One of the challenges for robots to grasp unknown objects is to predict whether objects will fall at the beginning of grasping. Evaluating robotic grasp state accurately and efficiently is a significant step to addres...
详细信息
Reliable environmental context prediction is critical for wearable robots (exoskeletons or prostheses) to assist terrain-adaptive locomotion. Inspired by the mechanism of human perception of the environment, the visio...
详细信息
The accurate detection of lateral walking gait phases is essential for the effective implementation of hip exoskeleton systems in lateral resistance walking exercises. However, limitations in hardware, such as memory ...
详细信息
ISBN:
(数字)9798350344639
ISBN:
(纸本)9798350344646
The accurate detection of lateral walking gait phases is essential for the effective implementation of hip exoskeleton systems in lateral resistance walking exercises. However, limitations in hardware, such as memory and computing power, in the microcontrollers of wearable devices, significantly impact the size and training speed of the lateral walking gait phase detection model, thus affecting the exoskeleton system. This study proposes a data optimization algorithm that utilizes K-means clustering combined with commonly used machine learning algorithms, including Random Forests (RF), Support Vector Machines (SVM), and k-Nearest Neighbors (KNN), to reduce both the training time and size of the model. With the implementation of this algorithm, the training time and model size of RF, SVM, and KNN-based models are reduced by 89.6%, 99.8%, and 97.9%, and 89.6%, 92.7%, and 95.2% respectively. The corresponding gait phase prediction accuracy experiences only a slight decrease of 1.6%, 1.7%, and 2.8% respectively. This method ensures a sufficiently high accuracy in detecting lateral walking gait phases while simultaneously achieving higher efficiency and a smaller model size.
In recent years, based on surface electromyography (sEMG), great progress has been made in gesture recognition tasks, which is significant to the study of computer interaction and prosthesis control. Prior to this, ma...
详细信息
The important purpose of soft exosuit is to decrease the energy consumption of users by providing assistance. If there is something wrong with the judgment of the human gait during the assisting process, the assisting...
详细信息
In the past two decades, with growing focus of lower limb exoskeleton, large variety of rigid exoskeletons were designed for medical rehabilitation and the other purposes. Compared with the rigid exoskeletons, which a...
详细信息
It is well known that terrain recognition and gait cycle prediction are important for powered exoskeleton. However, only a few works have focused on the concerns of complexity of the control system caused by using red...
详细信息
Soft Exosuit is a kind of Lower-limb wearable robots to augment and assist the wearer's performance. The wearer need different assistance modes to reduce the metabolic rate when walking on different terrains. Ther...
详细信息
For wearable soft exosuits, an imprecise control strategy can easily injure the wearer, while real-time terrain recognition and accurate gait phase estimation can effectively improve the control strategy of the exosui...
For wearable soft exosuits, an imprecise control strategy can easily injure the wearer, while real-time terrain recognition and accurate gait phase estimation can effectively improve the control strategy of the exosuit and make the wearer comfortable and safe. In this paper, a real-time terrain recognition algorithm based on Transformer and a gait estimation algorithm based on CNN-LSTM-Attention are implemented. The Transformer-based recognition of different terrains improves the recognition accuracy to some extent, and the CNN-LSTM-Attention based feature extraction for temporal signals such as gait phase is also extremely noticeable. Experiments show that the Transformer-based algorithm achieves 99.64 % recognition accuracy in six different terrain environments. In the gait phase estimation experiment, CNN-LSTM-CBAM achieved the best performance with an evaluation index r2 of 0.9221. The aforementioned terrain recognition algorithms and gait phase estimation algorithms may have a positive impact on soft exosuit and dynamic prosthetics research.
Squat gait is the most common abnormal gait in children with cerebral palsy. Gait training with lower limb rehabilitation robots can improve rehabilitation efficiency and reduce manual labor for them. A novel exoskele...
详细信息
ISBN:
(数字)9798350355123
ISBN:
(纸本)9798350355130
Squat gait is the most common abnormal gait in children with cerebral palsy. Gait training with lower limb rehabilitation robots can improve rehabilitation efficiency and reduce manual labor for them. A novel exoskeleton-based lower limb rehabilitation robot for children with cerebral palsy is proposed, considering the squat gait characteristics of children with cerebral palsy. The structure of the lower limb rehabilitation robot, which combines hip and knee with 4 active degrees of freedom exoskeleton and mobile walker, is designed and optimized. Static analysis of the key mechanical structure of the exoskeleton is conducted using ANSYS Workbench, and local structures are optimized to ensure the safety factor of the mechanical structure is greater than 1, guaranteeing reasonable structural strength and stiffness design. Dynamic simulation analysis based on the ADAMS model validates the reliability and dynamic walking effect of the lower limb exoskeleton rehabilitation robot, obtaining the curve relationship between joint angles and motion cycles, thus verifying the effectiveness of the mechanical system motion.
暂无评论