Abstract
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.
Highlights
Developing highly skilled nurses, often called Advanced Practice Nurses (APNs), has been identified as a strategic thrust for Singapore to deliver healthcare in the community [1]
The number of APNs are historically small as services requiring APN had not been ubiquitously large and present training pathways require long-term commitment to a nursing career
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Summary
Developing highly skilled nurses, often called Advanced Practice Nurses (APNs), has been identified as a strategic thrust for Singapore to deliver healthcare in the community [1]. These included unsupervised single-feature polynomial regression (PR) [20], rule-based PPG signal quality assessment [21], unsupervised multi-feature anomaly detection [22], and end-to-end deep learning [23]. In terms of data and computation factors, PR, rule-based and multi-feature techniques required only raw PPG measurements from the subject himself to function, while deep learning required the use of multiple public datasets in order to mitigate large amount of annotations. TSatbalned4.aErdmepxactieclapEl4owtterirstabnadndmsaentpsolortblirbeaPkydtohwonn. software were used for data and result visusiaglnizSaaelntiinsootnor.IdSniicgsltnuindalecdtsewignamEve4enfotarmtiosnfPo[2hr6ya]ssiwsoelamosgbuiclsiaenldgPhtionentsooegmPmJenaenandt EIJ4d’sastiaInsngeflotesr.maAnadtDioconoPnInstifenearurrcoehdusraPnPgGe of 1Psht ototo2p0leththDysomPowgraaps hcyhosen basBedlooodn vaopluilmote scthuadnyge[s20] to obtain Cthaerdbieacstapctoivsistiyble quantificaEtIlineocfnrtrasoredudesritmnhgearllmeacaotspitviliaetymount oSSfkkciinon’m’sseptlheuectrtirmnicgaallrcecoosnonduduurccctetiivsvi.ittyy
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