Data-based bipartite formation control for multi-agent systems with communication constraints.

This article investigates data-driven distributed bipartite formation issues for discrete-time multi-agent systems with communication constraints. We propose a quantized data-driven distributed bipartite formation control approach based on the plant's quantized and saturated information. Moreover, compared with existing results, we consider both the fixed and switching topologies of multi-agent systems with the cooperative-competitive interactions. We establish a time-varying linear data model for each agent by utilizing the dynamic linearization method. Then, using the incomplete input and output data of each agent and its neighbors, we construct the proposed quantized data-driven distributed bipartite formation control scheme without employing any dynamics information of multi-agent systems. We strictly prove the convergence of the proposed algorithm, where the proposed approach can ensure that the bipartite formation tracking errors converge to the origin, even though the communication topology of multi-agent systems is time-varying switching. Finally, simulation and hardware tests demonstrate the effectiveness of the proposed scheme.

Paper Supercapacitor Developed Using a Manganese Dioxide/Carbon Black Composite and a Water Hyacinth Cellulose Nanofiber-Based Bilayer Separator.

Flexible and green energy storage devices have a wide range of applications in prospective electronics and connected devices. In this study, a new eco-friendly bilayer separator and primary and secondary paper supercapacitors based on manganese dioxide (MnO2)/carbon black (CB) are developed. The bilayer separator is prepared via a two-step fabrication process involving freeze-thawing and nonsolvent-induced phase separation. The prepared bilayer separator exhibits superior porosity of 46%, wettability of 46.5°, and electrolyte uptake of 194% when compared with a Celgard 2320 trilayer separator (39%, 55.58°, and 110%). Moreover, lower bulk resistance yields a higher ionic conductivity of 0.52 mS cm-1 in comparison to 0.22 mS cm-1 for the Celgard separator. Furthermore, the bilayer separator exhibits improved mean efficiency of 0.44% and higher specific discharge capacitance of 13.53%. The anodic and cathodic electrodes are coated on a paper substrate using MnO2/CB and zinc metal-loaded CB composites. The paper supercapacitor demonstrates a high specific capacitance of 34.1 mF cm-2 and energy and power density of 1.70 µWh cm-2 and 204.8 µW cm-2 at 500 µA, respectively. In summary, the concept of an eco-friendly bilayer cellulose separator with paper-based supercapacitors offers an environmentally friendly alternative to traditional energy storage devices.

Adaptive Event-Triggered Bipartite Formation for Multiagent Systems via Reinforcement Learning.

This article investigates the online learning and energy-efficient control issues for nonlinear discrete-time multiagent systems (MASs) with unknown dynamics models and antagonistic interactions. First, a distributed combined measurement error function is formulated using the signed graph theory to transfer the bipartite formation issue into a consensus issue. Then, an enhanced linearization controller model for the controlled MASs is developed by employing dynamic linearization technology. After that, an online learning adaptive event-triggered (ET) actor-critic neural network (AC-NN) framework for the MASs to implement bipartite formation control tasks is proposed by employing the optimized NNs and designed adaptive ET mechanism. Moreover, the convergence of the designed formation control framework is strictly proved by the constructed Lyapunov functions. Finally, simulation and experimental studies further demonstrate the effectiveness of the proposed algorithm.

CFHA-Net: A polyp segmentation method with cross-scale fusion strategy and hybrid attention.

Colorectal cancer is a prevalent disease in modern times, with most cases being caused by polyps. Therefore, the segmentation of polyps has garnered significant attention in the field of medical image segmentation. In recent years, the variant network derived from the U-Net network has demonstrated a good segmentation effect on polyp segmentation challenges. In this paper, a polyp segmentation model, called CFHA-Net, is proposed, that combines a cross-scale feature fusion strategy and a hybrid attention mechanism. Inspired by feature learning, the encoder unit incorporates a cross-scale context fusion (CCF) module that performs cross-layer feature fusion and enhances the feature information of different scales. The skip connection is optimized by proposed triple hybrid attention (THA) module that aggregates spatial and channel attention features from three directions to improve the long-range dependence between features and help identify subsequent polyp lesion boundaries. Additionally, a dense-receptive feature fusion (DFF) module, which combines dense connections and multi-receptive field fusion modules, is added at the bottleneck layer to capture more comprehensive context information. Furthermore, a hybrid pooling (HP) module and a hybrid upsampling (HU) module are proposed to help the segmentation network acquire more contextual features. A series of experiments have been conducted on three typical datasets for polyp segmentation (CVC-ClinicDB, Kvasir-SEG, EndoTect) to evaluate the effectiveness and generalization of the proposed CFHA-Net. The experimental results demonstrate the validity and generalization of the proposed method, with many performance metrics surpassing those of related advanced segmentation networks. Therefore, proposed CFHA-Net could present a promising solution to the challenges of polyp segmentation in medical image analysis. The source code of proposed CFHA-Net is available at https://github.com/CXzhai/CFHA-Net.git.

Advanced Intelligent Control in Robots.

Advanced intelligent control (AIC) is a rapidly evolving and complex field that poses significant challenges [...].

Wave-Net: A lightweight deep network for retinal vessel segmentation from fundus images.

Accurate segmentation of retinal vessels from fundus images is fundamental for the diagnosis of numerous diseases of eye, and an automated vessel segmentation method can effectively help clinicians to make accurate diagnosis for the patients and provide the appropriate treatment schemes. It is important to note that both thick and thin vessels play the key role for disease judgements. Because of complex factors, the precise segmentation of thin vessels is still a great challenge, such as the presence of various lesions, image noise, complex backgrounds and poor contrast in the fundus images. Recently, because of the advantage of context feature representation learning capabilities, deep learning has shown a remarkable segmentation performance on retinal vessels. However, it still has some shortcomings on high-precision retinal vessel extraction due to some factors, such as semantic information loss caused by pooling operations, limited receptive field, etc. To address these problems, this paper proposes a new lightweight segmentation network for precise retinal vessel segmentation, which is called as Wave-Net model on account of the whole shape. To alleviate the influence of semantic information loss problem to thin vessels, to acquire more contexts about micro structures and details, a detail enhancement and denoising block (DED) is proposed to improve the segmentation precision on thin vessels, which replaces the simple skip connections of original U-Net. On the other hand, it could well alleviate the influence of the semantic gap problem. Further, faced with limited receptive field, for multi-scale vessel detection, a multi-scale feature fusion block (MFF) is proposed to fuse cross-scale contexts to achieve higher segmentation accuracy and realize effective processing of local feature maps. Experiments indicate that proposed Wave-Net achieves an excellent performance on retinal vessel segmentation while maintaining a lightweight network design compared to other advanced segmentation methods, and it also has shown a better segmentation ability to thin vessels.

A novel design of a passive variable stiffness soft robotic gripper.

Inspired by the twisting and hanging phenomenon of vines, this paper proposes and designs a passive variable stiffness soft robotic gripper to grasp an object in a simple and robust manner using the principle of jamming. This method has the characteristics of high reliability and good stability, which can achieve soft grasping and rigid load-bearing of the object. Firstly, according to two key issues, the design model of the gripper is proposed, the principle of the proposed gripper is analyzed, and the relationship between the stiffness of the gripper and the stiffness of the object is revealed. Secondly, the model of the robotic gripper is created using a conventional motor drive method, and the grasping process and deformation causes of the gripper are analyzed by using the principle of instability effect and large deformation principle. Finally, the experimental prototype is developed and the feasibility of the design principle and the grasping deformation process of the gripper are verified by gripping experiments.

Event-Triggered Distributed Data-Driven Iterative Learning Bipartite Formation Control for Unknown Nonlinear Multiagent Systems.

In this study, we investigate the event-triggering time-varying trajectory bipartite formation tracking problem for a class of unknown nonaffine nonlinear discrete-time multiagent systems (MASs). We first obtain an equivalent linear data model with a dynamic parameter of each agent by employing the pseudo-partial-derivative technique. Then, we propose an event-triggered distributed model-free adaptive iterative learning bipartite formation control scheme by using the input/output data of MASs without employing either the plant structure or any knowledge of the dynamics. To improve the flexibility and network communication resource utilization, we construct an observer-based event-triggering mechanism with a dead-zone operator. Furthermore, we rigorously prove the convergence of the proposed algorithm, where each agent's time-varying trajectory bipartite formation tracking error is reduced to a small range around zero. Finally, four simulation studies further validate the designed control approach's effectiveness, demonstrating that the proposed scheme is also suitable for the homogeneous MASs to achieve time-varying trajectory bipartite formation tracking.

Effect of velocity and acceleration in joint angle estimation for an EMG-Based upper-limb exoskeleton control.

Most studies on estimating user's joint angles to control upper-limb exoskeleton have focused on using surface electromyogram (sEMG) signals. However, the variations in limb velocity and acceleration can affect the sEMG data and decrease the angle estimation performance in the practical use of the exoskeleton. This paper demonstrated that the variations in elbow angular velocity (EAV) and elbow angular acceleration (EAA) associated with normal use led to a large effect on the elbow joint angle estimation. To minimize this effect, we proposed two methods: (1) collecting sEMG data of multiple EAVs and EAAs as training data and (2) measuring the values of EAV and EAA with a gyroscope. A self-developed upper-limb exoskeleton with pneumatic muscles was used in the online control phase to verify our methods' effectiveness. The predicted elbow angle from the sEMG-angle models which were trained in the offline estimation phase was transferred to control signal of the pneumatic muscles to actuate the exoskeleton to move to the same angle. In the offline estimation phase, the average root mean square error (RMSE) between predicted elbow angle and actual elbow angle was reduced from 22.54° to 10.01° (using method one) and to 6.45° (using method two), respectively; in the online control phase, method two achieved a best control performance (average RMSE = 6.87°). The results showed that using multi-sensor fusion (sEMG sensors and gyroscope) achieved a better estimation performance than using only sEMG sensor, which was helpful to eliminate the velocity and acceleration effect in real-time joint angle estimation for upper-limb exoskeleton control.

Distributed Model-Free Bipartite Consensus Tracking for Unknown Heterogeneous Multi-Agent Systems with Switching Topology.

This paper proposes a distributed model-free adaptive bipartite consensus tracking (DMFABCT) scheme. The proposed scheme is independent of a precise mathematical model, but can achieve both bipartite time-invariant and time-varying trajectory tracking for unknown dynamic discrete-time heterogeneous multi-agent systems (MASs) with switching topology and coopetition networks. The main innovation of this algorithm is to estimate an equivalent dynamic linearization data model by the pseudo partial derivative (PPD) approach, where only the input-output (I/O) data of each agent is required, and the cooperative interactions among agents are investigated. The rigorous proof of the convergent property is given for DMFABCT, which reveals that the trajectories error can be reduced. Finally, three simulations results show that the novel DMFABCT scheme is effective and robust for unknown heterogeneous discrete-time MASs with switching topologies to complete bipartite consensus tracking tasks.

An empirical study on assistive technology supported travel and tourism for the people with dementia.

Purpose: Research shows that people with disabilities have same desire for travel and tourism as others, but many constraints prevent them from independent travelling. Although the disabled tourism market is expanding, still there is lack of empirical research on impact analysis of assistive technology (AT) assisted travel and tourism with focus on the people with dementia (PWD). This study investigates the impacts of AT assisted travel and tourism for the PWD.Methods: The questionnaire-based survey gathered data from 327 PWD. SPSS is used to perform exploratory factor analysis and canonical coefficient analysis for investigating the usability of AT assistance in travel and tourism.Results: Results reveal four motivational (facilitated travel and tourism, cost effectiveness, easier communication and needs compatibility) and three achievements dimensions (improved achievements, improved independence and improved safety). Independent travel is desired by the PWD without any external help and ATs can help them in doing so. The use of AT can further accommodate in choosing the tourism activities, attractions and leisure management. The PWD feel improvements in their achievements through the use of ATs in travel and tourism.Conclusions: AT support, resultantly improves the confidence and self-esteem of the PWD and motivate them towards rehabilitation and independent living.Implications for rehabilitationThis study provides finer empirical understanding of the needs, motivations and achievements of the people with dementia (PWD) who use assistive technologies (ATs) for travel and tourism.Usually PWD have different motivations and accessibility needs, their travel and tourism related behaviours might also differ from the people not identified as having dementia.The ATs (facilitated travel and tourism, cost effectiveness, easier communication and needs compatibility) are the motivational factors for the PWD and should be focused during the development of ATs. These factors will allow PWD to better plan their travel and tourism activities without the help of their caregivers. The AT assisted communication opportunities will help PWD to eliminate their feeling of social isolation from rest of the community.AT supported travel and tourism results into sense of improved achievements, improved independence and improved safety on the part of the PWD. Resultantly, they feel more motivated to do activities, which otherwise they might be reluctant to perform. The sense of safety, independence and achievement does impact PWD psychologically and would help them towards rehabilitation.This study provides pointers for the dementia policy makers related to AT supported travel and tourism based on the real needs of the PWD.

A soft continuum robot, with a large variable-stiffness range, based on jamming.

Inspired by the physiological structure of the hand capable of realizing the continuous change in finger stiffness when grasping objects of different masses, a self-locking soft continuum robot with a large variable-stiffness range based on particle jamming and fibre jamming is proposed in this paper to meet the requirements of it in practical application. In this paper, a variable stiffness range is derived due to the good fluidity and rigidity of the spherical particles and the low elasticity and high toughness of the fibres. Then, an analysis model is established to deduce its self-locking condition, and the deflection angle of self-locking under the influence of external force is about 0.17 rad. The maximum stiffness of the experimental prototype can reach 1223.58 N m-1 due to the limitation of the experimental materials, despite the fact that the theoretical stiffness can be increased infinitely after self-locking. To explain the adaptability of the robot, the adaptive conditions of the soft continuum robot with variable stiffness are deduced. A new evaluation index, the adaptive intensity of the soft continuum robot, is introduced and the adaptability experiments are carried out. In adaptability experiments, the maximum bending angle of the continuum robot reaches 108°. Finally, the adaptability of the soft continuum robot to different geometries is discussed.

Impact evaluation of assistive technology support for the people with dementia.

This study explores important factors of assistive technology (AT) and evaluates their relative impact on AT effectiveness and retention. Questionnaire based survey is used for data collection from 327 people with dementia (PWD). This empirical study uses statistical techniques including exploratory factor analysis for factor identification, linear regression for impact study, Kruskal Wallis H and Mann Whitney U tests for the statistical significant study in terms of demographic and characteristics. The exploratory factor analysis results into 11 factors: operational support, physical support, psychological support, social support, cultural match, reduced external help, affordability, travel help, compatibility, effectiveness and retention. The results reveal that social support, psychological support and travel help and reduced external help strongly impact on AT effectiveness and retention. The users are motivated by socialization support through using AT without any external help. The use of AT during travelling improves confidence of the PWD and helps them psychologically.

Gait Evaluation Using Procrustes and Euclidean Distance Matrix Analysis.

Objective assessment of gait is important in the treatment and rehabilitation of patients with different diseases. In this paper, we propose a gait evaluation system using the Procrustes and Euclidean distance matrix analysis. We design and develop an android app to collect real time synchronous accelerometer and gyroscope data from two inertial measurement unit sensors through Bluetooth connectivity. The data is collected from 12 young (ten for modeling and two for validation) and 20 older subjects. We analyze the data collected from real world for stride, step, stance, and swing gait features. We validate our method with the measurements of gait features. The generalized Procrustes analysis is used to estimate a standard normal mean gait shape (NMGS) for ten young subjects. Each gait feature of both young and older subjects is then converted to find the best match with the NMGS using the ordinary Procrustes analysis. The shape distance between the NMGS and each gait shape is estimated using Riemannian shape distance, Riemannian size-and-shape distance, Procrustes size-and-shape distance, and root-mean-square deviation. A t-test is performed to provide statistical evidence of gait shape differences between young and older gaits. A mean form, which is considered as a standard normal mean gait form (NMGF), and inter-feature distances are estimated from the set of ten young subjects. The form difference is estimated between the NMGF and individual gaits of young and older. The degree of abnormality is then estimated for individual features and the result is plotted to visualize the feature in a gait. Experimental results demonstrate the performance of the proposed method.

An Automatic Gait Feature Extraction Method for Identifying Gait Asymmetry Using Wearable Sensors.

This paper aims to assess the use of Inertial Measurement Unit (IMU) sensors to identify gait asymmetry by extracting automatic gait features. We design and develop an android app to collect real time synchronous IMU data from legs. The results from our method are validated using a Qualisys Motion Capture System. The data are collected from 10 young and 10 older subjects. Each performed a trial in a straight corridor comprising 15 strides of normal walking, a turn around and another 15 strides. We analyse the data for total distance, total time, total velocity, stride, step, cadence, step ratio, stance, and swing. The accuracy of detecting the stride number using the proposed method is 100% for young and 92.67% for older subjects. The accuracy of estimating travelled distance using the proposed method for young subjects is 97.73% and 98.82% for right and left legs; and for the older, is 88.71% and 89.88% for right and left legs. The average travelled distance is 37.77 (95% CI ± 3.57) meters for young subjects and is 22.50 (95% CI ± 2.34) meters for older subjects. The average travelled time for young subjects is 51.85 (95% CI ± 3.08) seconds and for older subjects is 84.02 (95% CI ± 9.98) seconds. The results show that wearable sensors can be used for identifying gait asymmetry without the requirement and expense of an elaborate laboratory setup. This can serve as a tool in diagnosing gait abnormalities in individuals and opens the possibilities for home based self-gait asymmetry assessment.

Assistive technology for people with dementia: an overview and bibliometric study.

BACKGROUND: This study presents an overview of recent research activities in assistive technology (AT) for people with dementia. Bibliometric studies are used to explore breadth and depth of different research areas, yet this method has not yet been fully utilised in AT research for people with dementia. METHODS: The bibliometric method was used for collecting studies related to AT. Based on inclusion/exclusion criteria, the AT studies with a focus on people with dementia are considered. STUDY SCOPE: The study is based on factors such as number of publications, citations per paper, collaborative research output, P-Index, major research and application areas and national dementia strategies. DATA COLLECTION: Data were collected from 2000 to 2014 in AT research. The top 10 countries are selected based on their research outputs. RESULTS: USA emerged as the leading contributor with 503 publications and an annual growth rate of 16%, followed by UK with 399 publications and growth rate of 22%. Germany with 101 publications is on the 6th place, but it has a higher citation rate 16.43% as compared to USA (13.34%). Although all 10 countries show good collaborative research output, Italy, Spain and the Netherlands emerge as top collaborative research contributors with high percentages (84%, 84% and 79%). All the top 10 countries, except Canada, Germany and Spain, have national dementia strategies in place. CONCLUSION: The overall analysis shows that USA and UK are working extensively in AT research for people with dementia. Both these countries also have well established national dementia strategies.

IEEE 802.15.4 Frame Aggregation Enhancement to Provide High Performance in Life-Critical Patient Monitoring Systems.

In wireless body area sensor networks (WBASNs), Quality of Service (QoS) provision for patient monitoring systems in terms of time-critical deadlines, high throughput and energy efficiency is a challenging task. The periodic data from these systems generates a large number of small packets in a short time period which needs an efficient channel access mechanism. The IEEE 802.15.4 standard is recommended for low power devices and widely used for many wireless sensor networks applications. It provides a hybrid channel access mechanism at the Media Access Control (MAC) layer which plays a key role in overall successful transmission in WBASNs. There are many WBASN's MAC protocols that use this hybrid channel access mechanism in variety of sensor applications. However, these protocols are less efficient for patient monitoring systems where life critical data requires limited delay, high throughput and energy efficient communication simultaneously. To address these issues, this paper proposes a frame aggregation scheme by using the aggregated-MAC protocol data unit (A-MPDU) which works with the IEEE 802.15.4 MAC layer. To implement the scheme accurately, we develop a traffic patterns analysis mechanism to understand the requirements of the sensor nodes in patient monitoring systems, then model the channel access to find the performance gap on the basis of obtained requirements, finally propose the design based on the needs of patient monitoring systems. The mechanism is initially verified using numerical modelling and then simulation is conducted using NS2.29, Castalia 3.2 and OMNeT++. The proposed scheme provides the optimal performance considering the required QoS.

Impact of Load Variation on Joint Angle Estimation From Surface EMG Signals.

Many studies use surface electromyogram (sEMG) signals to estimate the joint angle, for control of upper-limb exoskeletons and prostheses. However, several practical factors still affect its clinical applicability. One of these factors is the load variation during daily use. This paper demonstrates that the load variation can have a substantial impact on performance of elbow angle estimation. This impact leads an increase in mean RMSE (Root-Mean-Square Error) from 7.86 ° to 20.44 ° in our experimental test. Therefore, we propose three methods to address this issue: 1) pooling the training data from all loads together to form the pooled training data for the training model; 2) adding the measured load value (force sensor) as an additional input; and 3) developing a two-step hybrid estimation approach based on load and sEMG. Experiments are conducted with five subjects to investigate the feasibility of the proposed three methods. The results show that the mean RMSE is reduced from 20.44 ° to 13.54 ° using method one, 10.47 ° using method two, and 8.48 ° using method three, respectively. Our study indicates that 1) the proposed methods can improve performance and stability on joint angle estimation and 2) sensor fusion (sEMG sensor and force sensor) is an efficient way to resolve the adverse effect of load variation.

Genetic algorithm-based classifiers fusion for multisensor activity recognition of elderly people.

Activity recognition of an elderly person can be used to provide information and intelligent services to health care professionals, carers, elderly people, and their families so that the elderly people can remain at homes independently. This study investigates the use and contribution of wrist-worn multisensors for activity recognition. We found that accelerometers are the most important sensors and heart rate data can be used to boost classification of activities with diverse heart rates. We propose a genetic algorithm-based fusion weight selection (GAFW) approach which utilizes GA to find fusion weights. For all possible classifier combinations and fusion methods, the study shows that 98% of times GAFW can achieve equal or higher accuracy than the best classifier within the group.