Dataset Information

An Integrated High-dexterity Cooperative Robotic Assistant for Intraocular Micromanipulation.

ABSTRACT: Retinal surgeons are required to manipulate multiple surgical instruments in a confined intraocular space, while the instruments are constrained at the small incisions made on the sclera. Furthermore, physiological hand tremor can affect the precision of the instrument motion. The Steady-Hand Eye Robot (SHER), developed in our previous study, enables tremor-free tool manipulation by employing a cooperative control scheme whereby the surgeon and robot can co-manipulate the surgical instruments. Although SHER enables precise and tremor-free manipulation of surgical tools, its straight and rigid structure imposes certain limitations, as it can only approach a target on the retina from one direction. As a result, the instrument could potentially collide with the eye lens when attempting to access the anterior portion of the retina. In addition, it can be difficult to approach a target on the retina from a suitable direction when accessing its anterior portion for procedures such as vein cannulation or membrane peeling. Snake-like robots offer greater dexterity and allow access to a target on the retina from suitable directions, depending on the clinical task at hand. In this study, we present an integrated, high-dexterity, cooperative robotic assistant for intraocular micromanipulation. This robotic assistant comprises an improved integrated robotic intraocular snake (I2RIS) with a user interface (a tactile switch or joystick unit) for the manipulation of the snake-like distal end and the SHER, with a detachable end-effector to which the I2RIS can be attached. The integrated system was evaluated through a set of experiments wherein subjects were requested to touch or insert into randomly-assigned targets. The results indicate that the high-dexterity robotic assistant can touch or insert the tip into the same target from multiple directions, with no significant increase in task completion time for either user interface.

SUBMITTER: Jinno M

PROVIDER: S-EPMC8552997 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature

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An Integrated High-dexterity Cooperative Robotic Assistant for Intraocular Micromanipulation.

Jinno Makoto M Li Gang G Patel Niravkumar N Iordachita Iulian I

IEEE International Conference on Robotics and Automation : ICRA : [proceedings]. IEEE International Conference on Robotics and Automation 20210601

Retinal surgeons are required to manipulate multiple surgical instruments in a confined intraocular space, while the instruments are constrained at the small incisions made on the sclera. Furthermore, physiological hand tremor can affect the precision of the instrument motion. The Steady-Hand Eye Robot (SHER), developed in our previous study, enables tremor-free tool manipulation by employing a cooperative control scheme whereby the surgeon and robot can co-manipulate the surgical instruments. A ...[more]

PMID: 34721938

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Project description:BackgroundWhile bedside assistants play a critical role in many robotic operations, substantial heterogeneity remains in bedside assistant training pathways. As such, this study aimed to develop consensus guidelines for bedside assistant skills required for team members in robotic operations.MethodsWe designed a study using the Delphi process to develop consensus guidelines around bedside assistant skills. We generated an initial list of bedside assistant skills from the literature, training materials, and expert input. We selected experts for the Delphi process based on prior scholarship in the area of robotic bedside assistant education and experience facilitating robotic bedside assistant training. For each item, respondents specified which robotic team members should have the skill from a list of "basic" bedside assistants, "advanced" bedside assistants, surgeons, surgical technologists, and circulating nurses. We conducted two rounds of the Delphi process and defined 80% agreement as sufficient for consensus.ResultsFourteen experts participated in two rounds of the Delphi process. By the end of the second round, the group had reached consensus on 253 of 305 items (83%). The group determined that "basic" bedside assistants should have 52 skills and that "advanced" bedside assistants should have 60 skills. The group also determined that surgeons should have 54 skills, surgical technologists should have 25 skills, and circulating nurses should have 17 skills. Experts agreed that all participants should have certain communication skills and basic knowledge of aspects of the robotic system.ConclusionsWe developed consensus guidelines on the skills required during robotic surgery by bedside assistants and other team members using the Delphi process. These findings can be used to design training around bedside assistant skills and assess team members to ensure that each team member has the appropriate skills. Hospitals can also use these guidelines to standardize expectations for robotic team members.

Project description:BackgroundHealth care is shifting toward 5 proactive approaches: personalized, participatory, preventive, predictive, and precision-focused services (P5 medicine). This patient-centered care leverages technologies such as artificial intelligence (AI)-powered robots, which can personalize and enhance services for users with disabilities. These advancements are crucial given the World Health Organization's projection of a global shortage of up to 10 million health care workers by 2030.ObjectiveThis study aimed to investigate the acceptance of a humanoid assistive robot among users with physical disabilities during (1) AI-powered (using a Wizard of Oz methodology) robotic performance of predefined personalized assistance tasks and (2) operator-controlled robotic performance (simulated distant service).MethodsAn explorative qualitative design was used, involving user testing in a simulated home environment and individual interviews. Directed content analysis was based on the Almere model and the model of domestic social robot acceptance.ResultsNine participants with physical disabilities aged 27 to 78 years engaged in robot interactions. They shared their perceptions across 7 acceptance concepts: hedonic attitudes, utilitarian attitudes, personal norms, social norms, control beliefs, facilitating conditions, and intention to use. Participants valued the robot's usefulness for practical services but not for personal care. They preferred automation but accepted remote control of the robot for some tasks. Privacy concerns were mixed.ConclusionsThis study highlights the complex interplay of functional expectations, technological readiness, and personal and societal norms affecting the acceptance of physically assistive robots. Participants were generally positive about robotic assistance as it increases independence and lessens the need for human caregivers, although they acknowledged some current shortcomings. They were open to trying more home testing if future robots could perform most tasks autonomously. AI-powered robots offer new possibilities for creating more adaptable and personalized assistive technologies, potentially enhancing their effectiveness and viability for individuals with disabilities.

Project description:Aging degrades the balance and locomotion ability due to frailty and pathological conditions. This demands balance rehabilitation and assistive technologies that help the affected population to regain mobility, independence, and improve their quality of life. While many overground gait rehabilitation and assistive robots exist in the market, none are designed to be used at home or in community settings. A device named Mobile Robotic Balance Assistant (MRBA) is developed to address this problem. MRBA is a hybrid of a gait assistive robot and a powered wheelchair. When the user is walking around performing activities of daily living, the robot follows the person and provides support at the pelvic area in case of loss of balance. It can also be transformed into a wheelchair if the user wants to sit down or commute. To achieve instability detection, sensory data from the robot are compared with a predefined threshold; a fall is identified if the value exceeds the threshold. The experiments involve both healthy young subjects and an individual with spinal cord injury (SCI). Spatial Parametric Mapping is used to assess the effect of the robot on lower limb joint kinematics during walking. The instability detection algorithm is evaluated by calculating the sensitivity and specificity in identifying normal walking and simulated falls. When walking with MRBA, the healthy subjects have a lower speed, smaller step length and longer step time. The SCI subject experiences similar changes as well as a decrease in step width that indicates better stability. Both groups of subjects have reduced joint range of motion. By comparing the force sensor measurement with a calibrated threshold, the instability detection algorithm can identify more than 93% of self-induced falls with a false alarm rate of 0%. While there is still room for improvement in the robot compliance and the instability identification, the study demonstrates the first step in bringing gait assistive technologies into homes. We hope that the robot can encourage the balance-impaired population to engage in more activities of daily living to improve their quality of life. Future research includes recruiting more subjects with balance difficulty to further refine the device functionalities.

Dataset Information

An Integrated High-dexterity Cooperative Robotic Assistant for Intraocular Micromanipulation.

Publications

An Integrated High-dexterity Cooperative Robotic Assistant for Intraocular Micromanipulation.

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