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1. Perceived Usefulness of Robotic Technology for Patient Fall Prevention

   https://doi.org/10.1177/21650799241262812  

   Logsdon, M_Cynthia ; Kondaurova, Irina ; Zhang, Nancy ; Das, Sumit ; Edwards, Bryan_D ; Mitchell, Heather ; Nasroui, Olfa ; Erdmann, Marjorie ; Yu, Hyejin ; Alqatamin, Moath ; et al ( September 2024 , Workplace Health & Safety)

   Technology has the potential to prevent patient falls in healthcare settings and to reduce work-related injuries among healthcare providers. However, the usefulness and acceptability of each technology requires careful evaluation. Framed by the Technology Acceptance Model (TAM) and using the Adaptive Robotic Nursing Assistant (ARNA) to assist with patient ambulation, the present study examined the perceived usefulness of robots in patients’ fall prevention with implications for preventing associated work-related injuries among healthcare providers.

   Employing an experimental design, subjects were undergraduate nursing students ( N = 38) and one external subject (not a nursing student) who played the role of the patient. Procedures included subjects ambulating a simulated patient in three ways: (a) following the practice of a nurse assisting a patient to walk with the patient wearing a gait belt; (b) an ARNA-assisted process with the gait belt attached to ARNA; (c) an ARNA-assisted process with a subject walking a patient wearing a harness that is attached to ARNA. Block randomization was used with the following experimental scenarios: Gait Belt (human with a gait belt), “ARNA + Gait Belt” (a robot with a gait belt), and “ARNA + Harness” (a robot with a harness). Descriptive statistics and a multiple regression model were used to analyze the data and compare the outcome described as the Perceived Usefulness (PU) of a robot for patient walking versus a human “nurse assistant” without a robot. The independent variables included the experimental conditions of “Gait Belt,” “ARNA + Gait Belt,” and “ARNA + Harness,” the subject’s age, race, and previous videogame playing experience.

   Results indicated that PU was significantly higher in the Gait Belt + ARNA and Harness + ARNA conditions than in the Gait Belt condition ( p-value <.01 for both variables). In examining potential influencing factors, the effects of race (White, African American, and Asian), age, and previous video-playing experience were not statistically significant ( p-value >.05).

   Results demonstrated that using robot technology to assist in walking patients was perceived by subjects as more useful in preventing falls than the gait belt. Patient fall prevention also has implications for preventing associated work-related injuries among healthcare providers.

   Understanding the effects of a subject’s perceptions can guide further development of assistive robots in patient care. Robotic engineers and interdisciplinary teams can design robots to accommodate worker characteristics and individual differences to improve worker safety and reduce work injuries.

    

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2. From the Dexterous Surgical Skill to the Battlefield—A Robotics Exploratory Study

   https://doi.org/10.1093/milmed/usaa253  

   Gonzalez, Glebys T ; Kaur, Upinder ; Rahman, Masudur ; Venkatesh, Vishnunandan ; Sanchez, Natalia ; Hager, Gregory ; Xue, Yexiang ; Voyles, Richard ; Wachs, Juan ( January 2021 , Military Medicine)

   null (Ed.)

   ABSTRACT Introduction Short response time is critical for future military medical operations in austere settings or remote areas. Such effective patient care at the point of injury can greatly benefit from the integration of semi-autonomous robotic systems. To achieve autonomy, robots would require massive libraries of maneuvers collected with the goal of training machine learning algorithms. Although this is attainable in controlled settings, obtaining surgical data in austere settings can be difficult. Hence, in this article, we present the Dexterous Surgical Skill (DESK) database for knowledge transfer between robots. The peg transfer task was selected as it is one of the six main tasks of laparoscopic training. In addition, we provide a machine learning framework to evaluate novel transfer learning methodologies on this database. Methods A set of surgical gestures was collected for a peg transfer task, composed of seven atomic maneuvers referred to as surgemes. The collected Dexterous Surgical Skill dataset comprises a set of surgical robotic skills using the four robotic platforms: Taurus II, simulated Taurus II, YuMi, and the da Vinci Research Kit. Then, we explored two different learning scenarios: no-transfer and domain-transfer. In the no-transfer scenario, the training and testing data were obtained from the same domain; whereas in the domain-transfer scenario, the training data are a blend of simulated and real robot data, which are tested on a real robot. Results Using simulation data to train the learning algorithms enhances the performance on the real robot where limited or no real data are available. The transfer model showed an accuracy of 81% for the YuMi robot when the ratio of real-tosimulated data were 22% to 78%. For the Taurus II and the da Vinci, the model showed an accuracy of 97.5% and 93%, respectively, training only with simulation data. Conclusions The results indicate that simulation can be used to augment training data to enhance the performance of learned models in real scenarios. This shows potential for the future use of surgical data from the operating room in deployable surgical robots in remote areas. 

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3. aBnormal motION capture In aCute Stroke (BIONICS): A Low-Cost Tele-Evaluation Tool for Automated Assessment of Upper Extremity Function in Stroke Patients

   https://doi.org/10.1177/15459683231184186  

   Zamin, Syed_A ; Tang, Kaichen ; Stevens, Emily_A ; Howard, Melissa ; Parker, Dorothea_M ; Seals, Allyson ; Jiang, Xiaoqian ; Savitz, Sean ; Shams, Shayan ( August 2023 , Neurorehabilitation and Neural Repair)

   The incidence of stroke and stroke-related hemiparesis has been steadily increasing and is projected to become a serious social, financial, and physical burden on the aging population. Limited access to outpatient rehabilitation for these stroke survivors further deepens the healthcare issue and estranges the stroke patient demographic in rural areas. However, new advances in motion detection deep learning enable the use of handheld smartphone cameras for body tracking, offering unparalleled levels of accessibility.

   In this study we want to develop an automated method for evaluation of a shortened variant of the Fugl-Meyer assessment, the standard stroke rehabilitation scale describing upper extremity motor function. We pair this technology with a series of machine learning models, including different neural network structures and an eXtreme Gradient Boosting model, to score 16 of 33 (49%) Fugl-Meyer item activities.

   In this observational study, 45 acute stroke patients completed at least 1 recorded Fugl-Meyer assessment for the training of the auto-scorers, which yielded average accuracies ranging from 78.1% to 82.7% item-wise.

   In this study, an automated method was developed for the evaluation of a shortened variant of the Fugl-Meyer assessment, the standard stroke rehabilitation scale describing upper extremity motor function. This novel method is demonstrated with potential to conduct telehealth rehabilitation evaluations and assessments with accuracy and availability.

    

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4. Hierarchical Task Assignment and Path Finding with Limited Communication for Robot Swarms

   https://doi.org/10.3390/app11073115  

   Albani, Dario ; Hönig, Wolfgang ; Nardi, Daniele ; Ayanian, Nora ; Trianni, Vito ( April 2021 , Applied Sciences)

   null (Ed.)

   Complex service robotics scenarios entail unpredictable task appearance both in space and time. This requires robots to continuously relocate and imposes a trade-off between motion costs and efficiency in task execution. In such scenarios, multi-robot systems and even swarms of robots can be exploited to service different areas in parallel. An efficient deployment needs to continuously determine the best allocation according to the actual service needs, while also taking relocation costs into account when such allocation must be modified. For large scale problems, centrally predicting optimal allocations and movement paths for each robot quickly becomes infeasible. Instead, decentralized solutions are needed that allow the robotic system to self-organize and adaptively respond to the task demands. In this paper, we propose a distributed and asynchronous approach to simultaneous task assignment and path planning for robot swarms, which combines a bio-inspired collective decision-making process for the allocation of robots to areas to be serviced, and a search-based path planning approach for the actual routing of robots towards tasks to be executed. Task allocation exploits a hierarchical representation of the workspace, supporting the robot deployment to the areas that mostly require service. We investigate four realistic environments of increasing complexity, where each task requires a robot to reach a location and work for a specific amount of time. The proposed approach improves over two different baseline algorithms in specific settings with statistical significance, while showing consistently good results overall. Moreover, the proposed solution is robust to limited communication and robot failures. 

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5. ARNA, a Service robot for Nursing Assistance: System Overview and User Acceptability

   https://doi.org/10.1109/CASE48305.2020.9216845  

   Abubakar, Shamsudeen ; Das, Sumit K. ; Robinson, Chris ; Saadatzi, Mohammed N. ; Logsdon, M. Cynthia ; Mitchell, Heather ; Chlebowy, Diane ; Popa, Dan O. ( August 2020 , IEEE CASE 2020)

   Using robots capable of collaboration with humans to complete physical tasks in unstructured spaces is a rapidly growing approach to work. Particular examples where increased levels of automation can increase productivity include robots used as nursing assistants. In this paper, we present a mobile manipulator designed to serve as an assistant to nurses in patient walking and patient sitting tasks in hospital environments. The Adaptive Robotic Nursing Assistant (ARNA) robot consists of an omnidirectional base with an instrumented handlebar, and a 7-DOF robotic arm. We describe its components and the novelties in its mechanisms and instrumentation. Experiments with human subjects that gauge the usability and ease of use of the ARNA robot in a medical environment indicate that the robot will get significant actual usage, and are used as a basis for a discussion on how the robot's features facilitate its adaptability for use in other scenarios and environment. 

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