Gait recognition in elderly patient monitoring is a standard process that employs medical healthcare systems, wearable sensors, motion capturing devices, and Information and Communication Technologies (ICT). The patterns of the patient movement are observed at different time instances for identifying the abnormality in gaits to provide better assistance. In this article, a novel Overlapping Gait Pattern Recognition method based on Regression Learning (RL) is introduced. This method classifies the gait pattern based on the direction of movement and angle of deviation of the patient at the initial stage. The analyses of differentiation are performed using RL for identifying the errors and differences in gait patterns through correlation. The errors are recurrently analyzed through different iterates for approximating the recognition accuracy in a reduced time. The classification of patterns through correlation and conditional analysis of the regression helps identify the errors through intense learning and deviation identification. The proposed method is found to achieve better recognition accuracy, fewer error rates, and smaller recognition delays for different gait patterns.

老年患者监测中的步态识别是一个标准过程，采用医疗保健系统，可穿戴传感器，运动捕捉设备以及信息和通信技术（ICT）。在不同的时间点观察患者的运动模式，以识别步态异常，以提供更好的帮助。本文介绍了一种基于回归学习（RL）的新颖的重叠步态模式识别方法。该方法根据初始阶段患者的运动方向和偏斜角度对步态进行分类。使用RL进行差异分析，以通过相关识别步态模式的错误和差异。通过不同的迭代来反复分析错误，以在较短的时间内逼近识别精度。通过相关性和回归的条件分析对模式进行分类，有助于通过深入学习和偏差识别来识别错误。发现所提出的方法对于不同的步态模式实现了更好的识别精度，更少的错误率和更小的识别延迟。