Mobile Arm Research Articles

Testing Digital Methods of Patient-Reported Outcomes Data Collection: Prospective Cluster Randomized Trial to Test SMS Text Messaging and Mobile Surveys

BackgroundHealth care delivery continues to evolve, with an effort being made to create patient-centered care models using patient-reported outcomes (PROs) data. Collecting PROs has remained challenging and an expanding landscape of digital health offers a variety of methods to engage patients.ObjectiveThe aim of this study is to prospectively investigate two common methods of remote PRO data collection. The study sought to compare response and engagement rates for bidirectional SMS text messaging and mobile surveys following orthopedic surgery.MethodsThe study was a prospective, block randomized trial of adults undergoing elective orthopedic procedures over 6 weeks. The primary objective was to determine if the method of digital patient engagement would impact response and completion rates. The primary outcome was response rate and total completion of PRO questionnaires.ResultsA total of 127 participants were block randomized into receiving a mobile survey (n=63) delivered as a hyperlink or responding to the same questions through an automated bidirectional SMS text messaging system (n=64). Gender, age, number of comorbidities, and opioid prescriptions were similar across messaging arms. Patients receiving the mobile survey were more likely to have had a knee-related surgery (n=50, 83.3% vs n=40, 62.5%; P=.02) but less likely to have had an invasive procedure (n=26, 41.3% vs n=39, 60.9%; P=.03). Overall engagement over the immediate postoperative period was similar. Prolonged engagement for patients taking opioids past postoperative day 4 was higher in the mobile survey arm at day 7 (18/19, 94.7% vs 9/16, 56.3%). Patients with more invasive procedures showed a trend toward being responsive at day 4 as compared to not responding (n=41, 59.4% vs n=24, 41.4%; P=.05).ConclusionsAs mobile patient engagement becomes more common in health care, testing the various options to engage patients to gather data is crucial to inform future care and research. We found that bidirectional SMS text messaging and mobile surveys were comparable in response and engagement rates; however, mobile surveys may trend toward higher response rates over longer periods of time.Trial RegistrationClinicalTrials.gov NCT03532256; https://clinicaltrials.gov/ct2/show/NCT03532256

A Brain-Computer Interface for Teleoperation of a Semiautonomous Mobile Robotic Assistive System Using SLAM

The proposed assistive hybrid brain-computer interface (BCI) semiautonomous mobile robotic arm demonstrates a design that is (1) adaptable by observing environmental changes with sensors and deploying alternate solutions and (2) versatile by receiving commands from the user’s brainwave signals through a noninvasive electroencephalogram cap. Composed of three integrated subsystems, a hybrid BCI controller, an omnidirectional mobile base, and a robotic arm, the proposed robot has commands mapped to the user’s brainwaves related to a set of specific physical or mental tasks. The implementation of sensors and the camera systems enable both the mobile base and the arm to be semiautonomous. The mobile base’s SLAM algorithm has obstacle avoidance capability and path planning to assist the robot maneuver safely. The robot arm calculates and deploys the necessary joint movement to pick up or drop off a desired object selected by the user via a brainwave controlled cursor on a camera feed. Validation, testing, and implementation of the subsystems were conducted using Gazebo. Communication between the BCI controller and the subsystems is tested independently. A loop of prerecorded brainwave data related to each specific task is used to ensure that the mobile base command is executed; the same prerecorded file is used to move the robot arm cursor and initiate a pick-up or drop-off action. A final system test is conducted where the BCI controller input moves the cursor and selects a goal point. Successful virtual demonstrations of the assistive robotic arm show the feasibility of restoring movement capability and autonomy for a disabled user.

Harvesting Brain Signal Using Machine Learning Methods

Abstract Brain computer interface (BCI) systems are developed in the biomedical engineering fields to increase the quality of life among patients with paralysis and neurological conditions. The development of a six class BCI controller to operate a semi-autonomous mobile robotic arm is presented. The controller uses the following mental tasks: imagined left/right hand squeeze, imagined left/right foot tap, rest, and a physical jaw clench. To design a controller, the locations of active electrodes are verified, and an appropriate machine learning algorithm is determined. Three subjects, ages ranging between 22 and 27, participated in five sessions of motor imagery experiments to record their brainwaves. These recordings were analyzed using event related potential (ERP) plots and topographical maps to determine active electrodes. bcilab was used to train two, three, five, and six class BCI controllers using linear discriminant analysis (LDA) and relevance vector machine (RVM) machine learning methods. The subjects' data were used to compare the two-method's performance in terms of error rate percentage. While a two class BCI controller showed the same accuracy for both methods, the three and five class BCI controllers showed the RVM approach having a higher accuracy than the LDA approach. For the five-class controller, error rate percentage was 33.3% for LDA and 29.2% for RVM. The six class BCI controller error rate percentage for both LDA and RVM was 34.5%. While the percentage values are the same, RVM was chosen as the desired machine learning algorithm based on the trend seen in the three and five class controller performances.

Effectiveness of Mobile Call Reminders and Health Information Booklet to Improve Postnatal Blood Glucose Monitoring among Mothers with Gestational Diabetes Mellitus Receiving Care from a Tertiary Health Centre, Puducherry - A Randomized Controlled Trial.

Background:In India, around 10% of mothers with gestational diabetes mellitus (GDM) develop diabetes within months after delivery. But only 29% of them undergo blood glucose testing in the postnatal period. Our study aimed to compare the proportion of mothers with GDM who got postnatal blood glucose checked at 8 weeks among mothers who received health education booklets and mobile reminders.Methods:We conducted a randomised controlled trial among 165 mothers with GDM receiving care from a tertiary health centre between January 2020 and June 2021. Mothers with GDM in the intervention 1 arm received a health education booklet in the third trimester, those in intervention 2 arm received mobile call reminders at the fourth and fifth weeks postpartum, control arm received standard care advised in the hospital; they were followed up at 8 weeks postnatally. We used Chi-square test to compare the effectiveness of intervention and standard care. Relative risk with a 95% confidence interval was calculated to measure the strength of association. A P value <0.05 was considered statistically significant.Results:A total of 161 participants (97.58%) completed the study; Out of 55 mothers with GDM in each arm, 30 (56.60%) in the booklet arm, 23 (42.59%) in the mobile reminder arm, and 13 (24.07%) in the standard care arm had undergone postnatal blood glucose monitoring at 8 weeks. There was a statistically significant difference in the postnatal blood glucose monitoring in the booklet arm (RR: 2.21 [1.35–3.64], P value <0.002) compared to the control arm, but the difference was not significant in the mobile reminder arm (1.65 [0.96–2.86], P value 0.072).Conclusion:Health education booklet and mobile call reminders effectively improved postnatal visit compliance compared to standard care. We can diagnose mothers with GDM progressing to diabetes in the postnatal period by implementing these interventions.

Novel 3-DOF counterbalance mechanism based on spring balancer for mobile robot arms

Mobile robot arms have been recently used in service sites. However, their payload is usually small due to the limitation of battery capacity, making it difficult to perform various tasks. Counterbalance mechanism (CBM), a mechanical device that compensates for gravitational torque or force acting on the robot, is one of the good solutions to this payload limit. However, conventional CBMs are focused only on revolute joints, so their application to mobile robot arms possessing a prismatic joint is limited. Therefore, we propose a 3-DOF CBM that counterbalances a robot with prismatic–pitch–pitch joints utilizing only one spring balancer. One prismatic joint and two pitch joints are counterbalanced through a single wire, which is subjected to a constant restoring force generated by a base-mounted spring balancer, and each link of the CBM is only equipped with small idlers. Thanks to this unique structure of the proposed 3-DOF CBM, its application to mobile robot arms can efficiently enhance the payload performance with a minimal increase in volume and mechanical complexity. The experiments confirmed that the proposed CBM and the mobile robot arm equipped with it can properly counterbalance all joints of the arm.

Interactive and Immersive Process-Level Digital Twin for Collaborative Human–Robot Construction Work

Human cognition plays a critical role in construction work, particularly in the context of high-level task planning and in-field improvisation. On the other hand, robots are adept at performing numerical computation and repetitive physical tasks with precise motion control. The unstructured and complex nature of construction environments and the inability to maintain tight tolerances in assembled workpieces pose several unique challenges to the wide application of robots in construction work. Thus, the robotization of field construction processes is best conceived as a collaborative human–robot endeavor that takes advantage of both human and robot intelligence as well as robots’ physical operation capabilities to overcome uncertainties and successfully perform useful construction work onsite. This paper proposes an interactive and immersive process-level digital twin (I2PL-DT) system in virtual reality (VR) that integrates visualization and supervision, task planning and execution, and bidirectional communication to enable collaborative human–robot construction work. In this work paradigm, the human worker is responsible for high-level task planning and work process supervision. The robot undertakes workspace sensing and monitoring, detailed motion planning, and physical execution of the work. A drywall installation case study involving imperfect rough carpentry (wall framing) is presented using a KUKA mobile industrial robotic arm emulator. A human-in-the-loop study involving 20 subjects was conducted for system verification and to collect feedback for future improvements. The experimental results show that users can use the system to specify work sequences, select optimal task plans, and perform robot trajectory guidance after simple training and felt positive about the system functions and user experience. The system demonstrates the potential of transitioning the role of construction workers from physical task performers to robot supervisors. In addition, the system establishes a promising framework for construction workers to remotely collaborate with onsite construction robots.

UNITI Mobile-EMI-Apps for a Large-Scale European Study on Tinnitus.

More and more observational studies exploit the achievements of mobile technology to ease the overall implementation procedure. Many strategies like digital phenotyping, ecological momentary assessments or mobile crowdsensing are used in this context. Recently, an increasing number of intervention studies makes use of mobile technology as well. For the chronic disorder tinnitus, only few long-running intervention studies exist, which use mobile technology in a larger setting. Tinnitus is characterized by its heterogeneous patient's symptom profiles, which complicates the development of general treatments. In the UNITI project, researchers from different European countries try to unify existing treatments and interventions to cope with this heterogeneity. One study arm (UNITI Mobile) exploits mobile technology to investigate newly implemented interventions types, especially within the pan-European setting. The goals are to learn more about the validity and usefulness of mobile technology in this context. Furthermore, differences among the countries shall be investigated. Practically, two native intervention apps have been developed for UNITI and the mobile study arm, which pose features not presented so far in other apps of the authors. Along the implementation procedure, it is discussed whether these features might leverage similar types of studies in future. Since instruments like the mHealth evidence reporting and assessment checklist (mERA), developed by the WHO mHealth technical evidence review group, indicate that aspects shown for UNITI Mobile are important in the context of health interventions using mobile phones, our findings may be of a more general interest and are therefore being discussed in the work at hand.

Virtualization of information object vulnerability testing container based on DeX technology and deep learning neural networks

The modern development of information security tools, along with the improvement of remote access methods, allows software and hardware to be audited without the need for direct access to the system under test. One of its components is related to the implementation of software on mobile ARM processor architectures. Within this direction of development, the approach that allows integrating Linux kernel-based distributions by introducing a virtual container chroot (change root) into the Android OS- based system and, consequently, performing penetration testing without the need to use personal computers is highlighted. An example of this approach is the Kali NetHunter distribution which allows remote system administration functionality through the KeX module. Besides the obvious advantages of KeX functionality, some disadvantages should also be mentioned: firstly, the low speed of GUI processing due to translation to remote hosts and the need to support translation at operating system level; secondly, the consumption of energy resources when using the desktop features of the KeX module. In order to solve the mentioned problems, a system of virtualization of energy-efficient container for testing the vulnerabilities of critical information objects has been developed and based on the principle of multi-containerization. The software of the system is represented by two components: an enlarged module for integration of the chroot container into the DeX environment (primary), and an enlarged module for ensuring energy efficiency using predictive neural network models based on variable time intervals (secondary). As a result of comparing the effectiveness of existing and implemented approaches in penetration testing, it is noted that the proposed system can be used in testing the security of particular platforms and systems, including highly sensitive information objects or resources.

Learning and planning with logical automata

We introduce a method to learn policies from expert demonstrations that are interpretable and manipulable. We achieve interpretability by modeling the interactions between high-level actions as an automaton with connections to formal logic. We achieve manipulability by integrating this automaton into planning via Logical Value Iteration, so that changes to the automaton have predictable effects on the learned behavior. These qualities allow a human user to first understand what the model has learned, and then either correct the learned behavior or zero-shot generalize to new, similar tasks. Our inference method requires only low-level trajectories and a description of the environment in order to learn high-level rules. We achieve this by using a deep Bayesian nonparametric hierarchical model. We test our model on several domains of interest and also show results for a real-world implementation on a mobile robotic arm platform for lunchbox-packing and cabinet-opening tasks.

Digital Twin for Designing and Reconfiguring Human–Robot Collaborative Assembly Lines

This paper discusses a digital twin-based approach for designing and redesigning flexible assembly systems. The digital twin allows modeling the parameters of the production system at different levels including assembly process, production station, and line level. The approach allows dynamically updating the digital twin in runtime, synthesizing data from multiple 2D–3D sensors in order to have up-to-date information about the actual production process. The model integrates both geometrical information and semantics. The model is used in combination with an artificial intelligence logic in order to derive alternative configurations of the production system. The overall approach is discussed with the help of a case study coming from the automotive industry. The case study introduces a production system integrating humans and autonomous mobile dual arm workers.

PERFORMANCE ANALYSIS OF THE SUPERCOMPUTER BASED ON RASPBERRY PI NODES

In this paper, a new Raspberry PI supercomputer cluster architecture is proposed. Generally, to gain speed at petaflops and exaflops, typical modern supercomputers based on 2009-2018 computing technologies must consume between 6 MW and 20 MW of electrical power, almost all of which is converted into heat, requiring high cost for cooling technology and Cooling Towers. The management of heat density has remained a key issue for most centralized supercomputers. In our proposed architecture, supercomputers with highly energy-efficient mobile ARM processors are a new choice as it enables them to address performance, power, and cost issues. With ARM’s recent introduction of its energy-efficient 64-bit CPUs targeting servers, Raspberry Pi cluster module-based supercomputing is now within reach. But how is the performance of supercomputers-based mobile multicore processors? Obtained experimental results reported on the proposed approach indicate the lower electrical power and higher performance in comparison with the previous approaches.

A Laser-Guided Solution to Manipulate Mobile Robot Arm Terminals Within a Large Workspace

Globally accurate manipulation and localization of the terminal of a mobile robot arm within a large workspace has always been a bottleneck problem. In this article, a planar-laser-guided solution is proposed for this issue. The core of this solution is using three movable planar lasers to designate a unique reference point with orientation information within the workspace, and designing a sensor module mounted on the terminal of the robot arm with photoelectric position sensitive devices (PSDs) to measure the relative pose between the terminal and the reference point. Thus, the terminal of the robot can be guided by this relative pose to the target in a given orientation. According to this concept, a parameterized model of the moveable planar lasers and the poses of the PSDs, as well as an expression of their geometric relationship, were derived. The manipulation strategy of the robot arm was further investigated through the model, and an error compensation method to improve the manipulation accuracy was proposed. Finally, a reduced-scale prototype system, based on this solution, was built, on which tracking and positioning experiments of the robot arm were carried out to verify the feasibility of the solution. It has been shown that this laser-guided solution is not sensitive to the blockage (occlusion) of several laser transmitters, which could be an advantage for its application in future autonomous settings.

Desain Robot Holonomic berbasis Roda Mecanum dengan Arm Manipulator

Movement systems of a mobile robot in an industry generally use the concept of differential drive or ackerman steering. However, both methods tend to have low mobility. This paper proposes an industrial mobile robot design with a holonomic concept using mecanum wheels to maneuver in all directions with better mobility. As a commonly used robot in the industrial field, an arm manipulator is combined with a mobile robot. The mobile robot and arm manipulator's mechanical design is made using software inventor and utilizing acrylic as its base material. The electronic design of the robot is created using Eagle software. After the robot manufacturing is complete, then a user interface is made using the processing IDE. Several robot tests are conducted to ensure that the designed robot runs appropriately. From the functional test results, parts of the robot can run well. The smallest error obtained is 5 cm for the robot heading test, and the most significant error is 20 cm. The testing of a servo motor, which is the arm manipulator's primary actuator, showed the highest error of only 2 degrees. Besides, the gripper of the arm manipulator can also hold objects properly.

Molecular Tetrahedrons as Selective and Efficient Ion Transporters via a Two-Station Swing-Relay Mechanism

The traditional approach to utilizing an ion-relay mechanism for ion transport requires three or more ion-relay stations. Herein, we describe a novel strategy, incorporating a swing action to reali...

Evaluation of an exercise-enabling control interface for powered wheelchair users: a feasibility study with Duchenne muscular dystrophy

BackgroundPowered wheelchairs are an essential technology to support mobility, yet their use is associated with a high level of sedentarism that can have negative health effects for their users. People with Duchenne muscular dystrophy (DMD) start using a powered wheelchair in their early teens due to the loss of strength in their legs and arms. There is evidence that low-intensity exercise can help preserve the functional abilities of people with DMD, but options for exercise when sitting in a powered wheelchair are limited.MethodsIn this paper, we present the design and the feasibility study of a new version of the MOVit device that allows powered-wheelchair users to exercise while driving the chair. Instead of using a joystick to drive the wheelchair, users move their arms through a cyclical motion using two powered, mobile arm supports that provide controller inputs to the chair. The feasibility study was carried out with a group of five individuals with DMD and five unimpaired individuals. Participants performed a series of driving tasks in a wheelchair simulator and on a real driving course with a standard joystick and with the MOVit 2.0 device.ResultsWe found that driving speed and accuracy were significantly lowered for both groups when driving with MOVit compared to the joystick, but the decreases were small (speed was 0.26 m/s less and maximum path error was 0.1 m greater). Driving with MOVit produced a significant increase in heart rate (7.5 bpm) compared to the joystick condition. Individuals with DMD reported a high level of satisfaction with their performance and comfort in using MOVit.ConclusionsThese results show for the first time that individuals with DMD can easily transition to driving a powered wheelchair using cyclical arm motions, achieving a reasonable driving performance with a short period of training. Driving in this way elicits cardiopulmonary exercise at an intensity found previously to produce health-related benefits in DMD.

Teaching robots to perform quasi-repetitive construction tasks through human demonstration

Robots can assist workers in performing physically-demanding construction tasks, which are typically quasi-repetitive, wherein the geometry of the workspace is dissimilar despite similar tasks. As a result, robots must determine motion trajectories based on the encountered workspace geometry. Learning from Demonstration (LfD) methods have the potential to be used in teaching robots specific tasks through human demonstration, such that robots can then perform learned tasks under different conditions. In this paper, the LfD method is investigated to teach robots how to perform quasi-repetitive construction tasks. Considering ceiling tile installation as the experimental process, the tasks of maneuvering and positioning tiles in a ceiling grid are defined as the target knowledge to be learned. Using a set of human demonstration videos, the designed approach first translates the physical work context, e.g., the pose of the tile, to the target digital twin, i.e., the workspace as-perceived by the robot. The Reinforcement Learning method is then applied to generate the control policy for the robot to perform the subsequent tasks. The proposed method is evaluated in the Robot Operating System (ROS) Gazebo simulator using a KUKA mobile industrial robotic arm emulator and 60 different scenes as test cases. The results show a 78% success rate in installing ceiling tiles based on 3000 virtual and 85 real demonstration videos. The success rate tends to continually rise with an increase in the number of real demonstration videos, confirming the promise and applicability of the LfD method in teaching robot apprentices to perform quasi-repetitive tasks on construction sites.

Kinematic modeling and control of mobile robot for large-scale workpiece machining

Safety and reliability are significant in the sense of robotic machining for large-scale workpieces. In this article, a control scheme is proposed to ensure the safe motion of the mobile robot. Screw theory is used to analyze the motion of the mobile robot. The mobile platform with Mecanum wheels can be considered as a mechanism with four driven screws in series. An auxiliary reference position of the mobile platform is calculated based on the kinematic model, and the motion of the mobile platform and robot arm can be decoupled to handle its redundant degrees of freedom. Constant speed control is investigated to reduce the interaction force between the robot and platform. Experiments are conducted on the mobile robotic machining task for a large-scale wind turbine blade. The mobile robot moves steadily and smoothly owing to the constant speed control with an auxiliary target.

Intelligence Comes from Within—Personality as a UI Paradigm for Smart Spaces

The buzzword “smart home” promises an intelligent, helpful environment in which technology makes life easier, simpler or safer for its inhabitants. On a technical level, this is currently achieved by many networked devices interacting with each other, working on shared protocols and standards. From a user experience (UX) perspective, however, the interaction with such a collection of devices has become so complex that it currently rather stands in the way of widespread adoption and use. So far, it does not seem likely that a common user interface (UI) concept will emerge as a quasi-standard, as the desktop interface did for graphical UIs. Therefore, our research follows a different approach. Instead of many singular intelligent devices, we envision a UI concept for smart environments that integrates diverse pieces of technology in a coherent mental model of an embodied “room intelligence” (RI). RI will combine smart machinery, mobile robotic arms and mundane physical objects, thereby blurring the line between the physical and the digital world. The present paper describes our vision and emerging research questions and presents the initial steps of technical realization.

RETRACTED ARTICLE: Artificial intelligence with robotics for advanced manufacturing industry using robot-assisted mixed-integer programming model

Digital technologies and artificial intelligence (AI) solutions are growing and changing rapidly and staying at the top are increasingly complicated. Presently, a rapid transformation occurs in advanced manufacturing, the world of innovation, and mass adoption. Robots become even more crucial as now, because they can be connected to the human mind through the machine/brain interface as AI evolves. A significant need to enhance productivity from the manufacturing sector provides the world economy with punishing challenges. The paper addresses the problem in real-world industry applications of the enforcement of an autonomous industrial mobile robot, in all such areas, namely communication, scheduling, mobile robot technology, and planning. The robot-assisted mixed-integer programming model (RA-MIPM) has been proposed for finding the optimal solution for the problem. This paper deals with the issue of the sequence of optimum feeding in a cell with feeders fed by a mobile manipulation arm robot. The efficiency criteria are to reduce the robot’s cumulative travel time in a particular program horizon. Besides, the robot must be designed for production lines to operate within the cell without a lack of feed components.

Comprehensive review on brain-controlled mobile robots and robotic arms based on electroencephalography signals

There is a significant progress in the development of brain-controlled mobile robots and robotic arms in the recent years. New advances in electroencephalography (EEG) technology have led to the possibility of controlling external devices, such as robots, directly via the brain. The development of brain-controlled robotic devices has allowed people with bodily disabilities to enhance their mobility, individuality, and many types of activity. This paper provides a comprehensive review of EEG signal processing in robot control, including mobile robots and robotic arms, especially based on noninvasive brain computer interface systems. Various filtering approaches, feature extraction techniques, and machine learning algorithms for EEG classification are discussed and summarized. Finally, the conditions of the environments in which robots are used and robot types are also discussed.