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Standardizing clinical laboratory test results is critical for conducting clinical data science research and analysis. However, standardized data processing tools and guidelines are inadequate. In this paper, a novel approach for standardizing categorical test results based on supervised machine learning and the Jaro-Winkler similarity algorithm is proposed. A supervised machine learning model is used in this approach for scalable categorization of the test results into predefined groups or clusters, while Jaro-Winkler similarity is used to map text terms into standard clinical terms within these corresponding groups. The proposed method is applied to 75062 test results from two private hospitals in Bangladesh. The Support Vector Classification algorithm with a linear kernel has a classification accuracy of 98%, which is better than the Random Forest algorithm when categorizing test results. The experiment results show that Jaro-Winkler similarity achieves a remarkable 99.93% success rate in the test result standardization for the majority of groups with manual validation. The proposed method outperforms previous studies that concentrated on standardizing test results using rule-based classifiers on a smaller number of groups and distance similarities such as Cosine similarity or Levenshtein distance. Furthermore, when applied to the publicly available MIMIC-III dataset, our approach also performs excellently. All these findings show that the proposed standardization technique can be very beneficial for clinical big data research, particularly for national clinical research data hubs in low- and middle-income countries.

Upper limb dysfunction (ULD) is common following a stroke, spinal cord injury, trauma, and occupational accidents. Post-stroke patients with ULD need long-term assistance from therapists for their rehabilitation, which generally occurs at the hospital or outpatient clinic. Physical therapists are unavailable because of geographical, financial, and scheduling concerns, and continuity of care needs to be improved due to the need to travel to multiple locations for therapy. As a result, providing specific, tailored therapy programs is challenging due to the absence of feedback and real-time monitoring. An effective telerehabilitation system can address this issue and is more cost-effective for healthcare providers and patients than traditional inpatient or person-to-person rehabilitation. Remotely operating robotic devices and using advanced technology improves patient and healthcare provider safety and reduces injuries. In this study, we developed a novel telerehabilitation framework for rehabilitation robots utilizing PTC’s Industrial Internet of Things (IIoT) platform to remotely provide robot-aided therapies for individuals with ULD. With the developed telerehabilitation framework, an operator can teleoperate the rehab robots to deliver Upper-limb (UL) exercises via an Augmented Reality (AR) based graphical user interface (GUI). This AR platform communicates bidirectionally using ThingWorx IIOT. It leverages the digital twin (DT) structure facilitated by Vuforia studio to visualize the physical robot motions happening in remote places. The telerehabilitation framework was validated through a commercially available robot (xArm 5), an exoskeleton (SREx), and an end-effector type rehabilitation robot (DMRbot) developed at Biorobotics Lab, UWM. The experiment results show that the telerehabilitation system can successfully provide UL rehab exercises in 2D and 3D planes via AR. The proposed framework is developed to facilitate robust and more promising robot-aided rehabilitation sessions remotely, and it can also be applied in other medical applications.

BackgroundOpioid use disorder (OUD) is an addiction crisis in the United States. As recent as 2019, more than 10 million people have misused or abused prescription opioids, making OUD one of the leading causes of accidental death in the United States. Workforces that are physically demanding and laborious in the transportation, construction and extraction, and health care industries are prime targets for OUD due to high-risk occupational activities. Because of this high prevalence of OUD among working populations in the United States, elevated workers’ compensation and health insurance costs, absenteeism, and declined productivity in workplaces have been reported. ObjectiveWith the emergence of new smartphone technologies, health interventions can be widely used outside clinical settings via mobile health tools. The major objective of our pilot study was to develop a smartphone app that can track work-related risk factors leading to OUD with a specific focus on high-risk occupational groups. We used synthetic data analyzed by applying a machine learning algorithm to accomplish our objective. MethodsTo make the OUD assessment process more convenient and to motivate potential patients with OUD, we developed a smartphone-based app through a step-by-step process. First, an extensive literature survey was conducted to list a set of critical risk assessment questions that can capture high-risk behaviors leading to OUD. Next, a review panel short-listed 15 questions after careful evaluation with specific emphasis on physically demanding workforces—9 questions had two, 5 questions had five, and 1 question had three response options. Instead of human participant data, synthetic data were used as user responses. Finally, an artificial intelligence algorithm, naive Bayes, was used to predict the OUD risk, trained with the synthetic data collected. ResultsThe smartphone app we have developed is functional as tested with synthetic data. Using the naive Bayes algorithm on collected synthetic data, we successfully predicted the risk of OUD. This would eventually create a platform to test the functionality of the app further using human participant data. ConclusionsThe use of mobile health techniques, such as our mobile app, is highly promising in predicting and offering mitigation plans for disease detection and prevention. Using a naive Bayes algorithm model along with a representational state transfer (REST) application programming interface and cloud-based data encryption storage, respondents can guarantee their privacy and accuracy in estimating their risk. Our app offers a tailored mitigation strategy for specific workforces (eg, transportation and health care workers) that are most impacted by OUD. Despite the limitations of the study, we have developed a robust methodology and believe that our app has the potential to help reduce the opioid crisis.

State-of-the-art techniques (SOTA) for motor imagery decoding have largely involved the use of common spatial patterns (CSP) and power spectral density (PSD), for feature extraction. Other frequency transforms, such as wavelets and empirical mode decomposition (EMD) have also been used but the aforementioned two have been the most popular. For classification, linear discriminant analysis (LDA) and support vector machines (SVM) have been mostly used. It is, however, worth investigating other approaches, such as deep learning, which offer a potential for improvement, but are not yet mainstream. Deep learning techniques based on neural networks (NNs) have been underexplored in motor imagery processing. Considering their success in other fields, which speaks to their potential for obtaining improved results over the SOTA, they should be explored for motor imagery decoding. This study takes a comparative approach in the use of deep learning as compared with the SOTA. From our findings, we infer that neural networks are suitable for motor imagery decoding and might be preferable over the SOTA. The use of specific feature extraction is also not as necessary as seen with SOTA approaches, though it might offer some gains in performance. Our results show a statistically significant improvement in decoding accuracies, up to 20% increase, with the use of NNs as compared with the SOTA. Also, we conclude that the use of crops for data augmentation might yield better results with shallow architectures as against deeper ones and that there might be other factors affecting the effectiveness of crops, needing further investigation.

Abstract Background Building control architecture that balances the assistive manipulation systems with the benefits of direct human control is a crucial challenge of human–robot collaboration. It promises to help people with disabilities more efficiently control wheelchair and wheelchair-mounted robot arms to accomplish activities of daily living. Methods In this study, our research objective is to design an eye-tracking assistive robot control system capable of providing targeted engagement and motivating individuals with a disability to use the developed method for self-assistance activities of daily living. The graphical user interface is designed and integrated with the developed control architecture to achieve the goal. Results We evaluated the system by conducting a user study. Ten healthy participants performed five trials of three manipulation tasks using the graphical user interface and the developed control framework. The 100% success rate on task performance demonstrates the effectiveness of our system for individuals with motor impairments to control wheelchair and wheelchair-mounted assistive robotic manipulators. Conclusions We demonstrated the usability of using this eye-gaze system to control a robotic arm mounted on a wheelchair in activities of daily living for people with disabilities. We found high levels of acceptance with higher ratings in the evaluation of the system with healthy participants.

Abstract Community-wide lockdowns in response to COVID-19 influenced many families, but the developmental cascade for children with autism spectrum disorder (ASD) may be especially detrimental. Our objective was to evaluate behavioral patterns of risk and resilience for children with ASD across parent-report assessments before (from November 2019 to February 2020), during (March 2020 to May 2020), and after (June 2020 to November 2020) an extended COVID-19 lockdown. In 2020, our study Mobile-based care for children with ASD using remote experience sampling method (mCARE) was inactive data collection before COVID-19 emerged as a health crisis in Bangladesh. Here we deployed “Cohort Studies”, where we had in total 300 children with ASD (150 test group and 150 control group) to collect behavioral data. Our data collection continued through an extended COVID-19 lockdown and captured parent reports of 30 different behavioral parameters (e.g., self-injurious behaviors, aggression, sleep problems, daily living skills, and communication) across 150 children with ASD (test group). Based on the children’s condition, 4–6 behavioral parameters were assessed through the study. A total of 56,290 behavioral data points was collected (an average of 152.19 per week) from parent cell phones using the mCARE platform. Children and their families were exposed to an extended COVID-19 lockdown. The main outcomes used for this study were generated from parent reports child behaviors within the mCARE platform. Behaviors included of child social skills, communication use, problematic behaviors, sensory sensitivities, daily living, and play. COVID-19 lockdowns for children with autism and their families are not universally negative but supports in the areas of “Problematic Behavior” could serve to mitigate future risk.

BackgroundDiabetes mellitus is a severe disease characterized by high blood glucose levels resulting from dysregulation of the hormone insulin. Diabetes is managed through physical activity and dietary modification and requires careful monitoring of blood glucose concentration. Blood glucose concentration is typically monitored throughout the day by analyzing a sample of blood drawn from a finger prick using a commercially available glucometer. However, this process is invasive and painful, and leads to a risk of infection. Therefore, there is an urgent need for noninvasive, inexpensive, novel platforms for continuous blood sugar monitoring. ObjectiveOur study aimed to describe a pilot test to test the accuracy of a noninvasive glucose monitoring prototype that uses laser technology based on near-infrared spectroscopy. MethodsOur system is based on Raspberry Pi, a portable camera (Raspberry Pi camera), and a visible light laser. The Raspberry Pi camera captures a set of images when a visible light laser passes through skin tissue. The glucose concentration is estimated by an artificial neural network model using the absorption and scattering of light in the skin tissue. This prototype was developed using TensorFlow, Keras, and Python code. A pilot study was run with 8 volunteers that used the prototype on their fingers and ears. Blood glucose values obtained by the prototype were compared with commercially available glucometers to estimate accuracy. ResultsWhen using images from the finger, the accuracy of the prototype is 79%. Taken from the ear, the accuracy is attenuated to 62%. Though the current data set is limited, these results are encouraging. However, three main limitations need to be addressed in future studies of the prototype: (1) increase the size of the database to improve the robustness of the artificial neural network model; (2) analyze the impact of external factors such as skin color, skin thickness, and ambient temperature in the current prototype; and (3) improve the prototype enclosure to make it suitable for easy finger and ear placement. ConclusionsOur pilot study demonstrates that blood glucose concentration can be estimated using a small hardware prototype that uses infrared images of human tissue. Although more studies need to be conducted to overcome limitations, this pilot study shows that an affordable device can be used to avoid the use of blood and multiple finger pricks for blood glucose monitoring in the diabetic population.

The wide use of motor imagery as a paradigm for brain-computer interfacing (BCI) points to its characteristic ability to generate discriminatory signals for communication and control. In recent times, deep learning techniques have increasingly been explored, in motor imagery decoding. While deep learning techniques are promising, a major challenge limiting their wide adoption is the amount of data available for decoding. To combat this challenge, data augmentation can be performed, to enhance decoding performance. In this study, we performed data augmentation by synthesizing motor imagery (MI) electroencephalography (EEG) trials, following six approaches. Data generated using these methods were evaluated based on four criteria, namely – the accuracy of prediction, the Frechet Inception distance (FID), the t-distributed Stochastic Neighbour Embedding (t-SNE) plots and topographic head plots. We show, based on these, that the synthesized data exhibit similar characteristics with real data, gaining up to 3% and 12% increases in mean accuracies across two public datasets. Finally, we believe these approaches should be utilized in applying deep learning techniques, as they not only have the potential to improve prediction performances, but also to save time spent on subject data collection.

Throughout the last decade, many assistive robots for people with disabilities have been developed; however, researchers have not fully utilized these robotic technologies to entirely create independent living conditions for people with disabilities, particularly in relation to activities of daily living (ADLs). An assistive system can help satisfy the demands of regular ADLs for people with disabilities. With an increasing shortage of caregivers and a growing number of individuals with impairments and the elderly, assistive robots can help meet future healthcare demands. One of the critical aspects of designing these assistive devices is to improve functional independence while providing an excellent human–machine interface. People with limited upper limb function due to stroke, spinal cord injury, cerebral palsy, amyotrophic lateral sclerosis, and other conditions find the controls of assistive devices such as power wheelchairs difficult to use. Thus, the objective of this research was to design a multimodal control method for robotic self-assistance that could assist individuals with disabilities in performing self-care tasks on a daily basis. In this research, a control framework for two interchangeable operating modes with a finger joystick and a chin joystick is developed where joysticks seamlessly control a wheelchair and a wheelchair-mounted robotic arm. Custom circuitry was developed to complete the control architecture. A user study was conducted to test the robotic system. Ten healthy individuals agreed to perform three tasks using both (chin and finger) joysticks for a total of six tasks with 10 repetitions each. The control method has been tested rigorously, maneuvering the robot at different velocities and under varying payload (1–3.5 lb) conditions. The absolute position accuracy was experimentally found to be approximately 5 mm. The round-trip delay we observed between the commands while controlling the xArm was 4 ms. Tests performed showed that the proposed control system allowed individuals to perform some ADLs such as picking up and placing items with a completion time of less than 1 min for each task and 100% success.

BackgroundApplications of robotics in daily life are becoming essential by creating new possibilities in different fields, especially in the collaborative environment. The potentials of collaborative robots are tremendous as they can work in the same workspace as humans. A framework employing a top-notch technology for collaborative robots will surely be worthwhile for further research. ObjectiveThis study aims to present the development of a novel framework for the collaborative robot using mixed reality. MethodsThe framework uses Unity and Unity Hub as a cross-platform gaming engine and project management tool to design the mixed reality interface and digital twin. It also uses the Windows Mixed Reality platform to show digital materials on holographic display and the Azure mixed reality services to capture and expose digital information. Eventually, it uses a holographic device (HoloLens 2) to execute the mixed reality–based collaborative system. ResultsA thorough experiment was conducted to validate the novel framework for mixed reality–based control of a collaborative robot. This framework was successfully applied to implement a collaborative system using a 5–degree of freedom robot (xArm-5) in a mixed reality environment. The framework was stable and worked smoothly throughout the collaborative session. Due to the distributed nature of cloud applications, there is a negligible latency between giving a command and the execution of the physical collaborative robot. ConclusionsOpportunities for collaborative robots in telerehabilitation and teleoperation are vital as in any other field. The proposed framework was successfully applied in a collaborative session, and it can also be applied in other similar potential applications for robust and more promising performance.

The burgeoning growth of Big Data not only matures and improves the data management efficiency and useful information extraction techniques, but also motivates the computational science researchers to come up with a new method or solution that can be repurposed for problems across the domain. Computational Sustainability joins this movement for a transferrable computational technique for sustainable development and a better future. Internet-of-energy (IoE) - leveraging IoT to smart grids associated with advanced analytics - is one of the prominent efforts in this regard. This paper presents a qualitative analysis on the elements of the energy and power management ecosystem in the United States. This qualitative study includes the Grid Overview of the United States; Weather and Climate and its impact on the entire energy generation and consumption dynamics; Peak Load Forecasting and its techniques and burgeoning challenges; Variable Renewable Energy, its reliability challenges and how we can take advantage of this variability; Commodity Prices and its criticality; Energy Disaggregation and its impact on consumption-awareness; and Generation Expansion and Decision Analysis. Besides, IoE integration, associated trade-offs, challenges, research opportunities and transferable computational techniques are addressed in this communication. Furthermore, schematics and quantitative analysis are presented in support of this study.

BackgroundSolid-organ transplantation is the treatment of choice for children with end-stage organ failure. Ongoing recovery and medical management at home after transplant are important for recovery and transition to daily life. Smartphones are widely used and hold the potential for aiding in the establishment of mobile health (mHealth) protocols. Health care providers, nurses, and computer scientists collaboratively designed and developed mHealth family self-management intervention (myFAMI), a smartphone-based intervention app to promote a family self-management intervention for pediatric transplant patients’ families. ObjectiveThis paper presents outcomes of the design stages and development actions of the myFAMI app framework, along with key challenges, limitations, and strengths. MethodsThe myFAMI app framework is built upon a theory-based intervention for pediatric transplant patients, with aid from the action research (AR) methodology. Based on initially defined design motivation, the team of researchers collaboratively explored 4 research stages (research discussions, feedback and motivations, alpha testing, and deployment and release improvements) and developed features required for successful inauguration of the app in the real-world setting. ResultsDeriving from app users and their functionalities, the myFAMI app framework is built with 2 primary components: the web app (for nurses’ and superadmin usage) and the smartphone app (for participant/family member usage). The web app stores survey responses and triggers alerts to nurses, when required, based on the family members’ response. The smartphone app presents the notifications sent from the server to the participants and captures survey responses. Both the web app and the smartphone app were built upon industry-standard software development frameworks and demonstrate great performance when deployed and used by study participants. ConclusionsThe paper summarizes a successful and efficient mHealth app-building process using a theory-based intervention in nursing and the AR methodology in computer science. Focusing on factors to improve efficiency enabled easy navigation of the app and collection of data. This work lays the foundation for researchers to carefully integrate necessary information (from the literature or experienced clinicians) to provide a robust and efficient solution and evaluate the acceptability, utility, and usability for similar studies in the future. International Registered Report Identifier (IRRID)RR2-10.1002/nur.22010

Recent technological advances enable gripper-equipped robots to perform many tasks traditionally associated with the human hand, allowing the use of grippers in a wide range of applications. Depending on the application, an ideal gripper design should be affordable, energy-efficient, and adaptable to many situations. However, regardless of the number of grippers available on the market, there are still many tasks that are difficult for grippers to perform, which indicates the demand and room for new designs to compete with the human hand. Thus, this paper provides a comprehensive review of robotic arm grippers to identify the benefits and drawbacks of various gripper designs. The research compares gripper designs by considering the actuation mechanism, degrees of freedom, grasping capabilities with multiple objects, and applications, concluding which should be the gripper design with the broader set of capabilities.

Being an emerging technology, robotic manipulation has encountered tremendous advancements due to technological developments starting from using sensors to artificial intelligence. Over the decades, robotic manipulation has advanced in terms of the versatility and flexibility of mobile robot platforms. Thus, robots are now capable of interacting with the world around them. To interact with the real world, robots require various sensory inputs from their surroundings, and the use of vision is rapidly increasing nowadays, as vision is unquestionably a rich source of information for a robotic system. In recent years, robotic manipulators have made significant progress towards achieving human-like abilities. There is still a large gap between human and robot dexterity, especially when it comes to executing complex and long-lasting manipulations. This paper comprehensively investigates the state-of-the-art development of vision-based robotic application, which includes the current state, components, and approaches used along with the algorithms with respect to the control and application of robots. Furthermore, a comprehensive analysis of those vision-based applied algorithms, their effectiveness, and their complexity has been enlightened here. To conclude, there is a discussion over the constraints while performing the research and potential solutions to develop a robust and accurate vision-based robot manipulation.

The alarmingly aggravating incidents of sepsis and septic shock, and associated mortality, morbidity, and annual treatment costs among ICU admissions are an increasing concern. SepINav is a medical informatics endeavor that helps ICU practitioners and researchers to monitor and intervene on the existing sepsis patients more efficiently and interactively and conduct retrospective studies to seek rationales to different sepsis scenarios in the ICU. Moreover, Bayesian Online Changepoint Detection will help the practitioners understand the structural changes in patients’ vital sign regimes that may harbinger prior to septic shock. Besides, several additional features are added to this data-driven software tool to promise efficient monitoring and intervention and address confounding medical interventions in the ICU.

Autism Spectrum Disorder (ASD) is a critical problem worldwide; however, low and middle-income countries (LMICs) often suffer more from it due to the lack of contextual research and effective care infrastructure. Moreover, ASD in LMICs offers unique challenges as cultural misperceptions and social practices often impede effective care there. However, the recent growth in mobile phone adoption in many LMICs has created a timely opportunity for improving ASD care practices through digital means. We present qualitative findings on the challenges of designing mobile assistive technologies for ASD in Bangladesh. Our data reveals a set of technical, social, and cultural challenges with caregivers and care professionals for designing a meaningful technology for them. We show how those challenges are deeply rooted in the broader infrastructural and cultural tensions of the country. We also present embedded design implications to overcome these challenges.

Purpose: Three-fourths of patients with advanced cancer are reported to suffer from pain. A primary barrier to provision of adequate symptom treatment is failure to appreciate the intensity of the symptoms patients are experiencing. Because data on Bangladeshi and Nepalese patients’ perceptions of their symptomatic status are limited, we sought such information using a cell phone questionnaire. Methods: At tertiary care centers in Dhaka and Kathmandu, we recruited 640 and 383 adult patients, respectively, with incurable malignancy presenting for outpatient visits and instructed them for that single visit on one-time completion of a cell phone platform 15-item survey of questions about common cancer-associated symptoms and their magnitudes using Likert scales of 0 to 10. The questions were taken from the Edmonton Symptom Assessment System and the Brief Pain Inventory instruments. Results: All but two Bangladeshi patients recruited agreed to study participation. Two-thirds of Bangladeshi patients reported usual pain levels ≥ 5, and 50% of Nepalese patients reported usual pain levels ≥ 4 (population differences significant at P < .001). Conclusion: Bangladeshi and Nepalese adults with advanced cancer are comfortable with cell phone questionnaires about their symptoms and report high levels of pain. Greater attention to the suffering of these patients is warranted.