Experimental evaluation of a novel robotic hospital bed mover with omni-directional mobility

Trends in Robotics Research in Occupational Safety and Health: A Scientometric Analysis and Review

Robots have been deployed in workplaces to assist, work alongside, or collaborate with human workers on various tasks, which introduces new occupational safety and health hazards and requires research efforts to address these issues. This study investigated the research trends for robotic applications in occupational safety and health. The scientometric method was applied to quantitatively analyze the relationships between robotics applications in the literature. The keywords "robot", "occupational safety and health", and their variants were used to find relevant articles. A total of 137 relevant articles published during 2012-2022 were collected from the Scopus database for this analysis. Keyword co-occurrence, cluster, bibliographic coupling, and co-citation analyses were conducted using VOSviewer to determine the major research topics, keywords, co-authorship, and key publications. Robot safety, exoskeletons and work-related musculoskeletal disorders, human-robot collaboration, and monitoring were four popular research topics in the field. Finally, research gaps and future research directions were identified based on the analysis results, including additional efforts regarding warehousing, agriculture, mining, and construction robots research; personal protective equipment; and multi-robot collaboration. The major contributions of the study include identifying the current trends in the application of robotics in the occupational safety and health discipline and providing pathways for future research in this discipline.

Application of a Machine Learning Algorithms in a Wrist-Wearable Sensor for Patient Health Monitoring during Autonomous Hospital Bed Transport

Smart sensors, coupled with artificial intelligence (AI)-enabled remote automated monitoring (RAMs), can free a nurse from the task of in-person patient monitoring during the transportation process of patients between different wards in hospital settings. Automation of hospital beds using advanced robotics and sensors has been a growing trend exacerbated by the COVID crisis. In this exploratory study, a polynomial regression (PR) machine learning (ML) RAM algorithm based on a Dreyfusian descriptor for immediate wellbeing monitoring was proposed for the autonomous hospital bed transport (AHBT) application. This method was preferred over several other AI algorithm for its simplicity and quick computation. The algorithm quantified historical data using supervised photoplethysmography (PPG) data for 5 min just before the start of the autonomous journey, referred as pre-journey (PJ) dataset. During the transport process, the algorithm continued to quantify immediate measurements using non-overlapping sets of 30 PPG waveforms, referred as in-journey (IJ) dataset. In combination, this algorithm provided a binary decision condition that determined if AHBT should continue its journey to destination by checking the degree of polynomial (DoP) between PJ and IJ. Wrist PPG was used as algorithm’s monitoring parameter. PPG data was collected simultaneously from both wrists of 35 subjects, aged 21 and above in postures mimicking that in AHBT and were given full freedom of upper limb and wrist movement. It was observed that the top goodness-of-fit which indicated potentials for high data accountability had 0.2 to 0.6 cross validation score mean (CVSM) occurring at 8th to 10th DoP for PJ datasets and 0.967 to 0.994 CVSM at 9th to 10th DoP for IJ datasets. CVSM was a reliable metric to pick out the best PJ and IJ DoPs. Central tendency analysis showed that coinciding DoP distributions between PJ and IJ datasets, peaking at 8th DoP, was the precursor to high algorithm stability. Mean algorithm efficacy was 0.20 as our proposed algorithm was able to pick out all signals from a conscious subject having full freedom of movement. This efficacy was acceptable as a first ML proof of concept for AHBT. There was no observable difference between subjects’ left and right wrists.

Characterization of hand forces exerted during non-powered hospital bed pushing and pulling tasks

Accurate assessment of biomechanical risk associated with pushing/pulling tasks represents a challenging issue, especially in the health system where personnel are often required to maneuver beds and carts. Most studies in this field have been carried out in the laboratory, while few data have been collected under actual working conditions. This study aims to characterize the forces exerted during non-powered hospital bed maneuvering. Twenty participants were required to move a bed (equipped with a customized handlebar to measure exerted forces) along an actual hospital path including straight, turn and maneuver phases. The results show that higher forces are associated with the initial phase (peak and mean values 222 and 68 N) while the straight, turn and maneuvering phases required similar (lower) efforts. The combined effect of left, right and transversal forces suggests that the trunk of the operator might experience axial rotation, thus calling for further investigations of this aspect.

Design and Evaluation of a Motorized Robotic Bed Mover With Omnidirectional Mobility for Patient Transportation

Patient transportation in hospitals faces many challenges, including the limited manpower, work-related injuries, and low efficiency of current bed pushing methods. This paper presents a new motorized robotic bed mover with omnidirectional mobility to address this problem. This device is composed of an omnidirectional mobility unit, a force sensing-based human-machine interface, and control hardware with batteries and electronics. The proposed bed mover can be attached to the bottom of a manual hospital stretcher, transforming it into a powered omnidirectional bed (OmniBed) that can be used only by one person. The function of the OmniBed is compared with that of a conventional powered bed, which only provides forward assistance with a fifth powered wheel. We perform a pilot study with 14 subjects to evaluate the performance of this OmniBed and benefits for hospital application. The experimental results show that the OmniBed can half the manpower while decreasing back muscle activities, revealing the potential health benefits for older staffs. The OmniBed also shows the promising signs of high precision and handling in small spaces with its one-step "parallel-parking" ability. This device is more ergonomic, more effective, and safer than the conventional powered bed.