步态）异常是疾病和残疾的预测指标及症状。过去，临床环境中使用过复杂的视频（基于摄像头）系统、压力垫或两者结合来监测和评估步态。本文基于人工智能对地面反作用力（GRF）模式进行全面研究，以利用大规模地面反作用力对健康对照者和步态障碍进行分类。所使用的数据集包含来自不同患者的GRF测量值。本文包括基于机器学习和深度学习的模型，用于利用地面反作用力对健康和步态障碍患者进行分类。为此分类提出了一种基于深度学习的架构GaitRec-Net。使用各种指标评估分类结果，并且每个实验都采用五折交叉验证方法进行分析。与机器学习分类器相比，发现所提出的深度学习模型在特征提取方面表现更好，从而实现了较高的分类准确率。因此，所提出的框架在异常步态模式自动分类方向上迈出了有前景的一步。
