To address the problems in the application of lower limb exoskeleton mechanical equipment, XGBOOT Algorithm-based research on gait phase recognition is carried out, only using motion attitude data measured by a single IMU. Firstly, foot motion data of six different gaits are collected, and each gait is divided into four phases. On this basis, XGBOOT algorithm optimized is applied to analyze the gait phase recognition with the foot motion data as the training set. In the process of establishing the model, the parameters involved in the model are further optimized by the Bayesian optimization algorithm（BOA）. Through calculation, the results show that the average accuracy of the model is 89.26% in the verification set, the precision of the model is 89.64% in the verification set, the recall rate of the model is 89.26% in the verification set, F1 value of the model is 89.10% in the verification set, which indicates that the model can achieve better gait phase recognition.

In order to improve the recognition rate for lower extremity motion patterns, this study designs a recognition method for such patterns, which integrates electromyography (EMG) and inertial measurement unit (IMU) signals in three posture modes, including walking on the ground, squatting, and extending seated legs, to address the difficulty with obtaining high signal-to-noise ratio EMG and IMU signals synchronously. Besides, this study proposes a synchronous analysis method for EMG and IMU dual-mode information to correct antipower frequency interference accelerometer signals. The collected signals are preprocessed to extract eigenvalues. And by using the kernel principal component analysis (KPCA), the information on these eigenvalues is fused. Finally, according to the characteristics of the data, a Bayesian-optimized XGBOOST algorithm is designed. Lower-limb movement patterns are classified with the feature vector put into the optimization algorithm. Multiperson experimental results show that the average recognition accuracy for different poses can reach 94.42%, the average F1 value 95.33%, and the average return value 95.68%, proving that the model proposed can be used to identify human motion intentions and its generalization ability can detect individual differences in human bodies.

Aiming at the problems of individual differences in the asynchrony process of human lower limbs and random changes in stride during walking, this paper proposes a method for gait recognition and prediction using motion posture signals. The research adopts an optimized gated recurrent unit (GRU) network algorithm based on immune particle swarm optimization (IPSO) to establish a network model that takes human body posture change data as the input, and the posture change data and accuracy of the next stage as the output, to realize the prediction of human body posture changes. This paper first clearly outlines the process of IPSO's optimization of the GRU algorithm. It collects human body posture change data of multiple subjects performing flat-land walking, squatting, and sitting leg flexion and extension movements. Then, through comparative analysis of IPSO optimized recurrent neural network (RNN), long short-term memory (LSTM) network, GRU network classification and prediction, the effectiveness of the built model is verified. The test results show that the optimized algorithm can better predict the changes in human posture. Among them, the root mean square error (RMSE) of flat-land walking and squatting can reach the accuracy of 10 -3, and the RMSE of sitting leg flexion and extension can reach the accuracy of 10 -2. The R 2 value of various actions can reach above 0.966. The above research results show that the optimized algorithm can be applied to realize human gait movement evaluation and gait trend prediction in rehabilitation treatment, as well as in the design of artificial limbs and lower limb rehabilitation equipment, which provide a reference for future research to improve patients' limb function, activity level, and life independence ability.

针对人体下肢的被动康复训练过程,文中使用某下肢康复训练机器人进行下肢被动训练,训练过程中采集下肢股直肌和腓肠肌的sEMG信号和大腿的运动姿态信号,运用机器学习方法对运动姿态信号和表面肌电(sEMG)信号进行分析,实现了下肢中4种不同被动训练状态的识别及其对下肢肌群训练康复效果的评价.结果显示:联合使用IMU和sEMG进行下肢被动训练过程监测,并通过机器学习算法进行处理,可以实现不同监测过程的自动识别以及下肢肌群训练效果量化分析判断.研究结果可为实现基于下肢被动康复训练过程的智能控制与康复情况评价奠定研究基础.

为实现快速步态状态判断,以更好地对下肢外骨骼进行高精度的步态识别和控制,进行了基于可穿戴惯性测量装置检测人体姿态变化的算法研究.通过对人体下肢的跌倒、转弯、蹲坐与起立等非周期性步态变化活动进行测算试验,获得了受试者实验过程中身体角度、下肢关节角速度和加速度变化等数据,随后应用随机森林等4种机器学习经典分类算法对受试者进行了活动识别对比分析,结果表明,决策树监督学习算法相对于其他算法,能够快速、准确地检测并判断出人体非周期性变化中的多种活动状态,历次识别精度均可达到99％以上,为可穿戴智能装备的开发与应用提供理论基础.

针对下肢术后康复训练需求,设计并开发了一种基于嵌入式控制器和远程控制架构的下肢康复机器人控制与监测系统,实现了下肢康复机器人的主动/被动训练模式控制、运动姿态与肌电信号采集、WIFI通讯、安全保护等功能,通过应用随机森林机器学习算法和线性回归算法实现了训练过程的识别与分析.实验结果表明:所研制的下肢康复机器人控制与监测系统可以通过安卓进行便携控制,并能通过训练过程中的监测信号,实现对训练过程的智能分析.同时可知,随机森林算法相对线性回归算法在运动识别方面更有优势,这对训练过程的自动监测和智能化控制具有积极意义.