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.

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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.

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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


Motion recognition provides movement information for people with physical dysfunction, the elderly and motion-sensing games production, and is important for accurate recognition of human motion. We employed three classical machine learning algorithms and three deep learning algorithm models for motion recognition, namely Random Forests (RF), K-Nearest Neighbors (KNN) and Decision Tree (DT) and Dynamic Neural Network (DNN), Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). Compared with the Inertial Measurement Unit (IMU) worn on seven parts of body. Overall, the difference in performance among the three classical machine learning algorithms in this study was insignificant. The RF algorithm model performed best, having achieved a recognition rate of 96.67%, followed by the KNN algorithm model with an optimal recognition rate of 95.31% and the DT algorithm with an optimal recognition rate of 94.85%. The performance difference among deep learning algorithm models was significant. The DNN algorithm model performed best, having achieved a recognition rate of 97.71%. Our study validated the feasibility of using multidimensional data for motion recognition and demonstrated that the optimal wearing part for distinguishing daily activities based on multidimensional sensing data was the waist. In terms of algorithms, deep learning algorithms based on multi-dimensional sensors performed better, and tree-structured models still have better performance in traditional machine learning algorithms. The results indicated that IMU combined with deep learning algorithms can effectively recognize actions and provided a promising basis for a wider range of applications in the field of motion recognition.


Research on motion recognition based on multi-dimensional sensing data and deep learning algorithms

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

Pudong Lu

Papers

Recognition of Upper Limb Action Intention Based on IMU