The research paper presents the heterogeneous computing model for analysis & restoration of human walking deformity and posture instability. Gait-related walking activities are very important for the analysis of postural instability, repairment of gait abnormality, diagnosis of cognitive declination, enhance the cognitive ability of human-centered humanoid robot system, and many clinical diagnoses, for example, Parkinson, pathological gait, freezing of gait, etc. at an early stage. For experiment analysis, 10 different lower limb activities are being considered of healthy and crouch walking subjects. A total of 25 healthy and 10 crouch walk subjects are considered for experiment purposes of different age groups, sex, and mental status. To achieve this objective the pattern of 10 different rehabilitation activities are captured using RGB-Depth (RGB-D) camera and classified using heterogeneous deep learning models. Different deep learning models Convolutional Neural Network (CNN) and CNN-LSTM (CNN-Long Short Term Memory) are used for the classification of these rehabilitation exercises. The RGB-D data is obtained using a Microsoft Kinect v2 sensor on a 100 Hz sampling frequency. Experimental results have shown significant activity recognition accuracy with 96% and 98% for CNN and CNN-LSTM models respectively.

中文翻译：

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伤后步行模式恢复与体位稳定性康复运动识别的异构计算模型

研究论文提出了用于分析和恢复人类步行畸形和姿势不稳定的异构计算模型。与步态相关的步行活动对于分析姿势不稳定性、修复步态异常、诊断认知偏差、增强以人为中心的人形机器人系统的认知能力以及许多临床诊断，例如帕金森病、病理步态、早期冻结步态等。对于实验分析，10 种不同的下肢活动正在考虑健康和蹲下行走的受试者。出于不同年龄组、性别和精神状态的实验目的，总共考虑了 25 名健康和 10 名蹲下行走的受试者。为了实现这一目标，使用 RGB-Depth (RGB-D) 相机捕获 10 种不同康复活动的模式，并使用异构深度学习模型进行分类。不同的深度学习模型卷积神经网络 (CNN) 和 CNN-LSTM (CNN-Long Short Term Memory) 用于对这些康复练习进行分类。RGB-D 数据是使用 Microsoft Kinect v2 传感器以 100 Hz 采样频率获得的。实验结果表明，CNN 和 CNN-LSTM 模型的活动识别准确率分别为 96% 和 98%。RGB-D 数据是使用 Microsoft Kinect v2 传感器以 100 Hz 采样频率获得的。实验结果表明，CNN 和 CNN-LSTM 模型的活动识别准确率分别为 96% 和 98%。RGB-D 数据是使用 Microsoft Kinect v2 传感器以 100 Hz 采样频率获得的。实验结果表明，CNN 和 CNN-LSTM 模型的活动识别准确率分别为 96% 和 98%。