In response to the problems of difficult identification of degradation stage start points and inadequate extraction of degradation features in the current rolling bearing remaining life prediction method, a rolling bearing remaining life prediction method based on multi-scale feature extraction and attention mechanism is proposed. Firstly, this paper takes the normalized bearing vibration signal as input and adopts a quadratic function as the RUL prediction label, avoiding identifying the degradation stage start point. Secondly, the spatial and temporal features of the bearing vibration signal are extracted using the dilated convolutional neural network and LSTM network, respectively, and the channel attention mechanism is used to assign weights to each degradation feature to effectively use multi-scale information. Finally, the mapping of bearing degradation features to remaining life labels is achieved through a fully connected layer for the RUL prediction of bearings. The proposed method is validated using the PHM 2012 Challenge bearing dataset, and the experimental results show that the predictive performance of the proposed method is superior to that of other RUL prediction methods.

https://www.mdpi.com/2079-9292/11/21/3616/pdf?version=1667880767

A Method for Predicting the Remaining Life of Rolling Bearings Based on Multi-Scale Feature Extraction and Attention Mechanism

This article presents the possibilities of newly developed middleware dedicated for distributed and modular control systems. The software enables the exchange of information locally, within one control module, and globally, between many modules. The executed information exchange system speed tests confirmed the correct operation of the software. The middleware was used in the control system of the active upper-limb exoskeleton. The upper-limb rehabilitation exoskeleton structure with six degrees of mechanical freedom is presented. The tests were performed using the prototype with three joints. The drives’ models of individual joints were developed and simulated. As a result, the courses of the motion trajectory were shown for different kinds of pressure on the force sensors, and different methods of signal filtering. The tests confirmed a correct operation of middleware and drives control system.

/pdf/the-middleware-for-an-exoskeleton-assisting-upper-limb-to5u74dx.pdf

The Middleware for an Exoskeleton Assisting Upper Limb Movement

Dynamic time warping under limited warping path length (LDTW) is a state-of-the-art time series similarity evaluation method. However, it suffers from high space-time complexity, which makes some large-scale series evaluations impossible. In this paper, an alternating matrix with a concise structure is proposed to replace the complex three-dimensional matrix in LDTW and reduce the high complexity. Furthermore, an evolutionary chain tree is proposed to represent the warping paths and ensure an effective retrieval of the optimal one. Experiments using the benchmark platform offered by the University of California-Riverside show that our method uses 1.33% of the space, 82.7% of the time used by LDTW on average, which proves the efficiency of the proposed method.

/pdf/improved-ldtw-algorithm-based-on-the-alternating-matrix-and-3173pyk3.pdf

Improved LDTW Algorithm Based on the Alternating Matrix and the Evolutionary Chain Tree

Accurate recognition of disabled persons’ behavioral intentions is the key to reconstructing hand function. Their intentions can be understood to some extent by electromyography (EMG), electroencephalogram (EEG), and arm movements, but they are not reliable enough to be generally accepted. In this paper, characteristics of foot contact force signals are investigated, and a method of expressing grasping intentions based on hallux (big toe) touch sense is proposed. First, force signals acquisition methods and devices are investigated and designed. By analyzing characteristics of signals in different areas of the foot, the hallux is selected. The peak number and other characteristic parameters are used to characterize signals, which can significantly express grasping intentions. Second, considering complex and fine tasks of the assistive hand, a posture control method is proposed. Based on this, many human-in-the-loop experiments are conducted using human–computer interaction methods. The results showed that people with hand disabilities could accurately express their grasping intentions through their toes, and could accurately grasp objects of different sizes, shapes, and hardness using their feet. The accuracy of the action completion for single-handed and double-handed disabled individuals was 99% and 98%, respectively. This proves that the method of using toe tactile sensation for assisting disabled individuals in hand control can help them complete daily fine motor activities. The method is easily acceptable in terms of reliability, unobtrusiveness, and aesthetics.

/pdf/application-of-foot-hallux-contact-force-signal-for-2048duub.pdf

Application of Foot Hallux Contact Force Signal for Assistive Hand Fine Control

Computer vision researchers have been tasked with developing applications to recognize the human body's structure, movements, and tracking. Recognizing human upper limb movements has brought benefits in physical therapy, virtual reality, human-robot systems, sports. This paper presents a multi-layer perceptron (MLP) artificial neural network (ANN) designed to recognize the upper limb movements performance in daily life tasks. For this, inertial measurement units (IMU) were employed to acquire information regarding of the upper limb movement. The performance of the model was assessed through a confusion matrix and the receiver operator characteristic (ROC) curves. Recognition accuracy obtained for the ANN model was 97.39%, the mean area under the curve (AUC) of the ROC curves was 0.973. According to the results, the proposed ANN could recognize upper limb movements in daily life tasks. Further research is suggested to test the model with fine movements.

Human Upper Limb Motion Recognition Using IMU sensors and Artificial Neural Networks

Using motion information of the upper limb to control the prosthetic hand has become a hotspot of current research. The operation of the prosthetic hand must also be coordinated with the user’s intention. Therefore, identifying action intention of the upper limb based on motion information of the upper limb is key to controlling the prosthetic hand. Since a wearable inertial sensor bears the advantages of small size, low cost, and little external environment interference, we employ an inertial sensor to collect angle and angular velocity data during movement of the upper limb. Aiming at the action classification for putting on socks, putting on shoes and tying shoelaces, this paper proposes a recognition model based on the Dynamic Time Warping (DTW) algorithm of the motion unit. Based on whether the upper limb is moving, the complete motion data are divided into several motion units. Considering the delay associated with controlling the prosthetic hand, this paper only performs feature extraction on the first motion unit and the second motion unit, and recognizes action on different classifiers. The experimental results reveal that the DTW algorithm based on motion unit bears a higher recognition rate and lower running time. The recognition rate reaches as high as 99.46%, and the average running time measures 8.027 ms. In order to enable the prosthetic hand to understand the grasping intention of the upper limb, this paper proposes a Generalized Regression Neural Network (GRNN) model based on 10-fold cross-validation. The motion state of the upper limb is subdivided, and the static state is used as the sign of controlling the prosthetic hand. This paper applies a 10-fold cross-validation method to train the neural network model to find the optimal smoothing parameter. In addition, the recognition performance of different neural networks is compared. The experimental results show that the GRNN model based on 10-fold cross-validation exhibits a high accuracy rate, capable of reaching 98.28%. Finally, the two algorithms proposed in this paper are implemented in an experiment of using the prosthetic hand to reproduce an action, and the feasibility and practicability of the algorithm are verified by experiment.

https://www.mdpi.com/1424-8220/22/5/1954/pdf?version=1646215625

Recognition of Upper Limb Action Intention Based on IMU

A prosthetic hand is one of the main ways to help patients with upper limb disabilities regain their daily living abilities. Prosthetic hand manipulation must be coordinated with the user’s action intention. Therefore, the key to the control of the prosthetic hand is to recognize the action intention of the upper limb. At present, there are still problems such as difficulty in decoding information and a low recognition rate of identifying action intention with EMG signals and EEG signals. While inertial sensors have the advantages of low cost and high accuracy and posture information can characterize the upper limb motion state, visual information has the advantages of high information and being able to detect the type of target objects, which can be complementarily fused with inertial sensors to further grasp the human motion requirements. Therefore, this paper proposes an upper limb action intention recognition method based on the fusion of posture information and visual information. The inertial sensor is used to collect the attitude angle data during the movement of the upper limb, and according to the similarity of the human upper limb structure to the linkage mechanism, a model of the upper limb of the human body is established using the positive kinematics theory of a mechanical arm to solve the upper limb end positions. The upper limb end positions were classified into three categories: torso front, upper body nearby, and the initial position, and a multilayer perceptron model was trained to learn the classification relationships. In addition, a miniature camera was installed on the hand to obtain visual image information during upper limb movement. The target objects are detected using the YOLOv5 deep learning method, and then, the target objects are classified into two categories: wearable items and non-wearable items. Finally, the upper limb intention is jointly decided by the upper limb motion state, target object type, and upper limb end position to achieve the control of the prosthetic hand. We applied the upper limb intention recognition method to the experimental system of a mechanical prosthetic hand and invited several volunteers to test it. The experimental results showed that the intention recognition success rate reached 92.4%, which verifies the feasibility and practicality of the upper limb action intention recognition method based on the fusion of posture information and visual information.

/pdf/research-on-upper-limb-action-intention-recognition-method-3ca4fxms.pdf

Han Du

Papers

Recognition of Upper Limb Action Intention Based on IMU

Research on Upper Limb Action Intention Recognition Method Based on Fusion of Posture Information and Visual Information

Application of Foot Hallux Contact Force Signal for Assistive Hand Fine Control