Intensive Care Units (ICUs) are equipped with many sophisticated sensors and monitoring devices to provide the highest quality of care for critically ill patients. However, these devices might generate false alarms that reduce standard of care and result in desensitization of caregivers to alarms. Therefore, reducing the number of false alarms is of great importance. Many approaches such as signal processing and machine learning, and designing more accurate sensors have been developed for this purpose. However, the significant intrinsic correlation among the extracted features from different sensors has been mostly overlooked. A majority of current data mining techniques fail to capture such correlation among the collected signals from different sensors that limits their alarm recognition capabilities. Here, we propose a novel information-theoretic predictive modeling technique based on the idea of coalition game theory to enhance the accuracy of false alarm detection in ICUs by accounting for the synergistic power of signal attributes in the feature selection stage. This approach brings together techniques from information theory and game theory to account for inter-features mutual information in determining the most correlated predictors with respect to false alarm by calculating Banzhaf power of each feature. The numerical results show that the proposed method can enhance classification accuracy and improve the area under the ROC (receiver operating characteristic) curve compared to other feature selection techniques, when integrated in classifiers such as Bayes-Net that consider inter-features dependencies. As there is no single sensor/device capable of complying with all clinical requirements, multiple therapeutic and monitoring devices are often deployed in the Intensive Care Units (ICUs) to collect real-time data for diagnosis, prognosis, treatment and more generally, patient monitoring. These devices generate visual and acoustic alarms to inform nurses and physicians about changes in a patient’s condition or a failure in device functionality . HoweverA false alarm might happen due to the low quality of signals  as a resAmong the non–learning methods, we can refer to the method proposed in . In thisOne of the challenges facing the above mentioned methods is that features whose impact on the model performance is individually low might be excluded in the feature extraction phase, while their combination with other features could improve the overall performance. These methods consider either the effect of each feature by itself on the target or the inter-feature mutual information to improve the performance. Therefore, the features relevant to the target class might be discarded if they have high correlation to the already selected features.Banzhaf power index that accounts for interdependency among the extracted features and their relevancy to the target class. Coalition game theory has been recently employed in the feature selection stage of machine learning approaches to improve their performance, where features are modeled as game players [Shapley value. The Shapley value of a feature shows the contribution of the feature in improving the accuracy of classification when all possible coalitions of features with any arbitrary size are considered. While this method can have a considerable impact on capturing the higher-level correlation among features , it involves a high computational complexity to calculate this factor for all possible groupings of the features, in particular in the presence of a large number of features. In [To suppress the false alarm in ICUs, here we develop a new coalition game-theoretical model based on  players ,29,30,31ures. In , we utilBanzhaf power to declare salient features with comparable accuracy but much less complexity. This metric is proportional to the number of times that a feature is a critical player for a coalition. In the proposed model, we define an information-theoretic notion for Banzhaf power, where a feature is determined to have a critical impact on a set of features if it increases the relevancy of the selected feature set on a target class and also is interdependent on more than half of the members in the set. The numerical results validate the desirable performance improvement of this method in reducing the false alarm rate compared to existing feature selection techniques when a classification method that has the capability of considering inter-features dependencies is utilized.In , we studThe rest of this paper is organized as follows. List of the abbreviations used in this paper is presented in true, or false. Each alarm was reviewed by a team of experts and at least two of them agreed on the alarm type. These alarms are assumed to be at least 5 min apart and are triggered 5 min from the start of each record while the onset of the events is within 10 s of the alarm . The PhysioNet challenge-2015 dataset includes a training dataset containing the recordings for 750 patients, and a test dataset containing the recordings for 500 patients. It is worth mentioning that the test dataset is not publicly available and we only had access to the training dataset. Out of 750 recordings in training dataset, only 220 samples include all three signals of ECGII, plethysmogram (PLET) and ABP that were used in this study. The resolution and frequency of each signal are 12 bit and 250 Hz, respectively. Furthermore, each signal has been filtered by notch filters and a finite impulse response (FIR) band pass (0.05 to 40 Hz). The signals might suffer from movement artifact, sensor disconnects, interference from pacemakers and other events.In this work, we use the publicly available Physionet Challenge 2015 database ,33. FourExtracting relevant features from the entire time-series signals is a key step in detecting the false alarms, as considering the original signals results in a large number of highly correlated features compared to the sample size that increases the chance of over–fitting the model to the training data. Here, we apply discrete wavelet transforms (DWT) on ECG, ABP and PLETH signals. This method is utilized as it can separate details in signals compared to other transforms and it can eliminate the noise with a low distortion rate. The DWT’s capability to detect specific time-frequency components of ECG signals has motivated several researchers to utilize this method in several related applications ,35,36.This transform performs an adaptive time-frequency decomposition of patterns in a signal. Moreover, the signal can be represented by a few wavelet coefficients and hence less features can be extracted from a signal.A set of dilated-translated wavelets, ined as: . DWT comh is a high pass filter. When this filter is convolved with a signal at low scales, the output is called an approximation set of the signal. Convolving a low–pass filter, approximate and detail coefficients can be done again depending on how much detail is required. Approximate and detail coefficients can be obtained respectively from Equations h is a hl shows the decomposition level. In Equations  and 4),,4), a−1 Here we calculate wavelet coefficients at 6 levels  of a player in a transferable utility coalition game including ey value , Banzhafaf power , and Banaf value . In our A simple game refers to a class of coalition games with characteristic function satisfying the following conditions .For swinger if the removal of this player from a winning coalition Based on the first property, the coalitions are divided into two sets of winning coalition, The Banzhaf power for player v function is calculated based on its members (features)’s contribution to the classifier performance. We measure the contribution of each feature in the game noting all possible coalitions of the players using Banzhaf power. The criterion to determine the most informative subset of features is the relevance of this set to the target class as well as the interdependence among the group members. If the relevance of the feature C, C can be approximated as [Next, we discuss our proposed coalition-based feature selection method, in which the features are considered as the players of the game, and the mated as :(8)R A subset of features with the lowest intra correlation and the highest inter correlation with the labels are selected using the Correlation-based Feature Subset Selection (CFS) [In order to evaluate the relevance of the obtained features, we used on (CFS) ; (ii) A on (CFS) ; (iii) Ton (CFS) ; (iv) Thon (CFS) , (v) Theon (CFS) . For comon (CFS)  and logion (CFS) ,29.In order to compare the performance of the aforementioned feature selection methods, Bayes-Net with 10-fold cross validation is selected as a representative classifier to classify the alarms into false and true alarms. It is worth mentioning that definition of the proposed feature selection method is independent of the choice of classifier technique and it can be applied to all classification techniques. The classification success rates, and the sensitivity and specificity for all aforementioned feature selection methods are presented in The results in The results show that the proposed algorithm when combined with Bayes Net classifier outperforms the majority of other feature selection methods in recognizing true and false alarms with a low computation complexity. Interestingly, the false alarm recognition rate (specificity) is substantially improved compared to the best competitors methods, while the true alarm recognition (sensitivity) remains almost at the same level. The low success rate for NoFS is somewhat expected and demonstrates the value of feature selection, since incorporating all features in classification not only increases the time and computational load of the classifier, but also decreases the classification accuracy due to the well-known over-fitting problem. It is also observed that the proposed method provides a similar level of accuracy compared to our previously developed coalition-based game theoretical feature selection method using Shapley value. However, the Banzhaf-based coalition game includes much less computational power. In Shapley-based coalition game, the marginal importance of a feature P and T have proved to be more significant for this decision making task. A similar observation can be made regarding the low level features of PLETH. However, the medium levels of ABP appear to be selected more frequently in the process. The frequencies of variations in these levels of wavelet decomposition seem to correspond to the informative patterns in dicrotic notch.Finally, we note that the above results are provided for 220 samples with valid ECG II, PLETH, and ABP signals, combining all alarm types, since the sample size for specific alarms are too small. Out of 220 patients with three recorded signals, the number of available samples for Asystole, Extreme Bradycardia, Extreme Tachycardia, Ventricular-Flutter/Fibrillation, and Ventricular-Tachycardia is 34 , 30 , 15 , 17 and 124 , respectively. However, for the sake of completeness, we present the results of our proposed method for the fifth alarm: Ventricular-Tachycardia, which includes 124 samples. We used the top-20 features reported by the proposed method. The proposed feature selection method can be utilized along with different classification methods. Here, we note that it is a known fact that different feature selection methods may perform differently when applied to different classifiers. To investigate this problem, we have tried the performance of our feature selection method over different classifiers including Random Forest, Naive Bayes, Sequential Minimal Optimization (SMO), and J48 decision tree. We observe that our proposed method performs almost equivalently to other feature selection methods, and the improvement is considerable for the Bayes-Net classifier. The justification for this observation is that Bayes-Net considers the relations among the features and hence using Banzhaf power which selects features with stronger synergistic powers yields better results. Therefore, to benefit from this proposed method, it is more advantageous to use it with classifiers that consider inter-feature dependencies. It is worth mentioning that the numerical results are reported for a small and relatively unbalanced dataset of 220 patients with three signals of ECG II, ABP and PLET. Out of these samples, 50 are false and 170 are true alarms.simple coalition game, where the average contribution of each feature (game player) is determined by Banzhaf power. A feature is defined to play a critical role in a coalition if it increased the relevancy of the coalition on target class and also was interdependent on more than half of the coalition’s members. The numerical results presented in this paper, calculated using Bayes-Net classifier, showed the superiority of the proposed method over existing feature selection methods such as Gain Ratio, Chi-square, and Relief methods in terms of false alarm detection as well as area under the ROC curve. It should be noted that the proposed method can be applicable to commonly used classifiers that require feature selection. However, it is more likely that the proposed method outperforms other feature selection techniques when integrated with classifiers which consider the inter-feature dependencies.One of the critical concerns in intensive care units that has not been resolved yet is the high false alarm rate. In this paper, we proposed a novel coalition game theoretic-based feature selection method to detect the false alarms. The proposed method accounts for information-theoretic correlation among the features in all possible coalitions of them. This feature selection problem is defined as a


The Hospital Pediatrico Universitario (HOPU) is the principal institution in Puerto Rico offering medical services to the children of the island and the Caribbean. There is limited use of point-of-care ultrasound (PoCUS) in their emergency department (ED) and obtaining an ultrasound through radiology is prohibitively time consuming. The objective of this study is to increase PoCUS knowledge and comfort by the ED physicians in the HOPU pediatric emergency department.N = 10). Pretest scores ranged from 30 to 63.3% with a mean of 41.6% [standard deviation (SD) 9.95]. The posttest scores ranged from 55 to 96%, with a mean of 66.1% (SD 12.26). The mean difference in scores was 24.42%  with a significance of p < 0.05 and range of 8.3–36.6%. Physician experience and confidence with each topic improved from baseline. After the course, the majority of the participants (> 70%) had at least some confidence in 5 of the 6 topics.Thirteen general pediatricians completed the entire PoCUS course, but only 10 completed both the pre- and post-tests and therefore included in the analysis (In EDs with limited resources, a longitudinal PoCUS educational curriculum is effective in improving the knowledge and comfort of physicians with limited PoCUS experience. The effectiveness of scheduled, repeated courses to refresh and refocus participants was highlighted following the unexpected challenges encountered during the course, including multiple natural disasters. PoCUS is an affordable, safe, and efficient imaging modality with a wide variety of uses in the medical field, particularly in the emergency setting. Medical providers in Europe have utilized ultrasound for decades and over the last 20 years; its use has exploded in the United States. A PoCUS examination is performed at bedside with immediate results, without radiation exposure to the patient or provider. In addition, ultrasonography is a fraction of the cost of other imaging modalities, including radiography, computed tomography (CT), and magnetic resonance imaging (MRI) which makes it a more accessible imaging option in financially strained circumstances.Since 1985, the World Health Organization (WHO) has recommended ultrasonography for developing countries because it is portable, inexpensive, non-invasive, safe, and provides information immediately , 8. Via Short-term PoCUS training programs are proven to deliver adequate knowledge and skills to novices . Shah etMore recently, Rominger et al. implemented a 12-month longitudinal PoCUS curriculum for primary care physicians working in rural outpatient clinics in Mexico . They orThe effectiveness of a PoCUS curriculum is not limited to developing countries. A study by Clay et al. focused on United States (US) Internal Medicine residents, found that a single day of intensive PoCUS training at the beginning of the year yielded significant improvement in PoCUS interpretation skills , 2. TheyThe Hospital Pediatrico Universitario (HOPU) is the principal institution in Puerto Rico offering medical services to the children of Puerto Rico and the Caribbean. HOPU is the pediatric academic institution for the University of Puerto Rico School of Medicine and the clinical center for the pediatric residency program. It is the only pediatric hospital to provide tertiary services to the island and guarantees that its services are accessible to children of all socio-economic levels, including the medically indigent. The Emergency Department (ED) at HOPU receives about 10,000 pediatric patients annually. It is generally a low-flow but high-acuity ED where general pediatricians and residents provide health care 24 h a day, 7 days a week. Prior to this study, there were no ultrasound machines in the Emergency Department and the physicians had no PoCUS training. Access to ultrasound was available through the Radiology Department but since the Radiology Department serves the entire Medical Center and not just the pediatric hospital, obtaining an official ultrasound report takes 24–48 h. Many of the conditions seen in the Emergency Department are easily identified and managed with the appropriate use of PoCUS. For example, the identification of cellulitis, abscess, free fluid in the abdomen (traumatic or nontraumatic), hydronephrosis, pericardial effusion, pleural effusion, appendicitis, intussusception, and global heart function. If the providers have basic PoCUS knowledge and skills, it would greatly aid in the local management of many medical conditions, reduce the number of referrals for imaging, and expedite the care of patients who do have a serious medical or surgical condition.The objective of this study is to increase ultrasound knowledge and comfort by the ED physicians in the HOPU pediatric emergency department. Although Puerto Rico is a US territory, its pediatric health care model and funding can be likened to a resource-limited site with the closest additional pediatric resources located 1000 miles away in Miami, Florida. Therefore, it is a unique setting which cannot be simply grouped with US facilities or resource-limited ones, so many of the previous studies may not be easily generalizable to this particular institution. Therefore, the authors sought to identify an effective ultrasound curriculum for the HOPU ED providers.This is a longitudinal educational curriculum conducted at the HOPU in San Juan, Puerto Rico. It is a pilot study to determine if a longitudinal approach would be an effective teaching strategy in this particular group of physicians. The instructors for the educational sessions included two pediatric emergency medicine (PEM) physicians, an emergency medicine (EM) physician and ultrasound fellow, and a PEM fellow from the University of Louisville. A PEM physician and ultrasound fellow from Indiana University joined the primary investigator on one of the trips. Both the University of Louisville and the University of Puerto Rico Institutional Review Boards reviewed this study and deemed it exempt.Prior to initiation of the course work, the primary investigator conducted a needs assessment at HOPU with local physicians to determine the most relevant topics and commonly encountered regional pathologies. They designed the curriculum based on the findings in the needs assessment and desires of the local providers with a focus on trauma evaluation (FAST), bedsides cardiac evaluation for global function, identification of skin and soft tissue infections, and abdominal pathologies, specifically intussusception and appendicitis. To accommodate the schedules of the local physicians, the team taught the course in multiple sessions rather than one long conference, which the Puerto Rican providers preferred. In addition, the amount of information presented in one long conference can be overwhelming for novices and the investigators had concerns for adequate knowledge retention in a single course. The longitudinal approach allowed participants to master several PoCUS concepts before moving on to new ones. Lastly, this design put the educators at the site for multiple points in time to review previous concepts, answer additional questions, discuss images and cases, and review technique. The HOPU ED did not have an ultrasound machine in their department prior to the course; however, Norton Children’s Hospital donated a Sonosite M-turbo ultrasound machine with low-frequency (curvilinear), phased-array, and high-frequency (linear) probes.The curriculum was designed based on the previous work by Rominger et al. in Mexico. This structure was used due to the time constraints of the local providers and the fact that the providers were all general pediatricians without subspecialty training, similar to the general practitioners trained in Mexico. The curriculum consisted of three, 2-day sessions given over 12 months. The instructors taught the material in English and, as a US territory, all participants were fluent in English. The first day consisted of lectures with hands-on training on healthy volunteers. The class ratio was 5–7 learners per two instructors. The second day of the course focused on hands-on examination of patients with pathologic findings on ultrasound. Prior to the practice sessions, the instructors identified patients with relevant pathology throughout the hospital and obtained consent (and assents as applicable) to ultrasound for learning purposes.The participants had all completed a 3-year general pediatrics residency and were working in the HOPU ED as independent providers and supervising physicians. The investigators tested the baseline ultrasound knowledge of each participant prior to the first session using a 25-question written test. The test was developed as part of the PEM PoCUS credentialing program at the University of Louisville used to demonstrate bedside PoCUS competency. The primary investigator distributed the same test to participants 3 months after the final session to assess ultrasound knowledge. The results of the test were withheld from participants until the completion of the post-curriculum evaluation. They each completed a questionnaire prior to each session and again 3 months following the last session, which assessed their subjective experience and confidence in the various PoCUS modalities. The first teaching course was in August 2017 and focused on care and use of the ultrasound machine, introduction to PoCUS, focused assessment with sonography in trauma (FAST), and basic bedside cardiac ultrasound. The next session was scheduled for November 2017, but the curriculum was adjusted following multiple natural disasters in Puerto Rico during the fall of 2017. The next session moved to February 2018 and it included a half-day review of the previous information plus the introduction of lung, skin and soft tissue, kidney, bladder, and ultrasound-guided procedures. The final session was May 2018 and focused on cardiovascular, abdominal, and ocular ultrasound. The instructors uploaded the presentations from each session onto a google drive that the participants had access to for their reference and review. The participants continued to order formal ultrasound examinations on patients through the HOPU radiology department throughout the study following their own bedside PoCUS and assessment. This was done for the safety of the patients until the providers could show competency with ultrasound by conducting at least 30 scans for each body area and completing a practical assessment of their skills.There was a PoCUS log with the machine to document the type of study conducted, reason for the imaging, presumed diagnosis, and querying if the PoCUS findings confirmed or changed the presumed diagnosis for the patient. Information included the provider’s initials, patient age, patient gender, and date of the study. This would allow the investigators to track the number of different PoCUS studies conducted and the number performed by each provider. This would allow the investigators to track the number of each type of study conducted by each participant. It would also allow investigators to determine if improved knowledge and experience with PoCUS was assisting diagnosis and treatment plans, specifically the clinical integration of the information. Participants downloaded de-identified images to a secure flash drive, which the senior investigator, who is a core instructor for the ultrasound curriculum at the University of Louisville and instructor at multiple national and international PoCUS courses, reviewed for technique, quality, and findings.The investigators analyzed the basic demographic data for the participants and compared each one’s change in confidence and experience from the beginning to the end of the yearlong curriculum. Investigators compared the pretest and posttest averages using a paired T test. They conducted all analyses in IBM SPSS statistics version 24.p value < 0.01) , have the appropriate probe and the appropriate windows. For example, an adequate bedside cardiac PoCUS would have the correct probe, orientation and windows for a parasternal long axis, parasternal short axis, subxiphoid, and/or apical 4 chamber views with at least 3 of the traditional cardiac windows.This study met the objective to increase the PoCUS knowledge of the physicians working in the HOPU pediatric ED. The participants’ scores increased by an average of almost 25%. This is similar to the improvement in immediate posttest scores following a single day of intensive training demonstrated by Clay , 2; howeThis curriculum consisted of 15 classroom/lecture hours, 15 required hands-on, and 10 optional extra hands-on hours over the course of the year, which is similar to the total training hours in the 9-week educational intervention in Rwanda . The lonThe HOPU participants had little to no confidence in bedside PoCUS. Following the educational intervention, the majority of participants self-report at least some confidence and many had moderate confidence with five of the six imaging modalities presented over the course. The participants all conducted at least 5 of each type of body scan during the educational sessions and hands-on training. Keddis et al. also evaluated the confidence level in a similar 5-point Likert scale and showed a comparable improvement in confidence immediately following short-term PoCUS teaching sessions . HoweverThe HOPU ED providers were novice ultrasonographers with little to no experience with PoCUS prior to the educational intervention. The initial design called for ultrasound logs to accurately document the total number, type, and intent of studies for each participant. However, due to multiple natural disasters, which occurred on the island shortly after the initiation of the curriculum, the logs were not accurately documented. Without accurate documentation of number of scans per provider, no providers were eligible for a practical ultrasound evaluation. The pilot phase of the study showed acquisition of knowledge and confidence with this type of training, so the next phase will focus on documentation and credentialing. The primary investigator will become the local ultrasound champion in the HOPU ED at the completion of her fellowship. With a local champion, the ultrasound logs will be more accurate and she can conduct practical ultrasound assessments of the local physicians when they have completed at least 30 scans of a particular body area.There were 52 studies saved on the ultrasound machine which were used in the analysis. At the completion of the curriculum, the participants report conducting between 58 and 131 PoCUS studies. This discrepancy suggests that the HOPU physicians were not saving all of their images and using PoCUS more than documented. The number of overall PoCUS studies is lower than those documented in previous interventions by Rominger and Shah ; howeverThe intervention required curriculum modification following two major natural disasters (Hurricanes Irma and Maria), which affected the island in September 2017 after the first educational session. The hospital was with limited power and resources for 3 months following the hurricanes. The second session was originally scheduled for November 2017, but because power and communication had not been consistently restored, the session was moved to February 2018. Because there was a prolonged time between educational sessions, the educators gave a review of the August information before the scheduled February session. There was a significant drop in PoCUS use from September 2017 through November 2017, but once there was restoration of consistent power and communication, there was a dramatic increase in PoCUS use. It can be theorized that the providers utilized PoCUS as an effective diagnostic modality when there were still limited resources while the island was recovering from the hurricanes. This supports the usefulness of PoCUS training in this setting and the value of a longitudinal curriculum. Having a scheduled session following the disaster recovery period allowed the learners to review the previous information after a period of limited use. In addition, almost 25% of initial participants did not complete the full 12-month curriculum and their information was removed from the study. These providers left the island permanently following the natural disasters.The biggest limitation in this study was natural disasters, which resulted in loss of participants, decreased scanning months and adjustment of the originally scheduled curriculum. This also affected the documentation of images on the ultrasound logs which were used to track the number and type of study and the clinical applicability of the information. Upon discussion with the providers, it seemed that they were using the ultrasound machine much more frequently than they were documenting; however, only the documented studies could be used in analysis. This was beyond the control of the investigators; however, the remaining participants still showed increase in PoCUS knowledge, confidence and experience despite the circumstances. The total number of PoCUS studies and confidence level may have been affected by recall bias since each participant was asked to self-report this information at the completion of the course. The participants did not keep accurate ultrasound logs so the information was limited to subjective recall. In addition, the biggest limitation was no local ultrasound champion to oversee and answer real-time questions. This would have been helpful following the hurricanes when communication with providers on the island was limited.The investigators show that a longitudinal PoCUS curriculum is an effective design to teach PoCUS to local physicians working in acute ED settings. Due to unforeseen circumstances, 6 months of the intervention occurred during a resource-limited time period for the HOPU, which further demonstrated its utility in this setting. This also highlighted the value of repeated, longitudinal sessions so the providers can refresh their knowledge following gap in ultrasound use due to challenging circumstances. At the completion of the course, all participants demonstrated a sustained increase in PoCUS knowledge, confidence and experience. A local champion, better documentation, and practical ultrasound assessments for independent scanning would be the next steps in continuing the PoCUS education program at HOPU.


We know little about the best approaches to design training for healthcare professionals. We thus studied how user-centered and theory-based design contribute to the development of a distance learning program for professionals, to increase their shared decision-making (SDM) with older adults living with neurocognitive disorders and their caregivers.In this mixed-methods study, healthcare professionals who worked in family medicine clinics and homecare services evaluated a training program in a user-centered approach with several iterative phases of quantitative and qualitative evaluation, each followed by modifications. The program comprised an e-learning activity and five evidence summaries. A subsample assessed the e-learning activity during semi-structured think-aloud sessions. A second subsample assessed the evidence summaries they received by email. All participants completed a theory-based questionnaire to assess their intention to adopt SDM. Descriptive statistical analyses and qualitative thematic analyses were integrated at each round to prioritize training improvements with regard to the determinants most likely to influence participants’ intention.Of 106 participants, 98 completed their evaluations of either the e-learning activity or evidence summary (93%). The professions most represented were physicians (60%) and nurses (15%). Professionals valued the e-learning component to gain knowledge on the theory and practice of SDM, and the evidence summaries to apply the knowledge gained through the e-learning activity to diverse clinical contexts. The iterative design process allowed addressing most weaknesses reported. Participants’ intentions to adopt SDM and to use the summaries were high at baseline and remained positive as the rounds progressed. Attitude and social influence significantly influenced participants' intention to use the evidence summaries (P < 0.0001). Despite strong intention and the tailoring of tools to users, certain factors external to the training program can still influence the effective use of these tools and the adoption of SDM in practice.A theory-based and user-centered design approach for continuing professional development interventions on SDM with older adults living with neurocognitive disorders and their caregivers appeared useful to identify the most important determinants of learners’ intentions to use SDM in their practice, and validate our initial interpretations of learners’ assessments during the subsequent evaluation round. Neurocognitive disorders require making several difficult decisions to ensure older adults remain independent as long as possible, while maintaining their well-being and safety . These dAlthough SDM can improve the quality of life of patients with neurocognitive disorders and their caregivers , decisioInitiatives aimed at increasing the use of SDM by HCPs may comprise training programs, leaflets, financial incentives or email reminders; however evidence remains scarce on their effectiveness to change professional behaviour and improve patient/caregiver participation in decision-making , 19. ThiIn this research project, we studied how user-centered design and theory contribute to the development of a distance learning program to support HCPs in implementing SDM with older adults living with neurocognitive disorders and their family/friend caregivers. More specifically, the investigation was guided by the following overarching research question: What are the features of the training program and design strategies that may increase HCPs’ intention to adopt SDM in this clinical context?SDM requires the adoption of a diverse set of behaviours by HCPs , 35. ThiThis was a convergent, parallel, mixed-method study in which three HCP subsamples participated in the tailoring of a professional training program. A first subsample helped tailor the first component of the program (the e-learning activity), whereas the second and third helped tailor the second component (a series of five evidence summaries). We used user-centered design approaches for tailoring each component, by improving user experience of the prototypes during several iterative evaluations that each comprised both quantitative and qualitative data collection .Details on the training program are reported elsewhere . Brieflywww.decisionbox.ulaval.ca/en/ (Series on Older Adults – Problems with Thinking or Memory).The second modality of the training program consisted of a series of five evidence summaries describing the options available to older adults living with neurocognitive disorders who face five important and frequent decisions that we identified in a previous study . These dWe recruited convenience samples of HCPs from any profession  who practiced in family medicine clinics or homecare services in the province of Quebec, Canada. Of the primary care settings invited to participate in the project (46 clinics and 8 homecare services), 20 agreed to participate . Figure At study entry, all study participants completed a questionnaire on their sociodemographic and professional characteristics. The study participants assigned to evaluate the e-learning activity also completed a questionnaire before and after exposure to the activity, to assess: (1) their preferred role in decision-making using the Control Preferences Scale , 48; (2)These participants also assessed the e-learning activity during a semi-structured think-aloud session that was screen-captured and audio-recorded using Flashback (Blueberry Software). One of two trained moderators  conducted these sessions. After each section of the training, the moderator asked participants about their perceptions on the content and learning strategies used, and recorded any usability issues. At the end of the session, the moderator also asked participants to comment on the main benefits and inconveniences of SDM, which allowed us to describe the factors influencing participants’ beliefs about the consequences of using SDM, one of the components of the theoretical framework guiding this work Fig. .Inspired by Susan Michie’s mapping approach, we modified the e-learning activity by adding/enhancing behaviour-change techniques targeting the factors likely to limit the adoption of SDM at each round , 52. To The study participants assigned to evaluate the DBs received emails with a link to access a web-based questionnaire to evaluate each DB, at a rate of one per week for five weeks. The DBs were also available on a website. The questionnaire served to (1) explain the purpose of the DB; (2) allow participants to access the DB under evaluation by clicking on a link; and (3) ask a series of questions about what they thought of the DB.Congruent with the theoretical approach adopted, we used two questionnaires to assess the psychological construct ‘intention’ and its potential determinants. We used the CPD-REACTION questionnaire, which is based on the integrated framework described above and was created as a routine assessment of the impact of continuing professional education on practice . We alsoWe sent two weekly reminders to participants who had not completed their evaluations.After each round, we used descriptive statistics to summarize participant ratings. We also imported the qualitative comments made in the open-text fields of the questionnaire into a specialized software program (NVivo), and two researchers  analyzed them using a thematic deductive/inductive qualitative analysis approach . We initAn interdisciplinary expert panel subsequently met to review the qualitative and quantitative findings, and identified strategies to improve the DB, so as to limit the identified weaknesses. The panel consisted of a graphic designer (JB), a human factors engineer (HOW), an epidemiologist (MAL), and four knowledge-translation researchers . The experts started by prioritizing each of the problems uncovered. Then, we determined the most appropriate solutions by considering the magnitude, frequency, and severity of these problems, and modified the DBs so that HCPs could explain the pros and cons of health options, as understood from the DBs, to patients and their caregivers.We used the same evaluation/tailoring process again in two more rounds, with new participants each time.We used descriptive statistical analyses to summarize participants’ characteristics, their interest in each DB topic, their level of intention, and the potential predicting determinants of their intention. We used SAS  to conduct these descriptive statistics, and a significance level of 0.05.After the entire series of evaluation/tailoring rounds, we integrated the quantitative and qualitative findings to generate conclusions on the factors influencing intention, and on the changes in users’ intention as the rounds progressed.One hundred and six HCPs from 20 clinics and homecare services located in 13 cities, agreed to participate in this study. A first cohort of 16 HCPs was assigned to help design the e-learning activity, a second cohort of 36 different HCPs helped design the DBs in a three-round evaluation process, and a third cohort of 54 helped design the DBs by providing comments in a fourth evaluation round. These cohorts are represented in Fig. In the three cohorts, 78% of participants were women, 60% were physicians, and 57% had fewer than 20 years of practice Table .Table 1CThe data that was used to tailor the training program is presented in this section and summarized in the Fig. Participants expressed their appreciation of the content and design of the e-learning activity on numerous occasions, and in every section Table . They esUser-centered design initially allowed discovering several weaknesses of the e-learning activity. The approach then made it possible to remedy these weaknesses and then evaluate the impact of the changes made during the next evaluation round. For example, participants mentioned several elements lacking clarity, either in terms of the training component or the content Table . We therSeveral other types of issues were corrected systematically, such as usability issues, or features perceived as irrelevant or less valuable such as the videos with avatars, the introduction, or the training module explaining the evaluation of evidence quality. We either removed these elements, or made them optional for people interested in learning more.We received several comments on the inapplicability of some of the examples across professions. Therefore, in the final version we diversified the professions displayed in the examples offered.The activity required participants to extract the benefits and harms of patient decision aids from a scientific abstract of the Cochrane systematic review on their impact. Participants found the exercise difficult. We chose to try to improve the format of the exercise first, to make it easier. We therefore simplified the text of the abstract as much as possible, added pictograms and smiley faces to help identify the benefits and harms, and translated it into French, to further understanding of the information. We received no further negative comments from participants on this exercise thereafter.Participants reported general interest in the topics covered in the decision boxes (DBs), with an overall mean interest of 80% (± SD 11%). The DB perceived as least interesting concerned the power of attorney (67% ± SD 29%), and the one perceived as most interesting covered non-pharmacological treatment to manage agitation, aggression, or psychotic symptoms (88% ± SD 11%). Participants mentioned that they liked the visual design of the DBs, because it facilitates their use in practice, especially the tabular format presenting risks and benefits/harms Table . SeveralThe limitations reported by study participants on the decision boxes allowed enhancing them to improve user experiences. Some of the modifications made were quite extensive. For example, several participants reported lower beliefs about their capabilities to use the DB to explain the pros and cons of health options to patients, because of the lack of accessibility to the services described in the DB Table . We judgIn some cases, we chose not to attempt to resolve the issues raised. For example, some participants felt the statistics were too hard for patients and their family caregivers to understand. Since probabilities in numerical formats are required to understand risks, we chose not to change the risks presentation in the DBs. Instead, we modified the e-learning activity by adding a module describing best practices to communicate risks to people with lower numeracy skills.Similarly to the e-learning activity, many of the participants’ comments were specific and easily addressed. For example, participants mentioned that the information was too dense, the terminology challenging, or some usability issues. We therefore adjusted the content of the DBs to reduce their length and complexity, and thereby limit the time needed to understand them, without compromising their meaning. We kept the use of jargon to a minimum, and added a glossary where we were unable to find simplified terminology. We also synthesized the scientific information on the pros and cons of the options in summary tables where possible. When there were more than two options for a given clinical situation, we added a table setting out the potential options for the decision at hand on the first page, including the estimates of the probabilities of impacts for each option, as well as the corresponding page detailing the impacts.A few physicians reported that the topic covered by DB #5 (Power of Attorney) was irrelevant to their professional role. In an earlier Delphi study , we idenWe explored participants’ beliefs about consequences, or usefulness of SDM process after completing the e-learning activity. Participants’ descriptions of the benefits and inconveniences of SDM Table , were veSome of the participants’ comments after reading a decision box also point to a lack of knowledge about SDM, for example that DBs are of little use when stating their opinion or making recommendations Table . To imprVisual examination of the quantitative descriptive results suggests that there was no change in participants' intention to adopt SDM after their participation in the e-learning activity. Intention was relatively high and stable across the three rounds, with a mean level of 6.8, on a scale ranging from 1 (low intention) to 7 (high intention)  the use of theory and user-centred design makes it possible to tailor the training program to users’ needs, the effectiveness of this approach being better assessed through qualitative than quantitative techniques; (2) each of the two studied training components appears essential to supporting the adoption of SDM; and (3) training should be tailored to each profession.The theory-based and user-centered design approach enabled us to identify the strengths and weaknesses of the two training components, and to improve their content, design, navigation, and usability, while solving any technical issues.The theory-based approach allowed focusing on the known determinants most likely to influence participants’ intention to adopt SDM, a strategy previously recommended in the Behavior Change Wheel Model  and in aFurthermore, the user-centered design approach allowed us to focus on the learners’ perspective of the problem by validating our interpretation of their assessments during the next evaluation round. Several earlier studies have described the benefits of user-centered design to create evidence-based information , 57–61, However, although the use of theory within an iterative user-centered design approach makes it possible to develop high-quality training, it required considerable resources and a continuous commitment from the research team, as previously highlighted in another study . We alsoFindings from this study and from two of our other recent study on user-centered design , 65, ledThe CPD-reaction questionnaire to assess the DBs was also limited in that it was already near to the maximum after a single evaluation round, leaving very little room to measure improvements through subsequent rounds. These results are consistent with those of previous studies that reported a strong intention of clinicians to support women in breast cancer decision-making  and a hiOverall, our study revealed that each of the two training components was essential for professionals to adopt SDM Table . FirstlyDespite the interprofessional context of care, participants asked that training be adapted to their specific professional role. Indeed, both in the evaluations of DBs and e-learning activity, professionals asked that the clinical examples provided during training match each of their professions. This finding is congruent with the observations that, for continuing professional development to be valued, it must meet the individual needs of professionals, the populations they serve, and the organizations in which they work . This maWe also found that, for clinicians to consider using the DBs, they should list the health and social services needed to implement the options presented, along with the related contact information. This implies that any decision aid should be tailored to the area where it is distributed.In each evaluation round, we observed significant associations between intention and two of its primary determinants, namely beliefs about consequences (or attitude) and social influence. These findings concur with those of an extensive review of the determinants of clinician behaviours, to the effect that said determinants are among those most related to intention . This alThe use of a non-random study sample may also have affected the results by introduction of selection bias. For instance, we may have recruited HCPs with higher intentions to adopt shared decision-making compared to the overall population of professionals who practice in primary care settings. This selection bias could then be responsible for the high intentions we observed in the participants from the start of the study.The sample sizes were relatively small for the planned quantitative evaluations. Our conclusions were limited by the fact that the quantitative scores showed a ceiling effect. Nevertheless, the qualitative evaluation of each component allowed for a rich description to complement the quantitative results.The literature indicates that professionals’ intention alone does not always predict SDM adoption  or the eWe used convenience samples, so the participants who accepted to participate may be different from the general population.Most participants were physicians and nurses, and none practiced in rural areas. Our conclusions may therefore not apply to other types of professionals, or to those practicing in rural areas. Moreover, our findings may have been different if more social workers had participated in the study, especially since the DB topics concerned social aspects of care. We also acknowledge the high proportion of participants who were women (78%); however, this proportion reflects the overall gender balance among HCPs in Quebec .Some of the comments received during the evaluation of the DBs via the open-text field of the web-based questionnaire were hard to interpret and would have required further discussion with the participants to clarify their opinions.A theory-based design approach for continuing professional development interventions on shared decision-making appeared particularly useful to identify the most important determinants of learners’ intentions to use SDM in their clinical practice. A user-centered approach allowed focusing on learners’ perspectives of the problem by validating our initial interpretations of their assessments during the subsequent evaluation round. Participants’ qualitative assessments of the programme provided greater insight into the weaknesses of the training and allowed us to better adapt the program than their quantitative assessments. We also observed a complementarity of the two studied training components, with the e-learning activity providing knowledge on the theory and practice of SDM, and the decision boxes providing the practical information needed to discuss specific health questions with older patients with neurocognitive disorders and their family or friend caregiver. In future research, it would be interesting to compare a group exposed to training tailored using such a theory-based user-centered design approach, to a group exposed to non-tailored training, to assess whether this design approach actually creates a more effective training program to increase adoption of SDM by health professionals. Further research is also needed to evaluate this two-component training program and to highlight the barriers and facilitators for its completion, in order to optimize its implementation and ensure effective uptake and use of SDM.Additional file 1. Participants’ level of intention to adopt shared decision-making and levels of the potential predictors of this intention at baseline and after each evaluation round of the e-learning activity .Additional file 2. Participants’ level of intention to use what they learned through the DBs to explain the pros and cons of health options to patients, and levels of the potential predictors of this intention at each evaluation round .


This work presents a table cleaning and inspection method using a Human Support Robot (HSR) which can operate in a typical food court setting. The HSR is able to perform a cleanliness inspection and also clean the food litter on the table by implementing a deep learning technique and planner framework. A lightweight Deep Convolutional Neural Network (DCNN) has been proposed to recognize the food litter on top of the table. In addition, the planner framework was proposed to HSR for accomplishing the table cleaning task which generates the cleaning path according to the detection of food litter and then the cleaning action is carried out. The effectiveness of the food litter detection module is verified with the cleanliness inspection task using Toyota HSR, and its detection results are verified with standard quality metrics. The experimental results show that the food litter detection module achieves an average of  Due to long working hours, low wages, unwillingness to work as a cleaner, workforce shortage has been a constant problem for food court cleaning and maintenance tasks in recent times . RecentlVarious techniques have been developed for cleaning robots to recognize the different class of litter  and compute the cleaning strategy. Among them, computer vision-based techniques are widely used in cleaning robots for recognizing the litter and compute the cleaning action ,17,18,19Deep learning-based object detection is an emerging technique. Deep Neural Network architecture can be modified and optimized to solve different complex tasks in computer vision such as object classification, object detection, and object segmentation applications. It has been widely used in the robotic filed to detect the obstacles , pick anDeveloping a real-time planner algorithm is another critical challenge of cleaning robot applications. In the planner, module path planning is a key component that plays the vital define the cleaning path according to food litter detection. In literature, various methods are available for autonomous cleaning robots for solving the path planning problems such as Dijkstra’s algorithm, D-star, A-star, rapidly exploring random tree (RRT), and probabilistic roadmap (PRM) technique. The pros and cons of each scheme and path smoothing techniques for autonomous Robot Navigation have been described in Reference . BecauseMotivated by the works mentioned above, this work proposes the deep learning-based food litter detection system and a path planner algorithm for the Toyota Human Support Robot (HSR) ,32 to ac(1) The 2D location from the output of the lightweight Deep Convolutional Neural Network (DCNN) based litter detection model is combined with the depth data to yield the 3D location of the food litter on top of the table.(2)The proposed framework can provide a high confidence level in classifying various types of litters like liquid, solid.(3) The planner algorithm computes the cleaning mode and find the cleaning path for removing the food litters from the table. The planning and execution behaviors of how the robot cleans the dirty table are inspired by the actual cleaning activities conducted by the human. The real-time planner uses the Depth-First Search (DFS)  and Prob(4) The proposed method is validated in Toyota HSR robot and its efficiency has been verified with standard quality metrics.The rest of the paper is organized out as follows: The proposed CNN network contains 16 layers with 9 convolutional layers and 6 pooling layers. The network is built on python with DarkFlow as a backend. DarkFlow is an open-source object detection algorithm that can be used for object detection and localization. The advantage with DarkFlow is that the architecture of the network can be altered, that is, changing the activation functions, network layers, and training using custom objects. The number of convolutional and pooling layers has been decided to make sure that the network does not cause any overfitting. The huge number of hidden layers could, in turn, cause overfitting issues. We started with adding convolutional and hidden layers until we receive a good enough F-1 score . Figure f and g are two variables that are involved in convolution. f is the input and g is the filter function.Convolutional layers are used to extract higher-level features that could be used for performing some complicated classification. In a nutshell, the convolutional layer performs the operation of the convolution between its input and filter of the desired size. The number and size of the filters are given by the user as a parameter to the layer. The Equation  describiI is input image matrix, K is the filter size, P is zero padding and S is the stride length.The output is a two-dimensional activation map that provides the response of convolution at each spatial position. Based on the size of the filter and the size of the input, the size of the output can be determined using Equation .(2)outpThe first convolutional layer takes in images of size Max poolingAverage poolingPooling layers are generally designed to prevent over-fitting and, by applying non-linear down-sampling, it reduces the size and robustness to expedite computation. Zero-padding is not performed within the pooling layer as it would work against the purpose of the layer. The predominantly used pooling methods are:Batch size = 64Learning Rate = 0.001policy = stepsThreshold = 0.5Max pooling: The maximum adjacent value is taken from the input image based on the position of pixels. This is implemented through input channels. Median pooling: The average of all values within the region covered by the filter is taken and assigned as the output value. The other important settings for the network layers are the hyper-parameters, which were set as follows:P—binary value which determines if there is an object of interest in the image, IOU) method  in the color image. The location of the litter object is wrapped inside the bounding box. The idea behind the bounding box is that each image is divided into segments with an identical area, and a target vector has been generated for them (Equation ). The boEquation ) is usedIOU thresholds were tested out during the experiments, and the best one has been picked. In our case, we have chosen the IOU threshold to be 0.5, which has been widely reported in the literature and provide more stable results. This means when evaluating the output; if the IOU calculated from the predicted bounding box and the actual bounding box is equal to more than 0.5, we would consider that as a correct output, whereas anything below 0.5 is considered as the wrong predictions of the bounding box coordinates. The average IOU matching calculated over all the bounding boxes in the test set is 0.7044, with an overall confidence 0.589.For fixing the various The planner module is developed for accomplishing the table cleaning task through HSR. The planner has two functions, namely finding the cleaning method and constructing the cleaning path. Two cleaning methods are adopted for the table cleaning task, which includes sweeping and wiping, where the sweeping method is used to clean the solid and semi-solid food litter, and wiping mode is used to remove stains. Straight move or grouping based sweeping action is adopted for cleaning solid, and semi-solid food litter where grouping based sweeping is used for cleaning the multiple litters in one shot and straight move is used for removing the separated food litters. Further, zig-zag cleaning action is considered for wiping the stains. P is the feasible path for connecting the nodes. The path planner constructs two-way path, such as direct path planning and grouping based path planning.To construct the path, the planner uses the probabilistic road map algorithms. Here, the DFS technique generates the initial path from the detected bounding region, then PRM scheme has been applied for fine-tune path according to HSR mobility function. The key element of path planning function q is new generated node, Initially, the planner starts the path planning by exploring the path which starts from the leftmost upper node, and that node is marked as quations  and 6)..qstart. Algorithm 1 Cleaning Path Generation1:Step 12:N is an array holds all the nodes position3:P is an array used to store the cleaning path4:N);     Set uppermost left nodes as 5:load 6:def_goal(litter collection point);   the function load the litter collection point as 7:Step 28:while cleaning path between do9:Q,  are variable used for store the temporary value 10:fordo 11:dfs_paths(  Q= 12:end for 13:backtrack.find(Q);     the function find the backtrack node in the explored path 14:group.find( 15:def_PRM_node_connect(R);   PRM function connect the nodes according to threshold function R Path = 16:plt.plot , operating as a ROS slave. The slave block contains a control block and a litter detection framework. The control block governs the interface between the deep learning framework and ROS. It wraps the data received from the deep learning framework into the ROS compatible messages and sends it to the primary system. Similarly, it unwraps the ROS message into a suitable format that is required by the deep learning framework. Both the primary and the secondary systems are connected over Transmission Control Protocol/Internet Protocol (TCP/IP) and share all the ROS topics.To execute the cleaning task, the primary system enables the RGB-D sensor and collect the RGB-D stream of image data to the secondary system. The GPU in the secondary system, which is running the deep learning framework, takes in the input data and performs the object detection and classification task and returns detected information, which contains the coordinates and types of litters.K with focal length Since the boundary box of the detected object is in the image coordinate frame, the knowledge of the point in the 3D world coordinate system is essential for the robot to manipulate the arm and grabber. The pixel coordinates in the color image of the boundary box, including the center, top left, bottom left, and top right, are converted to 3D coordinate in the world frame in meter from the robot base frame. Then the 3D coordinate of the center of boundary box becomes the representative for each object of interest. In this paper, we assume that the RGB-D camera is set up so that its Field of View (FOV) covers the whole table, which needs to be cleaned. To do the 2D to 3D conversion, projective geometry is used for mapping the point in the image plane to 3D point in the world frame. Intrinsic and extrinsic parameters of the camera are estimated by the camera calibration method . TranslaUpon receiving the control information, the primary system then generates the cleaning map for executing the cleaning action through arm motion. After the path has been derived from DFS and PRM techniques, then Moveit  service The HSR RGB-D camera is used to capture the litter data set. The specification of RGB-D camera is given In addition to that, the CNN learning rate was improved, and over-fitting was prevented by applying data expansion to the captured images. Data expansion applies simple geometrical operations on images like scaling, rotating, shifting, and flipping to increase the number of samples in the data set. Further, the images are resized to K-fold cross-validation is adopted in this work to validate the dataset. In this method, the dataset is divided into k subsets, and K-1 is used for training. This work uses 10 fold cross-validation. The images shown are obtained from the model with the highest accuracy.The cleanliness inspection part has been assessed through food litter detection on the dining table. The video demonstrating how HSR detects litters then executes the cleaning actions are provided as Equation ) metric This section describes the comparative analysis of proposed work with other existing case studies in the literature. The comparison analysis has been performed based on CNN and non-CNN methods used on different cleaning robot applications for the detection of different kinds of litter . In the literature, Faster RCNN with ResNet or inception, Mobilnet V2 SSD , and YOLIn contrast to a non-deep learning model, Deep learning techniques offer a significant advantage in scenarios when sufficient data is available. These techniques are able to autonomously extract features from images, which allow them to learn features and patterns which are difficult to figure out statistically. CNN architectures are specifically good for extracting features from images since they use a combination of pixels next to each other for features.The efficiency of the planner module has been tested through cleaning path generation and table cleaning tasks using generated cleaning path. Two experiments are conducted to assess the table cleaning function. In the first experiment, Robot cleans the solid food litter on the table. The second experiment is set to clean the presence of both solid waste and liquid spillages. To carry out the experiments, firstly, the litter detection module was run to recognize the food litter, then the planner was executed to generate the cleaning path. The The test results indicate that the proposed algorithm can satisfy the real-time requirement. It is more suitable for executing the cleanliness inspection and table cleaning application using HSR.In this work, the cleaning actions depend upon the precise value of the depth information acquired by the RGB-D cameras, which have a restricted resolution. Even though the use of cloth has some advantages, the expected movement of the litter is not reached by the actions. Further, The Toyota arm has 5 degrees of freedom rotation, because of which the arm planning becomes complicated by involving the robot base to reach certain points on the table. This can be solved by adding an additional degree of freedom to the robotic arm. In our feature work, we plan to improve the quality of the RGB-D vision system and HSR motion planner function, which helps to predict the object position and orientation accurately and improve the cleaning efficiency. The computing power of the existing CPU installed on the HSR is relatively slow, an extra computing board with additional hardware computing power can be used to expedite the detection and identification of food litter.Developing a cleaning robot platform should have two critical features. The environment should be of real-world setting, and the platform should be able to reach every nook and corner of the cleaning region. This proposed cleaning platform Toyota HSR is developed for assisting humans in a general setting, and proven to be efficient in conventional environments. The proposed framework is tested on a common food-court like setting so that the real-world implementation can be without any issues. Unlike the earlier results, the proposed work focuses on classifying the litter into various types, for which the cleaning process is different. Moreover, the classification module is augmented with an optimal path planning module and control module to achieve high efficiency. The efficiency of the detection framework is evaluated through cleanliness inspection, and its detection accuracy was measured through standard quality metrics. The experimental results show that the proposed cleaning framework detects most of the food litter with highest detection accuracy among the considered methods, and HSR cleaning part takes an average of reasonable time to clean the three to five food litters on the table. The proposed method can be used to clean vertical surfaces like glass panels, windows in homes, among others. The module can be used to identify litters not only on tables but also on the floors. This scaling of this application can open new doors in the cleaning industry.


Lower middle-income countries are not heavily presented in global media on COVID-19. Bangladesh, a densely populated nation, have conducted only 1759 tests in last one month with 9 reported deaths. The decisions of cluster-wise lockdown or social distancing, or even preparing the health system to respond to the pandemic are made without the availability of adequate data. This is fuelling panic in local community and giving an obscure picture in the global data.th of April 2020, only 88 diagnosed cases and 9 deaths have been associated with COVID-19 with first case detected on the 8th of March [With major discussions around COVID-19 pandemic focused on China, USA and Europe, few media reports have acknowledged that it has also silently paved its way into lower middle-income countries. In Bangladesh, as per the 5th of March and the 5th of April, which included some hard-hit countries such as Italy, a total of less than 3000 tests in the first 4 weeks of transmission were likely insufficient to illustrate the viral transmission in Bangladesh [In a country of 164 million and with 155 898 overseas arrivals between the 8Due to the shortage of test kits, authorities had to set strict eligibility parameters for the test. Thus, only those with direct contact with foreign returnees were tested. There was a refusal to consider the possibility of any community transmission, which created confusion. Furthermore, there was a rather strong opposition to the development of rapid diagnostic kits on diagnostics attributes such as sensitivity and specificity, without careful consideration on affordability and subsequent accessibility. This problem has contributed to a reduced availability of baseline information on the degree of transmission of COVID-19 in Bangladesh. The situation forced the government to enforce measures without being able to rely on substantial data.In Bangladesh, the COVID-19 crisis was therefore a game of “catching up”. The government has undertaken measures iteratively, in response to the situational development in the country. It promoted hand hygiene and other protective measures, such as social distancing, airport screening, quarantine wherever necessary, and restricted movement during government holidays . As the The crisis has already started in the form of limited availability of disinfectants and personal protective equipment in the health care facilities. This led to elevated perceived risk on COVID-19 infection in the hospitals. Many facilities are now refusing to admit patients who suffer from other routine complications. Lack of testing also limited quarantines of close contacts of the suspected cases. These resulted to country-wide fear and an avalanche of reports on deaths with pneumonia/flu-like symptoms.th to the 25th March) received over 420 629 phone calls to request testing, but was only able to test 0.3% of those who raised a concern over their status [The fear soon turned to a panic, as some villages or entire local areas were locked down by local authorities over untested possible COVID -19 related deaths. The only testing facility during the first 3 weeks of the crisis  . On the Also, decision making based on the current prevalence of COVID-19 can undermine the issue of the health system preparedness without a careful understanding of the dynamics of COVID-19 evidenced worldwide. It can include premature lifting of restricted movement and sanctions and lower concentration of testing and contact tracing in the rural areas. These can impact not only the health system planning and response in terms of financial input, but also appropriate service delivery and required supply to address the COVID-19 due to the changing dynamics of population density in the event of reduced sanctions. Additionally, it can lead to a surge on heightened risk of exposure due to minimized health response priorities, efforts and initiatives - such as contact tracing and testing in the rural areas and considering their movement to the urban areas.th to the 30th March). This could be because either Bangladesh is extremely lucky, or this scarcity of databased on appropriate testing now obscures an ominous death toll that is to follow.While pandemics hardly give opportunities to many countries to prepare, developed nations will have the data to at least trace their progress and assess their position on the curve. Unfortunately, the curve for Bangladesh seems flat with only one case detected in a period of 72 hours (the 28


Grey wolf optimizer (GWO) is a meta-heuristic algorithm inspired by the hierarchy of grey wolves (Canis lupus). Fireworks algorithm (FWA) is a nature-inspired optimization method mimicking the explosion process of fireworks for optimization problems. Both of them have a strong optimal search capability. However, in some cases, GWO converges to the local optimum and FWA converges slowly. In this paper, a new hybrid algorithm (named as FWGWO) is proposed, which fuses the advantages of these two algorithms to achieve global optima effectively. The proposed algorithm combines the exploration ability of the fireworks algorithm with the exploitation ability of the grey wolf optimizer (GWO) by setting a balance coefficient. In order to test the competence of the proposed hybrid FWGWO, 16 well-known benchmark functions having a wide range of dimensions and varied complexities are used in this paper. The results of the proposed FWGWO are compared to nine other algorithms, including the standard FWA, the native GWO, enhanced grey wolf optimizer (EGWO), and augmented grey wolf optimizer (AGWO). The experimental results show that the FWGWO effectively improves the global optimal search capability and convergence speed of the GWO and FWA. Finding an optimal solution in high-dimensional complex space is a common issue in many engineering fields . When soInspired by the leadership hierarchy and the mechanism of hunting of grey wolves, Gray Wolf Optimizer, a new meta-heuristic optimization algorithm, was proposed by Mirjalili in 2014 . The GWOWhen searching for the optimal value in a high-dimensional space, the single meta-heuristic algorithm always has some disadvantages, such as a low accuracy, poor generalization ability, and poor local optima avoidance ability. The hybrid algorithm utilizes the differences between the two optimization algorithms, combines their advantages, and makes up for shortcomings to improve the overall performance of solving complex optimization problems . The solAs mentioned above, GWO advances itself strongly on exploitation. However, in some cases it converges prematurely and gets trapped in a local optimum. FWA not only has a high exploration capability, but also has the disadvantage of a slow convergence speed compared to the recent algorithms. This paper proposes a new hybrid algorithm which combines the exploration ability of FWA with the exploitation ability of GWO to increase the convergence characteristics. (1)A novel hybrid algorithm based on GWO and FWA is proposed.(2)The proposed algorithm is tested on 16 benchmark functions with a wide range of dimensions and varied complexities.(3)The performance of the proposed approach is compared with standard GWO, FWA, Moth Flame Optimization (MFO), Crow Search Algorithm (CSA) , improveThe major work of this paper is summarized as follows:The rest of paper is arranged as follows: The Grey Wolf Optimizer (GWO) is a meta-heuristic algorithm proposed by Mirgalili et al.  in 2014.The GWO algorithm is a mathematical model based on the social hierarchy of wolves. The fittest solution found is considered as the alpha  and (2):The grey wolves can easily encircle the prey with the ability to recognize its location. The whole hunting process is usually led by the alpha. However, in a complex search space, it is impossible to get the location of the prey at the beginning. Therefore, GWO consider that the first three best solutions, alpha, beta, and delta, have more information about the location of the prey. Then, the other wolves update their positions based on these three positions, as shown in The mathematical equations of this phase are as follows:When the prey stops moving, the grey wolf completes the hunting process by attacking. In order to mimic the process of the grey wolf approaching the prey, the GWO algorithm causes According to Equation (3), (1)N fireworks are randomly generated in the search space, and each firework represents a feasible solution.(2)Evaluate the quality of these fireworks. Consider that the optimal location may be close to the fireworks with a better fitness; these fireworks get a smaller search amplitude and more explosion sparks to search the surrounding area. On the contrary, those fireworks with a bad fitness will get fewer explosion sparks and a larger search amplitude. The number of explosion sparks and the explosion amplitude of fireworks are calculated as shown in Equations (9) and (10):(3)k:To guarantee the diversity of the fireworks, another method of generating sparks, Gaussian explosion, is designed in FWA. For the randomly selected fireworks, a number of dimensions are randomly selected and updated, as shown in Equation (12), to get the position of a Gaussian spark at the dimension k to a new position:k, and k.The generated explosion sparks and Gaussian sparks may exceed the boundaries of the search space. Equation (13) maps the sparks beyond the boundary at dimension (4)Inspired by the behavior according to which fireworks burst into sparks in the night sky and illuminate the surrounding area, Tan and Zhu proposed the Fireworks Algorithm (FWA) in 2010. In FWA, a firework is considered as a viable solution in the search space of the optimization problem. Furthermore, the process of sparking fireworks is deemed as the process of searching the neighborhood of these fireworks. The specific steps of the fireworks algorithm are as follows:In this selection strategy, if there are many other locations around The execution flow of the FWA algorithm is shown in Algorithm 2.As mentioned in A random value The pseudo code of the hybrid FWGWO algorithm is shown in Algorithm 3.The time complexity of FWGWO is mainly determined by the population size In this section, 16 benchmark functions with different characteristics are used to evaluate the proposed FWGWO hybrid algorithm. The experiment results are compared with nine other algorithms to verify the superiority of FWGWO.A total of nine algorithms, including IPSO, PSO, BBO, CSA, MFO, FWA, GWO, AGWO, and EGWO, were selected for comparison with the FWGWO algorithm proposed in this paper. These nine algorithms contain both classical algorithms and new algorithms proposed in recent years. The parameter settings of these algorithms are shown in Sixteen benchmark functions listed in Three performance indicators, the mean fitness, fitness variance, and best fitness, are selected in this paper to evaluate the results of the experiments. The mathematical equations are defined as follows:In addition, a nonparametric statistical test, Wilcoxon’s rank-sum test , is utilThirty independently repeated experiments were implemented using the 10 algorithms mentioned above on each benchmark function. Furthermore, the variances of the results of these 30 independent experiments are also smaller. As can be seen in In The mean running time of 30 repetitions has been shown in Wilcoxon’s nonparametric statistical test is conducted at the 5% significance level in order to determine whether the FWGWO provides a significant improvement compared to other algorithms or not. The results of different algorithms on the benchmark function were employed to test the Wilcoxon rank sum, and In this paper, a hybrid algorithm FWGWO based on Grey Wolf Optimizer (GWO) and Fireworks Algorithm (FWA) is proposed for the optimization of multidimensional complex space. The FWGWO algorithm combines the good exploitation ability of GWO with the strong exploration ability of the FWA algorithm. In order to balance the exploitation with the exploration, an adaptive balance coefficient is employed in this algorithm. The probability of exploitation or exploration is controlled by the balance coefficient. By changing the balance coefficient, the FWGWO algorithm can avoid the local optimal value as much as possible and has a fast convergence speed. In order to verify the performance superiority of the FWGWO algorithm, the FWGWO algorithm was tested 30 times with IPSO, PSO, BBO, CSA, MFO, FWA, GWO, AGWO, and EGWO algorithms on 16 benchmark functions, and their test results were compared with each other. The compared results show that the FWGWO algorithm has a better global optimization ability and faster convergence speed compared with other algorithms. In addition, the Wilcoxon rank sum test was used to test the optimization results. The test results show that the FWGWO algorithm has a significant improvement compared to other algorithms.This paper presents a new method for combining two algorithms. In the future, this method could be used in combination with other enhanced algorithms. Furthermore, the FWGWO can be applied to solve single- and multi-objective optimization problems like the feature selection problem.


This review surveys physiological, behavioral, and morphological evidence converging to the view of the cerebro-cerebellum as loci of internal forward models. The cerebro-cerebellum, the phylogenetically newest expansion in the cerebellum, receives convergent inputs from cortical, subcortical, and spinal sources, and is thought to perform the predictive computation for both motor control, motor learning, and cognitive functions. This predictive computation is known as an internal forward model. First, we elucidate the theoretical foundations of an internal forward model and its role in motor control and motor learning within the framework of the optimal feedback control model. Then, we discuss a neural mechanism that generates various patterns of outputs from the cerebro-cerebellum. Three lines of supporting evidence for the internal-forward-model hypothesis are presented in detail. First, we provide physiological evidence that the cerebellar outputs (activities of dentate nucleus cells) are predictive for the cerebellar inputs [activities of mossy fibers (MFs)]. Second, we provide behavioral evidence that a component of movement kinematics is predictive for target motion in control subjects but lags behind a target motion in patients with cerebellar ataxia. Third, we provide morphological evidence that the cerebellar cortex and the dentate nucleus receive separate MF projections, a prerequisite for optimal estimation. Finally, we speculate that the predictive computation in the cerebro-cerebellum could be deployed to not only motor control but also to non-motor, cognitive functions. This review concludes that the predictive computation of the internal forward model is the unifying algorithmic principle for understanding diverse functions played by the cerebro-cerebellum. The cerebellum has developed in the sensory domain of the central nervous system (CNS) as evidenced by the fact that it has emerged in the alar plate  of the neural tube of the rhombencephalon of primitive jawless vertebrate such as myxinoids (hagfish) or petromyzonts . Section “Functional Evidence for Cerebellar Forward Model” summarizes neural and behavioral evidence that the cerebellum performs predictive computation. Section “Anatomical Structure Supporting for Cerebellar Forward Models” surveys anatomical structures of the cerebro-cerebellum that potentially scaffold the forward-model computation. Section “Cognitive Functions and Cerebellar Forward Models” branches out to speculate a possible role of the cerebro-cerebellum in higher cognitive functions from the viewpoint of the internal forward model. Finally, Section “Remaining Issues About Cerebellar Internal Models” concludes the internal-model hypothesis of the cerebellum and enumerates unresolved issues toward the goal of understanding the cerebro-cerebellum.One critical problem in biological motor control is that afferent sensory signals have inevitable temporal delays in reaching the central nervous system. In other words, the brain always observes “the past” of its own body and environments. Visual signals, for example, arrive at the primary visual cortex about 30 ms later and at the parietal cortex about 80 ms later than an onset of a visual stimulus  model provides a unified framework that integrates diverse computational processes in motor control and motor learning such as the internal forward model, Kalman filter, and feedback control . First, the Kalman filter is a recursive method to estimate the current state that integrates a predicted state from an internal forward model and sensory afferents. An optimal estimator is a weighted sum of a predicted state and sensory afferents determined by Kalman gain according to their relative accuracies (or variances). The computation of Kalman gain requires the accuracy of a predicted state from an internal forward model for the optimal tradeoff. Second, the optimally estimated state propels the feedback controller to generate appropriate motor commands, which is a product of the estimated state and the feedback gain matrix. Here, the computation of the feedback gain matrix requires information about the body dynamics and the control cost so that the body is guided to achieve the task goal with a minimum amount of energy. Again, the internal forward model contributes to the computation of feedback gain. Accordingly, the computation of gain matrices in the OFC model necessitates the knowledge of dynamics implemented in an internal forward model.To summarize, an internal forward model plays at least the three key roles in motor control and motor learning: (1) state prediction for compensating the delay in sensory feedback in online motor control; (2) state prediction for computing a prediction error between a predicted outcome and an actual outcome in motor learning; and (3) computation of Kalman gain and feedback gain. Theoretically speaking, it is possible that these roles are solved collectively by a single, unified forward model or separately by multiple, distributed forward models. Convergent lines of evidence suggest that the cerebellum is a neural correlate of an internal forward model, as discussed below.An internal forward model contributes not only to state prediction for a fast movement but also to the computation of prediction error for motor learning and of gain matrices for the OFC model. A wide range of previous studies converge to the cerebellum processes as the loci of internal forward models  of DNC activity  cells], two physiological mechanisms have been proposed. One mechanism is the recruitment of a post-inhibitory rebound excitation . We proposed that these signs may be caused by malfunctions of the two output modes  contained predictive information about the future input to the cerebellum (MF firing rates at time t+t1), which in turn supports the internal-forward-model hypothesis of the cerebellum. Since the MF activities analyzed here originate from the motor cortex, the cerebellar output predicts the future state of the motor cortex, which in turn returns to the motor cortex through the thalamus.We then proceeded to directly test the internal-forward-model hypothesis by examining whether the cerebellar output could predict the cerebellar input in the future. Following the linear reconstructions of PC and DNC firing rates, the MF firing rates at time We note that the linear equations derived from the experimental firing rates resemble those of optimal estimation known as Kalman filter. Based on formal correspondence between the experimentally derived linear equations and the Kalman-filter equations, we speculate the following computational steps in the cerebellar circuits : (1) theBr/Kr ratio, defined below; Lee et al., rB and rK stand for viscous and elastic coefficients, respectively, estimated from wrist movements of a subject with a canonical correlation analysis, and the Br/Kr ratio evaluates the ratio of velocity control to position control. The movement kinematics of wrist was decomposed into a slower  F1 component and a faster  F2 component, and the Br/Kr ratio was computed for F1 component and F2 component, respectively. The F1 component belonged to the same frequency range of the target motion and encoded both velocity and position (higher Br/Kr ratio) of the target motion. This kinematic formula of the F1 component appeared optimal to synchronize the wrist movement with the target motion in a predictive manner. Indeed, for the control subjects, the F1 component lagged behind the target motion for about 60 ms  than in the control group  in the patient group  component had a significant difference between the control and patient groups. Namely, the Br/Kr ratios of the F1 component of the patient group , suggesting difficulty in recruiting velocity control in cerebellar patients. In contrast, Br/Kr ratios of the F2 component were comparable for both groups . Taken together, our results support the hypothesis that cerebellar patients have an impairment in the forward-model prediction while relatively maintaining corrective control in response to sensory feedback.We further demonstrated that the Br/Kr ratios of the F1 component and performance/accuracy of pursuit movement in the cerebellar patients and the control subjects  and plays an essential role in the prediction step, and another MF input to DN conveys sensory feedback information and plays a critical role in the filtering step. Therefore, the conventional diagram of the cerebro-cerebellum in which the same MF projecting to the cerebellar cortex and DN is not compatible with our proposal of Kalman-filter computation in the cerebellum.Here we argue morphological substrata supporting our findings of predictive computation presented in Section “Behavioral Evidence for Cerebellar Internal Model”. The conventional circuit diagram of the cerebro-cerebellum  depicts without collaterals to DN. Indeed, Kelly and Strick . Therefore, it was concluded that PCs and DNCs received the projections from distinct populations of MFs, thereby fulfilling the requirements of the Kalman-filter model.Consistent with the anatomical observations, there were two functionally distinct populations of MFs in our data set , the other regions of the cerebellum could receive input projections in different ways  to DN  to analyze cerebellar fMRI task activation  and resting-state functional connectivity. They demonstrated novel aspects of the functional topography of the human cerebellum. There were two distinct representations (lobules I-VI and lobule VIII) of motor activation that was consistent with prior studies, in particular with the above-mentioned trans-neuronal tracing study in the monkey  the main MFa input to the cerebro-cerebellum is originated from a non-motor area and relayed by PN, and (2) the filtering MFb input is originated from a distinct cortical or subcortical source and relayed by a non-PN nucleus that has significant collateral projection to DN  , top, wh x(t+t1) , bottom.Our recent study arguably demonstrated that the cerebellar output (activities of DCs) at current time was predictive about the cerebellar input (activities of MFs) at a future time, supporting the hypothesis that the cerebellum performs the computation of an internal forward model (Tanaka et al., We would like to conclude this review article by enlisting two unanswered questions that we think important for promoting a better understanding of the computational functions and the neural mechanisms of the cerebellum. First, how is a predictive activity computed in the cerebellum utilized in the cerebral cortex? Our previous study reported that MF activities, the input to the cerebellum, shared response properties with the activity of neurons in motor areas, implying that activities of the cortical neurons are copied into the cerebellum as an input (Tomatsu et al., SK, HT, TI and JL conceived the research. HT and SK drafted the manuscript. HT, SK, TI and JL finalized the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.