Human Limb Movement Research Articles

Progress and development trends of lower limb exoskeleton rehabilitation robots

Abstract. Currently, as the aging population intensifies, many elderly people are trapped by lower limb dysfunctions caused by diseases such as stroke sequelae, spinal cord injuries, and bone injuries. Research on lower limb exoskeleton robots has made progress both domestically and internationally. Foreign lower limb exoskeleton robot technology is very mature and widely applied; although starting later, domestic development in this field has been rapid. This paper studies the key technologies of lower limb exoskeleton robots from several aspects: the degrees of freedom in human lower limb movement, the cyclic movement of human gait, structural design, driving methods, and interactive control. With the continuous advancement of technology, lower limb exoskeleton robots will become more intelligent and focus more on personalized services and wearing comfort in future development, aiming to bring more convenience and help to disabled individuals, rehabilitation patients, and groups with special needs.

Human Muscle Inspired Anisotropic and Dynamic Metal Ion-Coordinated Mechanically Robust, Stretchable and Swelling-Resistant Hydrogels for Underwater Motion Sensing and Flexible Supercapacitor Application.

Mechanically robust and anisotropic conductive hydrogels have emerged as crucial components in the field of flexible electronic devices, since they possess high mechanical properties and intelligent sensing capabilities. However, the hydrogels often swell on exposure to aqueous medium because of their hydrophilicity, which compromises their mechanical properties. Additionally, the hydrogels' isotropic polymeric networks demonstrate isotropic ion transport, which significantly diminishes the sensing capabilities of electrical devices based on hydrogels. These factors greatly limit their use in flexible and wearable sensors. In this study, we have developed poly(acrylamide-co-maleic acid-co-butyl acrylate) based anisotropic hydrogels by prestretching and drying, followed by ionic cross-linking to fix the alignment. The anisotropic arrangement of the polymer network resulted in significant improvements in mechanical performance and electrical conductivity along the prestretching direction. This anisotropic hydrogel combines hydrophobic and metal ion-ligand interactions, enhancing the maximum tensile strength up to 11 MPa along the prestretching direction, about 3 times higher than in the perpendicular direction. The optimized 200% prestretched hydrogel exhibited high tensile strength (7 MPa), flexibility (fracture strain 370%), high toughness (16 MJ m-3) and antiswelling behavior in water (equilibrium swelling ratio 2% after 15 days). alongside higher conductivity (3 times higher) and strain sensing ability (4 times higher gauge factor) along the prestretching direction. The hydrogel demonstrated efficient and stable underwater sensing for underwater communication and to monitor human limb position and movement. The anisotropic hydrogel electrolyte-based flexible supercapacitor exhibited 117 Fg-1 specific capacitance at 0.5 Ag-1, and maximum energy density 5.85 Whkg-1, significantly higher than the corresponding values for the isotropic hydrogel-based device (88 F g-1 and 4.4 Whkg-1, respectively). This hydrogel mimics the structural design of unidirectionally oriented muscle fibers, showing better direction dependent functional properties than the corresponding isotropic hydrogel. The anti-swelling ability and retention of mechanical and conductive properties of these hydrogels in aqueous environment suggest long-term usage capability of these functional materials.

Multi-branch deep learning neural network prediction model for the development of angular biosensors based on sEMG.

Human gait motion intention recognition is very important for the lower extremity exoskeleton robot to accurately synchronize and respond to the user's natural motion. And motion intention recognition is generally performed through sEMG. Deep learning neural networks perform well in dealing with high-dimensional data and nonlinear relationships such as sEMG, but different deep learning neural networks have their own advantages in dealing with different types of data. Therefore, a multi-branch deep learning neural network, which enables different neural networks to process different feature items, could achieve more accurate and efficient motion intention recognition. The purpose of this study is to 1) Establish a multi-branch deep learning neural network model to achieve accurate gait recognition and effective estimation of joint angles. 2) Quantify the performance of the multi-branch deep learning neural network model in gait recognition and joint angle prediction using sEMG. This study involved the collection of sEMG and plantar pressure data during walking in human subjects. Firstly, the collected signals are filtered and denoised to ensure the quality and reliability of the data. Calculate the time domain features and the frequency domain features to capture the key information of gait. Then, using the sensitivity difference of different structural neural networks to different feature data, a multi-branch deep learning neural network model is developed, in which the extracted features are used as the input of the model. The output of the model includes gait cycle and joint angle, so as to realize the accurate recognition of human gait and the effective estimation of joint angle. The results show that the proposed method has high accuracy in identifying human gait and estimating joint angles. The multi-branch neural network model successfully integrates time-domain and frequency-domain features and provides reliable prediction of gait cycle and joint angle. The highest accuracy of gait recognition is 95.42%, the lowest is 90.11%, and the average is 92.16%. The average error of joint angle estimation is 3.19. This study designed a human walking gait recognition and joint angle prediction model to achieve accurate human lower limb motion intention recognition.The model can be integrated into the sEMG sensor to design a angular biosensors, which can predict the human joint angle in real time.

Multiphase soft metal enabled high-performance fabric-based wearable energy harvesting

Highly efficient and flexible power sources have always been the focus and goal in the field of wearable electronics. However, the existing wearable power sources are limited by their large size, high weight, electrolyte leakage, and poor mechanical compliance, which seriously hinders their practical applications. Herein, we propose a novel strategy to achieve and demonstrate a fabric-based high-performance wearable triboelectric nanogenerator (TENG) structure. The metals of gallium (Ga), bismuth (Bi), indium (In), and tin (Sn) are mixed according to specific mass ratios, and then they are transformed in liquid phase to form room-temperature multiphase soft metals of GaBiInSn. The material exhibits both metallic and fluidic properties, and possesses a characteristic of multiphase structure. The GaBiInSn material is directly deposited between polytetrafluoroethylene (PTFE) layers and nonwoven fabrics to establish multi-level conductive and frictional interfaces, creating charge transfer and solid-liquid hybrid dielectric layers. Therefore, the thin film-type triboelectric nanogenerators with a structure of PTFE/GaBiInSn/nonwoven fabric (LM-P-TENGs) is manufactured. The LM-P-TENGs can be integrated onto fabrics without compromising their breathability, comfortableness, and flexibility. Particularly, LM-P-TENGs efficiently convert the mechanical energy of human limb movements into sustainable electrical energy output. Under the human limb mechanical triggering conditions, LM-P-TENG achieves a champion specific open-circuit voltage up to 900 V, peak short-circuit current density of 43.3 mA/m2, and high power density of 12.56 W/m2. This work demonstrates the application potential of room-temperature multiphase soft materials in flexible wearable power sources, while introducing a novel power supply mode for wearable electronics. Additionally, the LM-P-TENGs also present applications in self-powered wearables, medical biosensing systems, human-machine interaction systems, near-eye display systems, etc. for the flexible electronics.

Integration of multiscale fusion of residual neural network with 2-D gramian angular fields for lower limb movement recognition based on multi-channel sEMG signals

The human lower limb movements recognition (LLMR) plays a pivotal role in active lower limb exoskeleton robots. Employing surface electromyography (sEMG) signals for LLMR allows for the convenient, rapid and stable capture of signal variations, facilitating efficient identification of lower limb motion patterns. However, current sEMG-based LLMR methods face challenges such as incomplete feature extraction, limited contextual information and restricted feature extraction scales during feature extraction. This paper proposed a LLMR method based on Gramian Angular Fields (GAF) and multiscale fusion of Residual Neural Network (MS-ResNet). The denoised sEMG time series was transformed into Gramian Angular Difference Field (GADF) matrix based on GAF. The MS-ResNet model, incorporating ResNet and multiscale feature fusion concepts, was proposed to comprehensively capture global and local information through different-scale feature extraction and fusion, so as to improve recognition performance. sEMG signals from 11 muscles of the preferred leg of 15 healthy subjects were recorded during six common lower limb movements. Experimental analysis investigated the impact of the convolutional kernel size (k × k) in Stream 2 of MS-ResNet and the number of muscles involved on recognition performance. The study revealed that selecting k as 13, coupled with 11 muscles, yielded optimal model performance with the average cross-individual recognition accuracy reaching 97.62 %, demonstrating the model’s efficiency in LLMR. This method could provide a viable solution for developing more efficient and reliable LLMR systems, applicable to lower limb exoskeleton robots and intelligent prosthetics.

High-Performance Sensing Platform Based on Morphology/Lattice Collaborative Control of Femtosecond-Laser-Induced MXene-Composited Graphene.

Flexible sensors based on laser-induced graphene (LIG) are widely used in wearable personal devices, with the morphology and lattice arrangement of LIG the key factors affecting their performance in various applications. In this study, femtosecond-laser-induced MXene-composited graphene (LIMG) is used to improve the electrical conductivity of graphene by incorporating MXene, a 2D material with a high concentration of free electrons, into the LIG structure. By combining pump-probe detection, laser-induced breakdown spectroscopy (LIBS), and density functional theory (DFT) calculations, the morphogenesis and lattice structuring principles of LIMG is explored, with the results indicating that MXene materials are successfully embedded in the graphene lattice, altering both their morphology and electrical properties. The structural sparsity and electrical conductivity of LIMG composites (up to 3187 Sm-1) are significantly enhanced compared to those of LIG. Based on these findings, LIMG has been used in wearable electronics. LIMG electrodes are used to detect uric acid, with a minimum detection limit of 2.48µM. Additionally, LIMG-based pressure and bending sensors have been successfully used to monitor human limb movement and pulse. The direct in situ femtosecond laser patterning synthesis of LIMG has significant implications for developing flexible wearable electronic sensors.

Ultra-Stretchable Composite Organohydrogels Polymerized Based on MXene@Tannic Acid-Ag Autocatalytic System for Highly Sensitive Wearable Sensors.

Conductive hydrogels have attracted widespread attention in the fields of biomedicine and health monitoring. However, their practical application is severely hindered by the lengthy and energy-intensive polymerization process and weak mechanical properties. Here, a rapid polymerization method of polyacrylic acid/gelatin double-network organohydrogel is designed by integrating tannic acid (TA) and Ag nanoparticles on conductive MXene nanosheets as catalyst in a binary solvent of water and glycerol, requiring no external energy input. The synergistic effect of TA and Ag NPs maintains the dynamic redox activity of phenol and quinone within the system, enhancing the efficiency of ammonium persulfate to generate radicals, leading to polymerization within 10min. Also, ternary composite MXene@TA-Ag can act as conductive agents, enhanced fillers, adhesion promoters, and antibacterial agents of organohydrogels, granting them excellent multi-functionality. The organohydrogels exhibit excellent stretchability (1740%) and high tensile strength (184kPa). The strain sensors based on the organohydrogels exhibit ultrahigh sensitivity (GF=3.86), low detection limit (0.1%), and excellent stability (>1000 cycles, >7 days). These sensors can monitor the human limb movements, respiratory and vocal cord vibration, as well as various levels of arteries. Therefore, this organohydrogel holds potential for applications in fields such as human health monitoring and speech recognition.

Numerical simulation of response behavior and damage regularity of human lower limbs under axial impact load

Abstract Axial impacts to the lower extremities of personnel in events such as explosions and falls on the underside of vehicles, which are extremely hazardous to personnel, may result in serious injuries. Most of the current studies on human lower limb injuries from axial loading have not considered the restraining effect of the knee joint. In this paper, based on the THUMS human finite element model, the failure parameters of the tibia, calcaneus, and talus of the lower limb are added and verified. On this basis, the response behavior of the lower limbs of personnel under different axial loads is investigated, and the damage law is further analyzed and studied. It is found that the motion of human lower limbs under axial impact is divided into two phases: the axial motion phase and the rotation phase around the knee joint, and the lower limbs are mainly subjected to compression and bending loads, respectively. The duration of the axial motion phase is related to the impact velocity and the rate of change of velocity and affects the magnitude of tibial force.

Improved multi-layer wavelet transform and blind source separation based ECG artifacts removal algorithm from the sEMG signal: in the case of upper limbs.

Introduction: Surface electromyogram (sEMG) signals have been widely used in human upper limb force estimation and motion intention recognition. However, the electrocardiogram(ECG) artifact generated by the beating of the heart is a major factor that reduces the quality of the EMG signal when recording the sEMG signal from the muscle close to the heart. sEMG signals contaminated by ECG artifacts are difficult to be understood correctly. The objective of this paper is to effectively remove ECG artifacts from sEMG signals by a novel method. Methods: In this paper, sEMG and ECG signals of the biceps brachii, brachialis, and triceps muscle of the human upper limb will be collected respectively. Firstly, an improved multi-layer wavelet transform algorithm is used to preprocess the raw sEMG signal to remove the background noise and power frequency interference in the raw signal. Then, based on the theory of blind source separation analysis, an improved Fast-ICA algorithm was constructed to separate the denoising signals. Finally, an ECG discrimination algorithm was used to find and eliminate ECG signals in sEMG signals. This method consists of the following steps: 1) Acquisition of raw sEMG and ECG signals; 2) Decoupling the raw sEMG signal; 3) Fast-ICA-based signal component separation; 4) ECG artifact recognition and elimination. Results and discussion: The experimental results show that our method has a good effect on removing ECG artifacts from contaminated EMG signals. It can further improve the quality of EMG signals, which is of great significance for improving the accuracy of force estimation and motion intention recognition tasks. Compared with other state-of-the-art methods, our method can also provide the guiding significance for other biological signals.

A versatile surface micro structure design strategy for porous-based pressure sensors to enhance electromechanical performance

Pressure sensors based on porous structures offer comfortable user experiences and can adapt to the flexible movement of human limbs, which makes them attractive options for human–computer interaction and health monitoring. However, ascribed to the limitation of porous surfaces, foam-based pressure sensors are difficult to achieve accurate electric responses and stable signal output, limiting their great potential in wide application fields. Herein, a surface structure design strategy is proposed for improving the performance of porous foam-based pressure sensors. The coupling of microstructures and the collaboration of dual sensing mechanisms endow the sensor with high performance, achieving a ∼ 12 times signal fluctuation stability when compared to the sensor without any surface design. Even when working under complex vibration conditions, the developed sensor can also maintain a ∼ 7 times more stable output. An intelligent insole based on the algorithm model of a sensor array and multi-layer perceptron was developed for the diagnosis of human foot arch health. The sensors with surface structure could capture and differentiate signals more accurately, which results in a diagnostic accuracy of 99.75 % for the smart insole. The strategy for enhancement of sensor performance proposed in this work can be applied to foam sensors with multiple material systems, which is expected to broaden the potential application of porous flexible devices in fields like the Internet of Things, medical monitoring and brain-computer interface.

Implementation of intelligent healthcare image detection system based on OpenCV

As an interface between image processing technology and the real world, image detection can achieve real-time capture of information such as images and movements. It has wide applications in fields such as medical rehabilitation, military simulation, and intelligent recognition. This article aims to achieve autonomous medical detection and assist doctors in diagnosis. Based on a detailed analysis of the principles of image detection technology and the current development status of image detection technology, a comprehensive medical detection system solution is designed. Addressing issues such as the tension of hospital registration queues, lengthy doctor-patient consultations, lack of auxiliary diagnostic functions, and non-visualizable diagnosis and treatment results, this study utilizes literature analysis and modeling to simulate the collection and intelligent analysis of surface images of the human body and limb movements. This enables the realization of functions such as image detection, image capture, image transmission, image processing, and medical feedback. This article has completed the design of an intelligent medical system, solving the problem of low medical diagnosis efficiency. At the same time, it has been found that the clarity of infrared thermal imaging images is not high, which affects the accuracy of collecting human surface images and limb movements. The processing of infrared thermal imaging images has become a direction that needs further research.

Research on a new cable-driven lower limb rehabilitation robot with bilateral coordination control

In this paper, a new type of cable-driven lower limb rehabilitation robot (CDLLRR) based on bilateral control is proposed. Compared with the traditional rehabilitation robot, it has multiple cooperative remote rehabilitation modes of patient-therapist interaction with better safety, mechanical compliance and adaptability. Firstly, according to the characteristics of gait movement of human lower limbs and the performance requirements of the robot in therapy and rehabilitation, the overall structure, main components and assembly arrangement of the robot are designed in detail. Secondly, in order to ensure that the rehabilitation robot has excellent force transparency and system stability while still maintaining a certain position tracking, according to the performance measurement index based on the introduced bilateral control strategy, this paper proposes an improved PD controller based on Anderson method (IPDAM). Finally, the feasibility of the proposed CDLLRR and the effectiveness of the IPDAM are verified by simulation experiments.

Continuous Locomotion Mode and Task Identification for an Assistive Exoskeleton Based on Neuromuscular-Mechanical Fusion.

Human walking parameters exhibit significant variability depending on the terrain, speed, and load. Assistive exoskeletons currently focus on the recognition of locomotion terrain, ignoring the identification of locomotion tasks, which are also essential for control strategies. The aim of this study was to develop an interface for locomotion mode and task identification based on a neuromuscular-mechanical fusion algorithm. The modes of level and incline and tasks of speed and load were explored, and seven able-bodied participants were recruited. A continuous stream of assistive decisions supporting timely exoskeleton control was achieved according to the classification of locomotion. We investigated the optimal algorithm, feature set, window increment, window length, and robustness for precise identification and synchronization between exoskeleton assistive force and human limb movements (human-machine collaboration). The best recognition results were obtained when using a support vector machine, a root mean square/waveform length/acceleration feature set, a window length of 170, and a window increment of 20. The average identification accuracy reached 98.7% ± 1.3%. These results suggest that the surface electromyography-acceleration can be effectively used for locomotion mode and task identification. This study contributes to the development of locomotion mode and task recognition as well as exoskeleton control for seamless transitions.

Design of human lower limb motion data acquisition system based on multi-sensor

In order to protect the health of older people, a wearable lower extremity motion data acquisition system was designed. With STM32F103C8T6 as the core, the system uses an inertial attitude sensor to collect lower limb motion attitude information. In view of the large difference in plantar pressure data of different fall types, the distributed flexible film plantar pressure sensor is used to collect plantar pressure information as an auxiliary discrimination means, which can accurately distinguish the fall behavior from other behaviors, and carry out data transmission through the wireless module. The device is small in size, light in weight, low in power consumption, and wearable, which is easy to apply in the field of health monitoring, and can also be used as a reference for the implementation of corresponding protection strategies for active lower limb prosthetics.

Dynamic Model of Lower Limb Motion in the Sagittal Plane during the Gait Cycle

Context: This work presents the development of a dynamic model for human lower limb motion in the sagittal plane during the gait cycle. The primary objective of this model is to serve as a powerful tool for the design of rehabilitation and assistive devices, such as exoskeletons, prostheses, and orthoses. It achieves this by facilitating the estimation of joint torques, the detailed analysis of kinematic variables, optimal actuator selection, and the exploration of advanced control techniques. Method: The dynamic model consists of two primary components: (1) the plant model and (2) a closed-loop controller. The plant model represents the forward dynamics of human gait and is based on a multi-mass pendulum composed of three segments of the lower limb (thigh, lower leg, and foot) and three joints (hip, knee, and ankle). It is analyzed using the Euler-Lagrange formulation and the nonlinear second-order differential equations are implemented in MATLAB’s Simulink. To reproduce reference human gait trajectories and simulate the functioning of the neuromusculoskeletal system and the central nervous system, a closed-loop PID controller is incorporated into the plant model. It is noteworthy that the scope of this dynamic model is specifically confined to the sagittal plane. Results: The dynamic model is evaluated in terms of angular displacement tracking using the relative maximum error (RME) and the root mean square error (RMSE) for reference trajectories of healthy adult male human gait as reported in the literature. The model demonstrates tracking with errors below 2.2 [°] in magnitude and 3,5% for all three considered segments (thigh, lower leg, and foot). Conclusions: The quantitative results show that the dynamic model developed in this work is reliable and allows for a precise reproduction of human gait trajectories.

A Potential-Real-Time Thigh Orientation Prediction Method Based on Two Shanks-Mounted IMUs and Its Clinical Application

The detection and evaluation of gait kinematics is vital for patient diagnosis and rehabilitation. Aiming at limitations of commonly used optical capture and wearable sensing systems in clinical applications, this paper proposes a thigh attitude angle prediction method based on the hip error tolerance from the kinematic data of two shank-mounted IMUs (Inertial Measurement Unit). The novelties of the proposed method are summarized as follows: i) It develops a parallel approach to regress variation of hip error tolerance for different subjects. This parallel approach, by simultaneously deconstructing the shank kinematics data via different regression algorithm including support vector machine, boosting tree, and stepwise linearity regression, is able to well accommodate the characteristics of both health subjects and patients. ii) It develops some evaluation indices based on gait symmetry, consistency, and activity to fully evaluate the human lower limbs motion performance in gait by only two IMUs. The effectiveness of the proposed method is verified by the experimental results among 8 healthy subjects and 16 cerebral infarction patients. For the healthy subjects, the estimated error of thigh prediction compared with Xsens and Vicon are <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3.3 \pm {0.3^ \circ }$</tex-math> </inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3.5 \pm {0.7^ \circ }$</tex-math> </inline-formula> , respectively. For the patients, the estimated error compared with Xsens-measured angle is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4.8 \pm {1.7^ \circ }$</tex-math> </inline-formula> . Its broad significance in actual intelligent healthcare and robotics-assisted rehabilitation is three-fold: First, it is a recursive real-time method as it only needs data from the previous gait cycle to predict the thigh angle. Second, its accuracy meets the actual clinical needs. Third, it is a low-cost method that only needs two IMUs and has high potentials of clinical applications. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Gait is of great significance to quantify the degree of movement disorders in clinical practice, and the existing equipment is rarely able to meet the needs of full dimension, low cost, and low place constraints in clinical practice. This paper presents an innovative gait kinematic prediction method, which only uses two IMUs attached to the shanks to predict the orientation of thigh. Compared with related works, the proposed methods have achieved high-precision, real-time and low-cost acquisition. This paper is inspired by the problems of large number, high motion interference, and high cost of wearable gait measurement devices in clinical practice. The proposed method have potential to be integrated into exoskeletons and medical walking AIDS, which could greatly improve the comfort and control precision of the wearable system.

A stretchable conductive elastomer sensor with self-healing and highly linear strain for human movement detection and pressure response.

Expanding the detection information of wearable smart devices in applications has practical implications for their use in daily life and healthcare. Damage and breakage caused by mechanical injuries and continuous use are unavoidable for polymer matrices so self-healing properties are expected to be conferred on flexible sensors to extend their life and durability. In addition, a good linearity of relative resistance change vs. strain (gauge factor, GF) facilitates the streamlined conversion of electrical signals to 3D information of human motion, whereas existing works on sensors neglect the quantitative analysis of signals. This letter reports a self-healable flexible electronic sensor based on hydrogen bonding and electrostatic interaction between maleic acid-grafted natural rubber (MNR), polyaniline (PANI), and phytic acid (PA). MNR is the flexible matrix and the template for aniline (ANI) polymerization, and PA acts as the dopant and crosslinking agent. The MNR-PANI-PA sensor shows easy self-healing at room temperature, enhanced mechanical behaviour (∼2.5 MPa, 1000% strain), and excellent linearity (GF of 13.8 over 250% strain and GF of 32.0 over 250-100% strain). Due to the highly linear relationship between ΔR/R and bending angle, the electrical signals of human limb movement can output relevant information on bending angle and frequency. By constructing a sensing array, changes in the position and magnitude of applied pressure could also be detected in real-time. Based on these advantages, the MNR-PANI-PA composite sensor is expected to have potential applications in health monitoring, body motion detection, and electronic skins.

Разработка системы управления и позиционирования узлов мехатронного реабилитационного комплекса

A high pace of modern medical technologies development in the world requires the introduction of intelligent robotic equipment into Russian practice, which allows us to take the healthcare sector to a new level. Due to the progression of diseases of the patients’ musculoskeletal system and an increase in spinal injuries, rehabilitation equipment is becoming increasingly important. In addition, the development of domestic robotic rehabilitation complexes that match or exceed the functionality of foreign analogues is currently required. A fairly new area of modern medicine is a clinical analysis of biomechanical parameters and study of gait pathology using biomechanical models. Currently, to build parametric models, motion capture methods are being developed that make it possible to reconstruct and visualize the movement of human limbs, as well as to estimate various dynamic quantities, for example, motor forces or ground reaction force. The results obtained are used in rehabilitation complexes, video games, sports simulators, etc., where input data are user parameters recorded in real time. The results obtained allow us to conclude that it is necessary to further improve the characteristics of the rehabilitation complex by making changes to the mechanical design of the complex, as well as adjusting the mathematical model of the control system with additional calculations and modeling of the system structure. The developed experimental model of a mechatronic rehabilitation complex is based on the principles of subordinate regulation by actuators using PD regulators. And the desired coordinates of the movement of a person’s leg, obtained using a motion capture complex, are used as a driving signal for the control system.

Human Lower Limb Motion Intention Recognition for Exoskeletons: A Review

Human Lower Limb Motion Intention Recognition for Exoskeletons: A Review

Sensor Fusion-Based Anthropomorphic Control of a Robotic Arm.

The main goal of this research is to develop a highly advanced anthropomorphic control system utilizing multiple sensor technologies to achieve precise control of a robotic arm. Combining Kinect and IMU sensors, together with a data glove, we aim to create a multimodal sensor system for capturing rich information of human upper body movements. Specifically, the four angles of upper limb joints are collected using the Kinect sensor and IMU sensor. In order to improve the accuracy and stability of motion tracking, we use the Kalman filter method to fuse the Kinect and IMU data. In addition, we introduce data glove technology to collect the angle information of the wrist and fingers in seven different directions. The integration and fusion of multiple sensors provides us with full control over the robotic arm, giving it flexibility with 11 degrees of freedom. We successfully achieved a variety of anthropomorphic movements, including shoulder flexion, abduction, rotation, elbow flexion, and fine movements of the wrist and fingers. Most importantly, our experimental results demonstrate that the anthropomorphic control system we developed is highly accurate, real-time, and operable. In summary, the contribution of this study lies in the creation of a multimodal sensor system capable of capturing and precisely controlling human upper limb movements, which provides a solid foundation for the future development of anthropomorphic control technologies. This technology has a wide range of application prospects and can be used for rehabilitation in the medical field, robot collaboration in industrial automation, and immersive experience in virtual reality environments.