The use of technology by seniors with neurocognitive disorders in long-term care: a scoping review.

BACKGROUND: To map the current state of knowledge about the use of technology with seniors with neurocognitive disorders in long-term care to foster interactions, wellness, and stimulation. METHODS: Cumulative Index to Nursing and Allied Health Literature (CINAHL Plus); MEDLINE; PsycINFO; Embase and Web of Science were searched in eligible literature, with no limit of time, to describe the current use of technology by seniors with neurocognitive disorders in long-term care. All types of literature were considered except for theses, editorial, social media. This scoping review was built around the recommendations of Peters et al. (2020 version). Three researchers collaborated on the selection of articles and independently reviewed the papers, based on the eligibility criteria and review questions. RESULTS: The search yielded 3,605 studies, of which 39 were included. Most technology type reported was robotics. Included studies reports different positive effects on the use of such technology such as increase of engagement and positive. CONCLUSION: The study highlights different types and potential benefits of technology for long-term care residents with neurocognitive disorders, emphasizing the crucial need for additional research to refine interventions and their use.

Human manipulation strategy when changing object deformability and task properties.

Robotic literature widely addresses deformable object manipulation, but few studies analyzed human manipulation accounting for different levels of deformability and task properties. We asked participants to grasp and insert rigid and deformable objects into holes with varying tolerances and depths, and we analyzed the grasping behavior, the reaching velocity profile, and completion times. Results indicated that the more deformable the object is, the nearer the grasping point is to the extremity to be inserted. For insertions in the long hole, the selection of the grasping point is a trade-off between task accuracy and the number of re-grasps required to complete the insertion. The compliance of the deformable object facilitates the alignment between the object and the hole. The reaching velocity profile when increasing deformability recalls the one observed when task accuracy and precision decrease. Identifying human strategy allows the implementation of human-inspired high-level reasoning algorithms for robotic manipulation.

Biofabrication of Living Actuators.

The impact of tissue engineering has extended beyond a traditional focus in medicine to the rapidly growing realm of biohybrid robotics. Leveraging living actuators as functional components in machines has been a central focus of this field, generating a range of compelling demonstrations of robots capable of muscle-powered swimming, walking, pumping, gripping, and even computation. In this review, we highlight key advances in fabricating tissue-scale cardiac and skeletal muscle actuators for a range of functional applications. We discuss areas for future growth including scalable manufacturing, integrated feedback control, and predictive modeling and also propose methods for ensuring inclusive and bioethics-focused pedagogy in this emerging discipline. We hope this review motivates the next generation of biomedical engineers to advance rational design and practical use of living machines for applications ranging from telesurgery to manufacturing to on- and off-world exploration.

Electronic Skin: Opportunities and Challenges in Convergence with Machine Learning.

Recent advancements in soft electronic skin (e-skin) have led to the development of human-like devices that reproduce the skin's functions and physical attributes. These devices are being explored for applications in robotic prostheses as well as for collecting biopotentials for disease diagnosis and treatment, as exemplified by biomedical e-skins. More recently, machine learning (ML) has been utilized to enhance device control accuracy and data processing efficiency. The convergence of e-skin technologies with ML is promoting their translation into clinical practice, especially in healthcare. This review highlights the latest developments in ML-reinforced e-skin devices for robotic prostheses and biomedical instrumentations. We first describe technological breakthroughs in state-of-the-art e-skin devices, emphasizing technologies that achieve skin-like properties. We then introduce ML methods adopted for control optimization and pattern recognition, followed by practical applications that converge the two technologies. Lastly, we briefly discuss the challenges this interdisciplinary research encounters in its clinical and industrial transition.

[Research progress on robot-assisted subretinal and intravascular injection surgeries].

Various retinal diseases require subretinal and/or intravascular injections, which are precise and challenging ocular microsurgeries. Robot-assisted surgery is expected to promote surgery precision, visualization, and success rates. This review summarizes recent research progress on robot-assisted surgery for subretinal and intravascular injections, emphasizing effectiveness, safety, and intelligence, and aiming to provide valuable insights for research on the application of surgical robots in the treatment of retinal diseases.

Robots as Mental Health Coaches: A Study of Emotional Responses to Technology-Assisted Stress Management Tasks Using Physiological Signals.

The current study investigated the effectiveness of social robots in facilitating stress management interventions for university students by evaluating their physiological responses. We collected electroencephalogram (EEG) brain activity and Galvanic Skin Responses (GSRs) together with self-reported questionnaires from two groups of students who practiced a deep breathing exercise either with a social robot or a laptop. From GSR signals, we obtained the change in participants' arousal level throughout the intervention, and from the EEG signals, we extracted the change in their emotional valence using the neurometric of Frontal Alpha Asymmetry (FAA). While subjective perceptions of stress and user experience did not differ significantly between the two groups, the physiological signals revealed differences in their emotional responses as evaluated by the arousal-valence model. The Laptop group tended to show a decrease in arousal level which, in some cases, was accompanied by negative valence indicative of boredom or lack of interest. On the other hand, the Robot group displayed two patterns; some demonstrated a decrease in arousal with positive valence indicative of calmness and relaxation, and others showed an increase in arousal together with positive valence interpreted as excitement. These findings provide interesting insights into the impact of social robots as mental well-being coaches on students' emotions particularly in the presence of the novelty effect. Additionally, they provide evidence for the efficacy of physiological signals as an objective and reliable measure of user experience in HRI settings.

Wearable Robot Design Optimization Using Closed-Form Human-Robot Dynamic Interaction Model.

Wearable robots are emerging as a viable and effective solution for assisting and enabling people who suffer from balance and mobility disorders. Virtual prototyping is a powerful tool to design robots, preventing the costly iterative physical prototyping and testing. Design of wearable robots through modelling, however, often involves computationally expensive and error-prone multi-body simulations wrapped in an optimization framework to simulate human-robot-environment interactions. This paper proposes a framework to make the human-robot link segment system statically determinate, allowing for the closed-form inverse dynamics formulation of the link-segment model to be solved directly in order to simulate human-robot dynamic interactions. The paper also uses a technique developed by the authors to estimate the walking ground reactions from reference kinematic data, avoiding the need to measure them. The proposed framework is (a) computationally efficient and (b) transparent and easy to interpret, and (c) eliminates the need for optimization, detailed musculoskeletal modelling and measuring ground reaction forces for normal walking simulations. It is used to optimise the position of hip and ankle joints and the actuator torque-velocity requirements for a seven segments of a lower-limb wearable robot that is attached to the user at the shoes and pelvis. Gait measurements were carried out on six healthy subjects, and the data were used for design optimization and validation. The new technique promises to offer a significant advance in the way in which wearable robots can be designed.

When Trustworthiness Meets Face: Facial Design for Social Robots.

As a technical application in artificial intelligence, a social robot is one of the branches of robotic studies that emphasizes socially communicating and interacting with human beings. Although both robot and behavior research have realized the significance of social robot design for its market success and related emotional benefit to users, the specific design of the eye and mouth shape of a social robot in eliciting trustworthiness has received only limited attention. In order to address this research gap, our study conducted a 2 (eye shape) × 3 (mouth shape) full factorial between-subject experiment. A total of 211 participants were recruited and randomly assigned to the six scenarios in the study. After exposure to the stimuli, perceived trustworthiness and robot attitude were measured accordingly. The results showed that round eyes (vs. narrow eyes) and an upturned-shape mouth or neutral mouth (vs. downturned-shape mouth) for social robots could significantly improve people's trustworthiness and attitude towards social robots. The effect of eye and mouth shape on robot attitude are all mediated by the perceived trustworthiness. Trustworthy human facial features could be applied to the robot's face, eliciting a similar trustworthiness perception and attitude. In addition to empirical contributions to HRI, this finding could shed light on the design practice for a trustworthy-looking social robot.

Treatment with robot-assisted gait trainer Walkbot along with physiotherapy vs. isolated physiotherapy in children and adolescents with cerebral palsy. Experimental study.

BACKGROUND: Improving walking ability is a key objective in the treatment of children and adolescents with cerebral palsy, since it directly affects their activity and participation. In recent years, robotic technology has been implemented in gait treatment, which allows training of longer duration and repetition of the movement. To know the effectiveness of a treatment with the robotic-assisted gait trainer Walkbot combined with physiotherapy compared to the isolated physiotherapy treatment in children and adolescents with cerebral palsy, we carried out a clinical trial. METHODS: 23 participants, were divided into two groups: experimental and control. During 5 weeks, both groups received their physiotherapy sessions scheduled, in addition experimental group received 4 sessions per week of 40 min of robot. An evaluation of the participants was carried out before the intervention, at the end of the intervention, and at follow-up (two months after the end of the intervention). Gait was assessed with the Gross Motor Function Measure-88 dimensions D and E, strength was measured with a hydraulic dynamometer, and range of motion was assessed using the goniometer. A mixed ANOVA was performed when the assumptions of normality and homoscedasticity were met, and a robust mixed ANOVA was performed when these assumptions were not met. Statistical significance was stipulated at p < 0.05. For the effect size, η2 was calculated. RESULTS: Significant differences were found regarding the time x group interaction in the Gross Motor Function Measure-88 in dimension D [η2 = 0.016], in the flexion strength of the left [η2 = 0.128] and right [η2 = 0.142] hips, in the extension strength of the right hip [η2 = 0.035], in the abduction strength of the left hip [η2 = 0.179] and right [η2 = 0.196], in the flexion strength of the left knee [η2 = 0.222] and right [η2 = 0.147], and in the range of motion of left [η2 = 0.071] and right [η2 = 0.053] knee flexion. CONCLUSIONS: Compared to treatments without walking robot, physiotherapy treatment including Walkbot improves standing, muscle strength, and knee range of motion in children and adolescents with cerebral palsy. TRIAL REGISTRATION: ClinicalTrials.gov: NCT04329793. First posted: April 1, 2020.

Bionic e-skin with precise multi-directional droplet sliding sensing for enhanced robotic perception.

Electronic skins with deep and comprehensive liquid information detection are desired to endow intelligent robotic devices with augmented perception and autonomous regulation in common droplet environments. At present, one technical limitation of electronic skins is the inability to perceive the liquid sliding information as realistically as humans and give feedback in time. To this critical challenge, in this work, a self-powered bionic droplet electronic skin is proposed by constructing an ingenious co-layer interlaced electrode network and using an overpass connection method. The bionic skin is used for droplet environment reconnaissance and converts various dynamic droplet sliding behaviors into electrical signals based on triboelectricity. More importantly, the two-dimensional sliding behavior of liquid droplets is comprehensively perceived by the e-skin and visually fed back in real-time on an indicator. Furthermore, the flow direction warning and intelligent closed-loop control of water leakage are also achieved by this e-skin, achieving the effect of human neuromodulation. This strategy compensates for the limitations of e-skin sensing droplets and greatly narrows the gap between artificial e-skins and human skins in perceiving functions.

The effect of robot-assisted versus standard training on motor function following subacute rehabilitation after ischemic stroke - protocol for a randomised controlled trial nested in a prospective cohort (RoboRehab).

BACKGROUND: Body weight unloaded treadmill training has shown limited efficacy in further improving functional capacity after subacute rehabilitation of ischemic stroke patients. Dynamic robot assisted bodyweight unloading is a novel technology that may provide superior training stimuli and continued functional improvements in individuals with residual impairments in the chronic phase after the ischemic insult. The aim of the present study is to investigate the effect of dynamic robot-assisted versus standard training, initiated 6 months post-stroke, on motor function, physical function, fatigue, and quality of life in stroke-affected individuals still suffering from moderate-to-severe disabilities after subacute rehabilitation. METHODS: Stroke-affected individuals with moderate to severe disabilities will be recruited into a prospective cohort with measurements at 3-, 6-, 12- and 18-months post-stroke. A randomised controlled trial (RCT) will be nested in the prospective cohort with measurements pre-intervention (Pre), post-intervention (Post) and at follow-up 6 months following post-intervention testing. The present RCT will be conducted as a multicentre parallel-group superiority of intervention study with assessor-blinding and a stratified block randomisation design. Following pre-intervention testing, participants in the RCT study will be randomised into robot-assisted training (intervention) or standard training (active control). Participants in both groups will train 1:1 with a physiotherapist two times a week for 6 months (groups are matched for time allocated to training). The primary outcome is the between-group difference in change score of Fugl-Meyer Lower Extremity Assessment from pre-post intervention on the intention-to-treat population. A per-protocol analysis will be conducted analysing the differences in change scores of the participants demonstrating acceptable adherence. A priori sample size calculation allowing the detection of the minimally clinically important between-group difference of 6 points in the primary outcome (standard deviation 6 point, α = 5% and ß = 80%) resulted in 34 study participants. Allowing for dropout the study will include 40 participants in total. DISCUSSION: For stroke-affected individuals still suffering from moderate to severe disabilities following subacute standard rehabilitation, training interventions based on dynamic robot-assisted body weight unloading may facilitate an appropriate intensity, volume and task-specificity in training leading to superior functional recovery compared to training without the use of body weight unloading. TRIAL REGISTRATION: ClinicalTrials.gov. NCT06273475. TRIAL STATUS: Recruiting. Trial identifier: NCT06273475. Registry name: ClinicalTrials.gov. Date of registration on ClinicalTrials.gov: 22/02/2024.

Research on control strategy of pneumatic soft bionic robot based on improved CPG.

To achieve the accuracy and anti-interference of the motion control of the soft robot more effectively, the motion control strategy of the pneumatic soft bionic robot based on the improved Central Pattern Generator (CPG) is proposed. According to the structure and motion characteristics of the robot, a two-layer neural network topology model for the robot is constructed by coupling 22 Hopfield neuron nonlinear oscillators. Then, based on the Adaptive Neuro-Fuzzy Inference System (ANFIS), the membership functions are offline learned and trained to construct the CPG-ANFIS-PID motion control strategy for the robot. Through simulation research on the impact of CPG-ANFIS-PID input parameters on the swimming performance of the robot, it is verified that the control strategy can quickly respond to input parameter changes between different swimming modes, and stably output smooth and continuous dynamic position signals, which has certain advantages. Then, the motion performance of the robot prototype is analyzed experimentally and compared with the simulation results. The results show that the CPG-ANFIS-PID motion control strategy can output coupled waveform signals stably, and control the executing mechanisms of the pneumatic soft bionic robot to achieve biological rhythms motion propulsion waveforms, confirming that the control strategy has accuracy and anti-interference characteristics, and enable the robot have certain maneuverability, flexibility, and environmental adaptability. The significance of this work lies in establishing a CPG-ANFIS-PID control strategy applicable to pneumatic soft bionic robot and proposing a rhythmic motion control method applicable to pneumatic soft bionic robot.

Effects of virtual reality-based robot therapy combined with task-oriented therapy on upper limb function and cerebral cortex activation in patients with stroke.

BACKGROUND: This study aimed to investigate the effects of virtual reality (VR)-based robot therapy combined with task-oriented therapy on cerebral cortex activation and upper limb function in patients with stroke. METHODS: This study included 46 patients with hemiplegia within 1 year of stroke onset. Patients were divided into an experimental group (n = 23) and a control group (n = 23) using a computer randomization program. The experimental group received VR-based robot and task-oriented therapies, whereas the control group received only task-oriented therapy. All participants received interventions for 40 minutes per session, 5 times a week, for 8 weeks. For the pre- and post-evaluation of all participants, the Fugl-Meyer Assessment for the upper extremity, manual function test, motor activity log, and Jebsen-Taylor Hand Function Test were used to evaluate changes in upper limb function and motor-evoked potential amplitudes were measured to compare cerebral cortex activation. RESULTS: In comparison to the control group, experimental group demonstrated an improvement in the function of the upper limb (P < .01) and activation of the cerebral cortex (P < .01). CONCLUSION: The combined intervention of VR-based robot and task-oriented therapies is valuable for improving upper limb function and cerebral cortex activation in patients with stroke.

Direct biomechanical manipulation of human gait stability: A systematic review.

People fall more often when their gait stability is reduced. Gait stability can be directly manipulated by exerting forces or moments onto a person, ranging from simple walking sticks to complex wearable robotics. A systematic review of the literature was performed to determine: What is the level of evidence for different types of mechanical manipulations on improving gait stability? The study was registered at PROSPERO (CRD42020180631). Databases Embase, Medline All, Web of Science Core Collection, Cochrane Central Register of Controlled Trials, and Google Scholar were searched. The final search was conducted on the 1st of December, 2022. The included studies contained mechanical devices that influence gait stability for both impaired and non-impaired subjects. Studies performed with prosthetic devices, passive orthoses, and analysing post-training effects were excluded. An adapted NIH quality assessment tool was used to assess the study quality and risk of bias. Studies were grouped based on the type of device, point of application, and direction of forces and moments. For each device type, a best-evidence synthesis was performed to quantify the level of evidence based on the type of validity of the reported outcome measures and the study quality assessment score. Impaired and non-impaired study participants were considered separately. From a total of 4701 papers, 53 were included in our analysis. For impaired subjects, indicative evidence was found for medio-lateral pelvis stabilisation for improving gait stability, while limited evidence was found for hip joint assistance and canes. For non-impaired subjects, moderate evidence was found for medio-lateral pelvis stabilisation and limited evidence for body weight support. For all other device types, either indicative or insufficient evidence was found for improving gait stability. Our findings also highlight the lack of consensus on outcome measures amongst studies of devices focused on manipulating gait.

Bacterial outer membrane vesicle nanorobot.

Autonomous nanorobots represent an advanced tool for precision therapy to improve therapeutic efficacy. However, current nanorobotic designs primarily rely on inorganic materials with compromised biocompatibility and limited biological functions. Here, we introduce enzyme-powered bacterial outer membrane vesicle (OMV) nanorobots. The immobilized urease on the OMV membrane catalyzes the decomposition of bioavailable urea, generating effective propulsion for nanorobots. This OMV nanorobot preserves the unique features of OMVs, including intrinsic biocompatibility, immunogenicity, versatile surface bioengineering for desired biofunctionalities, capability of cargo loading and protection. We present OMV-based nanorobots designed for effective tumor therapy by leveraging the membrane properties of OMVs. These involve surface bioengineering of robotic body with cell-penetrating peptide for tumor targeting and penetration, which is further enhanced by active propulsion of nanorobots. Additionally, OMV nanorobots can effectively safeguard the loaded gene silencing tool, small interfering RNA (siRNA), from enzymatic degradation. Through systematic in vitro and in vivo studies using a rodent model, we demonstrate that these OMV nanorobots substantially enhanced siRNA delivery and immune stimulation, resulting in the utmost effectiveness in tumor suppression when juxtaposed with static groups, particularly evident in the orthotopic bladder tumor model. This OMV nanorobot opens an inspiring avenue to design advanced medical robots with expanded versatility and adaptability, broadening their operation scope in practical biomedical domains.

Magnetic motors in interphases: Motion control and integration in soft robots.

Magnetic motors are a class of out-of-equilibrium particles that exhibit controlled and fast motion overcoming Brownian fluctuations by harnessing external magnetic fields. The advances in this field resulted in motors that have been used for different applications, such as biomedicine or environmental remediation. In this Perspective, an overview of the recent advancements of magnetic motors is provided, with a special focus on controlled motion. This aspect extends from trapping, steering, and guidance to organized motor grouping and degrouping, which is known as swarm control. Further, the integration of magnetic motors in soft robots to actuate their motion is also discussed. Finally, some remarks and perspectives of the field are outlined.

Glove-Net: Enhancing Grasp Classification with Multisensory Data and Deep Learning Approach.

Grasp classification is pivotal for understanding human interactions with objects, with wide-ranging applications in robotics, prosthetics, and rehabilitation. This study introduces a novel methodology utilizing a multisensory data glove to capture intricate grasp dynamics, including finger posture bending angles and fingertip forces. Our dataset comprises data collected from 10 participants engaging in grasp trials with 24 objects using the YCB object set. We evaluate classification performance under three scenarios: utilizing grasp posture alone, utilizing grasp force alone, and combining both modalities. We propose Glove-Net, a hybrid CNN-BiLSTM architecture for classifying grasp patterns within our dataset, aiming to harness the unique advantages offered by both CNNs and BiLSTM networks. This model seamlessly integrates CNNs' spatial feature extraction capabilities with the temporal sequence learning strengths inherent in BiLSTM networks, effectively addressing the intricate dependencies present within our grasping data. Our study includes findings from an extensive ablation study aimed at optimizing model configurations and hyperparameters. We quantify and compare the classification accuracy across these scenarios: CNN achieved 88.09%, 69.38%, and 93.51% testing accuracies for posture-only, force-only, and combined data, respectively. LSTM exhibited accuracies of 86.02%, 70.52%, and 92.19% for the same scenarios. Notably, the hybrid CNN-BiLSTM proposed model demonstrated superior performance with accuracies of 90.83%, 73.12%, and 98.75% across the respective scenarios. Through rigorous numerical experimentation, our results underscore the significance of multimodal grasp classification and highlight the efficacy of the proposed hybrid Glove-Net architectures in leveraging multisensory data for precise grasp recognition. These insights advance understanding of human-machine interaction and hold promise for diverse real-world applications.

Learning Temporal-Spatial Contextual Adaptation for Three-Dimensional Human Pose Estimation.

Three-dimensional human pose estimation focuses on generating 3D pose sequences from 2D videos. It has enormous potential in the fields of human-robot interaction, remote sensing, virtual reality, and computer vision. Existing excellent methods primarily focus on exploring spatial or temporal encoding to achieve 3D pose inference. However, various architectures exploit the independent effects of spatial and temporal cues on 3D pose estimation, while neglecting the spatial-temporal synergistic influence. To address this issue, this paper proposes a novel 3D pose estimation method with a dual-adaptive spatial-temporal former (DASTFormer) and additional supervised training. The DASTFormer contains attention-adaptive (AtA) and pure-adaptive (PuA) modes, which will enhance pose inference from 2D to 3D by adaptively learning spatial-temporal effects, considering both their cooperative and independent influences. In addition, an additional supervised training with batch variance loss is proposed in this work. Different from common training strategy, a two-round parameter update is conducted on the same batch data. Not only can it better explore the potential relationship between spatial-temporal encoding and 3D poses, but it can also alleviate the batch size limitations imposed by graphics cards on transformer-based frameworks. Extensive experimental results show that the proposed method significantly outperforms most state-of-the-art approaches on Human3.6 and HumanEVA datasets.

Leading-edge curvature effect on aerodynamic performance of flapping wings in hover and forward flight.

This study investigates the role of leading-edge (LE) curvature in flapping wing aerodynamics considering hovering and forward flight conditions. A scaled-up robotic model is towed along its longitudinal axis by a rack gear carriage system. The forward velocity of the robotic model is changed by varying the advance ratioJfrom 0 (hovering) to 1.0. The study reveals that the LE curvature has insignificant influence on the cycle-average aerodynamic lift and drag. However, the time-history lift coefficient shows that the curvature can enhance the lift around the middle of downstroke. This enhanced lift is reduced from 5% to 1.2% asJchanged from 0 to 1.0. Further flow examinations reveal that the LE curvature is beneficial by enhancing circulation only at the outboard wing sections. The enhanced outboard circulation is found to emanate from the less stretched leading-edge vortices (LEVs), weakened trailing-edge vortices (TEVs), and the coherent merging of the tip vortices (TVs) with the minor LEVs as observed from the phase-lock planar digital particle image velocimetry measurements. The far-wake observation shows that the LE curvature enhances the vorticity within the TV, helping to reduce the overall flow fluctuations in the far field. These findings can be extended to explain the predominantly straight LE wing shape with a small amount of curvature only observed near the wing tip for flapping fliers with Re from 103to 104.

Experiment-free exoskeleton assistance via learning in simulation.

Exoskeletons have enormous potential to improve human locomotive performance1-3. However, their development and broad dissemination are limited by the requirement for lengthy human tests and handcrafted control laws2. Here we show an experiment-free method to learn a versatile control policy in simulation. Our learning-in-simulation framework leverages dynamics-aware musculoskeletal and exoskeleton models and data-driven reinforcement learning to bridge the gap between simulation and reality without human experiments. The learned controller is deployed on a custom hip exoskeleton that automatically generates assistance across different activities with reduced metabolic rates by 24.3%, 13.1% and 15.4% for walking, running and stair climbing, respectively. Our framework may offer a generalizable and scalable strategy for the rapid development and widespread adoption of a variety of assistive robots for both able-bodied and mobility-impaired individuals.