Stress Monitoring Device and its Future Direction

Stress and mental health have become a global concern as a result of the COVID-19 pandemic. This review highlights the technologies applied to stress monitoring with up-to -date literature on psychological and behavioural evaluation for stress detection. The working principle and potential of these stress identifiers, particularly physiological signals are explored. Researchers have been directing their interest in producing reliable and wearable devices to detect and prevent stress and panic attacks. The breakthrough of biochemical-near-infrared stress monitoring devices will facilitate medical practitioners in the early detection of stress and panic attacks. The review seeks to explore the types of methods used to detect stress as well as the pros and cons of the available technology while providing a new solution for future implications of stress monitoring detection devices. [ FROM AUTHOR] Copyright of IIUM Medical Journal Malaysia is the property of International Islamic University Malaysia, Faculty of Medicine and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

A wearable device for at-home obstructive sleep apnea assessment: State-of-the-art and research challenges.

In the last 3 years, almost all medical resources have been reserved for the screening and treatment of patients with coronavirus disease (COVID-19). Due to a shortage of medical staff and equipment, diagnosing sleep disorders, such as obstructive sleep apnea (OSA), has become more difficult than ever. In addition to being diagnosed using polysomnography at a hospital, people seem to pay more attention to alternative at-home OSA detection solutions. This study aims to review state-of-the-art assessment techniques for out-of-center detection of the main characteristics of OSA, such as sleep, cardiovascular function, oxygen balance and consumption, sleep position, breathing effort, respiratory function, and audio, as well as recent progress in the implementation of data acquisition and processing and machine learning techniques that support early detection of severe OSA levels.

Compliance Challenges in a Longitudinal COVID-19 Cohort Using Wearables for Continuous Monitoring: Observational Study.

BACKGROUND: The WEAICOR (Wearables to Investigate the Long Term Cardiovascular and Behavioral Impacts of COVID-19) study was a prospective observational study that used continuous monitoring to detect and analyze biometrics. Compliance to wearables was a major challenge when conducting the study and was crucial for the results. OBJECTIVE: The aim of this study was to evaluate patients' compliance to wearable wristbands and determinants of compliance in a prospective COVID-19 cohort. METHODS: The Biostrap (Biostrap USA LLC) wearable device was used to monitor participants' biometric data. Compliance was calculated by dividing the total number of days in which transmissions were sent by the total number of days spent in the WEAICOR study. Univariate correlation analyses were performed, with compliance and days spent in the study as dependent variables and age, BMI, sex, symptom severity, and the number of complications or comorbidities as independent variables. Multivariate linear regression was then performed, with days spent in the study as a dependent variable, to assess the power of different parameters in determining the number of days patients spent in the study. RESULTS: A total of 122 patients were included in this study. Patients were on average aged 41.32 years, and 46 (38%) were female. Age was found to correlate with compliance (r=0.23; P=.01). In addition, age (r=0.30; P=.001), BMI (r=0.19; P=.03), and the severity of symptoms (r=0.19; P=.03) were found to correlate with days spent in the WEAICOR study. Per our multivariate analysis, in which days spent in the study was a dependent variable, only increased age was a significant determinant of compliance with wearables (adjusted R2=0.1; ß=1.6; P=.01). CONCLUSIONS: Compliance is a major obstacle in remote monitoring studies, and the reasons for a lack of compliance are multifactorial. Patient factors such as age, in addition to environmental factors, can affect compliance to wearables.

Smartwatch Sensors with Deep Learning to Predict the Purchase Intentions of Online Shoppers.

In the past decade, the scale of e-commerce has continued to grow. With the outbreak of the COVID-19 epidemic, brick-and-mortar businesses have been actively developing online channels where precision marketing has become the focus. This study proposed using the electrocardiography (ECG) recorded by wearable devices (e.g., smartwatches) to judge purchase intentions through deep learning. The method of this study included a long short-term memory (LSTM) model supplemented by collective decisions. The experiment was divided into two stages. The first stage aimed to find the regularity of the ECG and verify the research by repeated measurement of a small number of subjects. A total of 201 ECGs were collected for deep learning, and the results showed that the accuracy rate of predicting purchase intention was 75.5%. Then, incremental learning was adopted to carry out the second stage of the experiment. In addition to adding subjects, it also filtered five different frequency ranges. This study employed the data augmentation method and used 480 ECGs for training, and the final accuracy rate reached 82.1%. This study could encourage online marketers to cooperate with health management companies with cross-domain big data analysis to further improve the accuracy of precision marketing.

Internet of Wearable Medical Things for COVID-19 Diagnostics

Wearable devices have played a key role in the medical industry, especially since the COVID-19 pandemic spread. The need for a self-monitoring system increased since the spread of the virus. With the development of semiconductor technology and the increased research and development in medical wearable devices, wearable devices have been able to detect the medical condition of patients. This paper presents a biomedical wearable device to monitor the vital signs of patients. The device can be used to detect the patient COVID-19 infection. Data were extracted using different sensors and other components, and results were displayed on a mobile application that showed the health status of the patient. A PCB (Printed Circuit Board) design was made for the purpose of making the system a wearable device. The system power consumption ranged from 5-37.5mW. © 2022 IEEE.

Detecting Door Operations Using Wearable Devices

Recording indoor human activities such as room occupancy is important to control the COVID-19 pandemic. Logs of human activities can be recorded using wearable devices, provided that the action of entering or exiting a room can be recognized based on the operation of doors. However, relatively few studies on human activity recognition have considered the detection of door operations using wearable devices. In this study, we propose a new deep learning-based technique to detect door operations. We developed a smartwatch application to collect and label multiple forms of data. To evaluate the proposed approach, we conducted an experiment in which we collected data during 4 door operations (2 types of doors with 2 activities, including entering and exiting) using the application. The collected data were then used to train deep learning models. The experimental results show that the average F1 scores ranged from 0.787 to 0.909 when acceleration and angular velocity data were used, which suggests that the proposed technique can detect door operations sufficiently well. © 2022 IEEE.

The Impact of IoT in Geriatric Disease and COVID-19: A Systematic Review

Advances in medicine and technology have resulted in an increase in human life expectancy and aging population across the globe. The growing geriatric population and its medical needs, has a significant impact on the healthcare ecosystem, including higher medical and human costs to manage the upsurge of chronic diseases such as Alzheimer's Dementia, Parkinson's, Diabetes Mellitus, frailty, stroke, cardiovascular disease and with the recent COVID-19 pandemic. IoT wearables and devices offer a promising solution to reliably assess, monitor, and support healthy ageing in a remote manner. Objective and holistic patient information made available by IoT technology is very valuable for physicians to manage the chronic ailments and slow down the disease progression in their geriatric patient base. In this study we present a broad overview of ailments pertinent to elders and review of the case studies with IoT applications and its interconnected sensing solutions to manage those ailments. © 2022 American Institute of Physics Inc.. All rights reserved.

Feasibility of Remotely Monitoring Daily Physical Activity in Individuals with Acquired Brain Injury

Research Objectives Wearable physical activity monitors (PAM) are increasingly replacing traditional patient recall questionnaires to monitor physical activity in the community. Remote objective monitoring of physical activity is needed especially in the current times of the Covid-19 pandemic. While PAM are increasingly used in multiple populations, there is no information on the feasibility and challenges of using PAM to remotely monitor physical activity in individuals with acquired brain injury. The main goal of this research is to determine the usability, ease of use and identify challenges of using PAM for remotely monitoring objective physical activity in the community in individuals with acquired brain injury. Design Mixed methods research (4-weeks). Setting In the community (real-world). Participants Ten individuals with acquired-brain-injury (Stroke and TBI). Interventions All participants wore three (wrist, ankle, waist) ActiGraph GT9X Link PAM simultaneously for 4-weeks in the community and provided their feedback weekly. Main Outcome Measures Usability, ease of use, challenges. Results Participants rated all PAM to have good usability on the System Usability Scale. They gave the best possible score on the After Scenario Questionnaire after using the remote data hub which was used daily to transfer the data remotely to the research dashboard. Qualitative content analysis showed that most challenges reported were for wrist PAM and wear attachments were the most challenging aspect of using PAM in this population. Conclusions This research helps establish the framework and challenges of using PAM to remotely monitor objective physical activity in the community in individuals with acquired brain injury. Continuous monitoring of physical activity in the community is essential to evaluate the progress and the effect of rehab interventions and for tracking daily physical activity in individuals with acquired brain injury. Author(s) Disclosures No conflicts.

Wearable Technology for Monitoring Respiratory Rate and SpO2 of COVID-19 Patients: A Systematic Review.

With the significant numbers of sudden home deaths reported worldwide due to coronavirus disease 2019 (COVID-19), wearable technology has emerged as a method for surveilling this infection. This review explored the indicators of COVID-19 surveillance, such as vitals, respiratory condition, temperature, oxygen saturation (SpO2), and activity levels using wearable devices. Studies published between 31 December 2019, and 8 July 2022, were obtained from PubMed, and grey literature, reference lists, and key journals were also searched. All types of articles with the keywords "COVID-19", "Diagnosis", and "Wearable Devices" were screened. Four reviewers independently screened the articles against the eligibility criteria and extracted the data using a data charting form. A total of 56 articles were on monitoring, of which 28 included SpO2 as a parameter. Although wearable devices are effective in the continuous monitoring of COVID-19 patients, further research on actual patients is necessary to determine the efficiency and effectiveness of wearable technology before policymakers can mandate its use.

Global Impact of COVID-19 Pandemic on Physical Activity Habits of Competitive Runners: An Analysis of Wearable Device Data.

The COVID-19 pandemic resulted in government restrictions that altered the lifestyle of people worldwide. Studying the impact of these restrictions on exercise behaviors will improve our understanding of the environmental factors that influence individuals' physical activity (PA). We conducted a retrospective analysis using an stringency index of government pandemic policies developed by Oxford University and digitally-logged PA data from more than 7000 runners collected using a wearable exercise-tracking device to compare the relationship between strictness of lockdowns and exercise habits on a global scale. Additionally, the time-of-day of PA globally, and activity-levels of PA in 14 countries, are compared between the pre-pandemic year of 2019 and the first pandemic year of 2020. We found that during the pandemic year there was a major shift in the time-of-day that runners exercised, with significantly more activity counts logged during standard working hours on workdays (p < 0.001) and fewer activities during the same time frame on weekends (p < 0.001). Of the countries examined, Italy and Spain had among the most strict lockdowns and suffered the largest decreases in activity counts, whereas France experienced a minimal decrease in activity counts despite enacting a strict lockdown with certain allowances. This study suggests that there are several factors affecting PA of dedicated runners, including government policy, workplace policy, and cultural norms.

Risk Assessment of COVID-19 Cases in Emergency Departments and Clinics With the Use of Real-World Data and Artificial Intelligence: Observational Study.

BACKGROUND: The recent COVID-19 pandemic has highlighted the weaknesses of health care systems around the world. In the effort to improve the monitoring of cases admitted to emergency departments, it has become increasingly necessary to adopt new innovative technological solutions in clinical practice. Currently, the continuous monitoring of vital signs is only performed in patients admitted to the intensive care unit. OBJECTIVE: The study aimed to develop a smart system that will dynamically prioritize patients through the continuous monitoring of vital signs using a wearable biosensor device and recording of meaningful clinical records and estimate the likelihood of deterioration of each case using artificial intelligence models. METHODS: The data for the study were collected from the emergency department and COVID-19 inpatient unit of the Hippokration General Hospital of Thessaloniki. The study was carried out in the framework of the COVID-X H2020 project, which was funded by the European Union. For the training of the neural network, data collection was performed from COVID-19 cases hospitalized in the respective unit. A wearable biosensor device was placed on the wrist of each patient, which recorded the primary characteristics of the visual signal related to breathing assessment. RESULTS: A total of 157 adult patients diagnosed with COVID-19 were recruited. Lasso penalty function was used for selecting 18 out of 48 predictors and 2 random forest-based models were implemented for comparison. The high overall performance was maintained, if not improved, by feature selection, with random forest achieving accuracies of 80.9% and 82.1% when trained using all predictors and a subset of them, respectively. Preliminary results, although affected by pandemic limitations and restrictions, were promising regarding breathing pattern recognition. CONCLUSIONS: This study represents a novel approach that involves the use of machine learning methods and Edge artificial intelligence to assist the prioritization and continuous monitoring procedures of patients with COVID-19 in health departments. Although initial results appear to be promising, further studies are required to examine its actual effectiveness.

A hand-targeted auxiliary personal protective equipment for intervention of fomite transmission of viruses.

In COVID-19, fomite transmission has been shown to be a major route for the spreading of the SARS-CoV-2 virus due to its ability to remain on surfaces for extended durations. Although glove wearing can mitigate the risk of viral transmission especially on high touch points, it is not prevalent due to concerns on diversion of frontline medical resources, cross-contamination, social stigma, as well as discomfort and skin reactions derived from prolonged wearing. In this study, we developed FlexiPalm, a hand-targeted auxiliary personal protective equipment (PPE) against fomite transmission of viruses. FlexiPalm is a unique palmar-side hand protector designed to be skin-conforming and transparent, fabricated from medical-grade polyurethane transparent film material as a base substrate. It serves primarily as a barrier to microbial contamination like conventional gloves, but with augmented comfort and inconspicuousness to encourage a higher public adoption rate. Compared to conventional glove materials, FlexiPalm demonstrated enhanced mechanical durability and breathability, comparable hydrophobicity, and displayed a minimal adsorption of SARS-CoV-2 spike protein and virus-like particles (VLP). Importantly, FlexiPalm was found to bind significantly less viral protein and VLP than artificial human skin, confirming its ability to reduce viral contamination. A pilot study involving participants completing activities of daily living showed a high level of comfort and task completion, illustrating the usability and functionality of FlexiPalm. Moreover, we have demonstrated that surface modification of FlexiPalm with microtextures enables further reduction in viral adsorption, thereby enhancing its functionality. An effective implementation of FlexiPalm will bolster PPE sustainability and lead to a paradigm shift in the global management of COVID-19 and other infectious diseases in general.

Prediction of COVID-19 Patients Recovery using Ensemble Machine Learning and Vital Signs Data Collected by Novel Wearable Device

During the spread of a pandemic such as COVID-19, the effort required of health institutions increases dramatically. Generally, Health systems’ response and efficiency depend on monitoring vital signs such as blood oxygen level, heartbeat, and body temperature. At the same time, remote health monitoring and wearable health technologies have revolutionized the concept of effective healthcare provision from a distance. However, analyzing such a large amount of medical data in time to provide the decision-makers with necessary health procedures is still a challenge. In this research, a wearable device and monitoring system are developed to collect real data from more than 400 COVID-19 patients. Based on this data, three classifiers are implemented using two ensemble classification techniques (Adaptive Boosting and Adaptive Random Forest). The analysis of collected data showed a remarkable relationship between the patient’s age and chronic disease on the one hand and the speed of recovery on the other. The experimental results indicate a highly accurate performance for Adaptive Boosting classifiers, reaching 99%, while the Adaptive Random Forest got a 91% accuracy metric © 2022. International Journal of Advanced Computer Science and Applications.All Rights Reserved.

Wearable Technologies for Pediatric Patients with Surgical Infections-More than Counting Steps?

Reliable vital sign assessments are crucial for the management of patients with infectious diseases. Wearable devices enable easy and comfortable continuous monitoring across settings, especially in pediatric patients, but information about their performance in acutely unwell children is scarce. Vital signs were continuously measured with a multi-sensor wearable device (Everion®, Biofourmis, Zurich, Switzerland) in 21 pediatric patients during their hospitalization for appendicitis, osteomyelitis, or septic arthritis to describe acceptance and feasibility and to compare validity and reliability with conventional measurements. Using a wearable device was highly accepted and feasible for health-care workers, parents, and children. There were substantial data gaps in continuous monitoring up to 24 h. The wearable device measured heart rate and oxygen saturation reliably (mean difference, 2.5 bpm and 0.4% SpO2) but underestimated body temperature by 1.7 °C. Data availability was suboptimal during the study period, but a good relationship was determined between wearable device and conventional measurements for heart rate and oxygen saturation. Acceptance and feasibility were high in all study groups. We recommend that wearable devices designed for medical use in children be validated in the targeted population to assure future high-quality continuous vital sign assessments in an easy and non-burdening way.

Studying the Effect of Long COVID-19 Infection on Sleep Quality Using Wearable Health Devices: Observational Study.

BACKGROUND: Patients with COVID-19 have increased sleep disturbances and decreased sleep quality during and after the infection. The current published literature focuses mainly on qualitative analyses based on surveys and subjective measurements rather than quantitative data. OBJECTIVE: In this paper, we assessed the long-term effects of COVID-19 through sleep patterns from continuous signals collected via wearable wristbands. METHODS: Patients with a history of COVID-19 were compared to a control arm of individuals who never had COVID-19. Baseline demographics were collected for each subject. Linear correlations among the mean duration of each sleep phase and the mean daily biometrics were performed. The average duration for each subject's total sleep time and sleep phases per night was calculated and compared between the 2 groups. RESULTS: This study includes 122 patients with COVID-19 and 588 controls (N=710). Total sleep time was positively correlated with respiratory rate (RR) and oxygen saturation (SpO2). Increased awake sleep phase was correlated with increased heart rate, decreased RR, heart rate variability (HRV), and SpO2. Increased light sleep time was correlated with increased RR and SpO2 in the group with COVID-19. Deep sleep duration was correlated with decreased heart rate as well as increased RR and SpO2. When comparing different sleep phases, patients with long COVID-19 had decreased light sleep (244, SD 67 vs 258, SD 67; P=.003) and decreased deep sleep time (123, SD 66 vs 128, SD 58; P=.02). CONCLUSIONS: Regardless of the demographic background and symptom levels, patients with a history of COVID-19 infection demonstrated altered sleep architecture when compared to matched controls. The sleep of patients with COVID-19 was characterized by decreased total sleep and deep sleep.

Evaluation of a machine learning approach utilizing wearable data for prediction of SARS-CoV-2 infection in healthcare workers.

Objective: To determine whether a machine learning model can detect SARS-CoV-2 infection from physiological metrics collected from wearable devices. Materials and Methods: Health care workers from 7 hospitals were enrolled and prospectively followed in a multicenter observational study. Subjects downloaded a custom smart phone app and wore Apple Watches for the duration of the study period. Daily surveys related to symptoms and the diagnosis of Coronavirus Disease 2019 were answered in the app. Results: We enrolled 407 participants with 49 (12%) having a positive nasal SARS-CoV-2 polymerase chain reaction test during follow-up. We examined 5 machine-learning approaches and found that gradient-boosting machines (GBM) had the most favorable validation performance. Across all testing sets, our GBM model predicted SARS-CoV-2 infection with an average area under the receiver operating characteristic (auROC) = 86.4% (confidence interval [CI] 84-89%). The model was calibrated to value sensitivity over specificity, achieving an average sensitivity of 82% (CI ±âˆ¼4%) and specificity of 77% (CI ±âˆ¼1%). The most important predictors included parameters describing the circadian heart rate variability mean (MESOR) and peak-timing (acrophase), and age. Discussion: We show that a tree-based ML algorithm applied to physiological metrics passively collected from a wearable device can identify and predict SARS-CoV-2 infection. Conclusion: Applying machine learning models to the passively collected physiological metrics from wearable devices may improve SARS-CoV-2 screening methods and infection tracking.

Research on Digital Technology Use in Cardiology: Bibliometric Analysis.

BACKGROUND: Digital technology uses in cardiology have become a popular research focus in recent years. However, there has been no published bibliometric report that analyzed the corresponding academic literature in order to derive key publishing trends and characteristics of this scientific area. OBJECTIVE: We used a bibliometric approach to identify and analyze the academic literature on digital technology uses in cardiology, and to unveil popular research topics, key authors, institutions, countries, and journals. We further captured the cardiovascular conditions and diagnostic tools most commonly investigated within this field. METHODS: The Web of Science electronic database was queried to identify relevant papers on digital technology uses in cardiology. Publication and citation data were acquired directly from the database. Complete bibliographic data were exported to VOSviewer, a dedicated bibliometric software package, and related to the semantic content of titles, abstracts, and keywords. A term map was constructed for findings visualization. RESULTS: The analysis was based on data from 12,529 papers. Of the top 5 most productive institutions, 4 were based in the United States. The United States was the most productive country (4224/12,529, 33.7%), followed by United Kingdom (1136/12,529, 9.1%), Germany (1067/12,529, 8.5%), China (682/12,529, 5.4%), and Italy (622/12,529, 5.0%). Cardiovascular diseases that had been frequently investigated included hypertension (152/12,529, 1.2%), atrial fibrillation (122/12,529, 1.0%), atherosclerosis (116/12,529, 0.9%), heart failure (106/12,529, 0.8%), and arterial stiffness (80/12,529, 0.6%). Recurring modalities were electrocardiography (170/12,529, 1.4%), angiography (127/12,529, 1.0%), echocardiography (127/12,529, 1.0%), digital subtraction angiography (111/12,529, 0.9%), and photoplethysmography (80/12,529, 0.6%). For a literature subset on smartphone apps and wearable devices, the Journal of Medical Internet Research (20/632, 3.2%) and other JMIR portfolio journals (51/632, 8.0%) were the major publishing venues. CONCLUSIONS: Digital technology uses in cardiology target physicians, patients, and the general public. Their functions range from assisting diagnosis, recording cardiovascular parameters, and patient education, to teaching laypersons about cardiopulmonary resuscitation. This field already has had a great impact in health care, and we anticipate continued growth.

Development of a low-cost wearable device for Covid-19 self-quarantine monitoring system.

Objectives: The objective of this study is to develop a Bluetooth-based low-cost wearable device for a self-quarantine monitoring system. Study design: The designed wearable device focuses on data transmission via Bluetooth, integration of tracking, tracing, and fencing into a single system, and low energy usage from its battery. Methods: We design a wearable device using smartphone equipped with GPS, a communication module, Bluetooth low energy (BLE) and a high-capacity battery as a solution for low-cost device with excellent efficiency. We divide the designed system into two parts, the client and the server parts. The client parts are wearable device attached to the individual being monitored and the mobile phone as GPS and telecommunications module. Whereas the server parts are user interface, digital map, notification system, and backend database. Then, the whole system was tested in laboratory and field scale. Results: We tested functions of integrated device such as wearable device, mobile applications, and server for laboratory scale test. Then, performing field test with geofencing, communication module, battery, web interface, and resource computing usage. The field test was conducted on a small scale with a limited number of trial patients. We found that the designed wearable device was successfully implemented for both self-quarantine and centralized quarantine requirements. The majority of the components used met the specifications and functioned properly as well. Conclusions: A BLE-enabled wearable device can be used for tracking self-quarantine patients. The laboratory and field scale tests demonstrate that the designed wearable device functions properly and meets the requirements. We anticipate that this low-cost wearable device is effective in limiting Covid-19 virus spread and preventing the formation of a new Covid-19 virus-infected cluster.

A Novel Non-Invasive Thermometer for Continuous Core Body Temperature: Comparison with Tympanic Temperature in an Acute Stroke Clinical Setting.

There is a growing research interest in wireless non-invasive solutions for core temperature estimation and their application in clinical settings. This study aimed to investigate the use of a novel wireless non-invasive heat flux-based thermometer in acute stroke patients admitted to a stroke unit and compare the measurements with the currently used infrared (IR) tympanic temperature readings. The study encompassed 30 acute ischemic stroke patients who underwent continuous measurement (Tcore) with the novel wearable non-invasive CORE device. Paired measurements of Tcore and tympanic temperature (Ttym) by using a standard IR-device were performed 3-5 times/day, yielding a total of 305 measurements. The predicted core temperatures (Tcore) were significantly correlated with Ttym (r = 0.89, p < 0.001). The comparison of the Tcore and Ttym measurements by Bland-Altman analysis showed a good agreement between them, with a low mean difference of 0.11 ± 0.34 °C, and no proportional bias was observed (B = -0.003, p = 0.923). The Tcore measurements correctly predicted the presence or absence of Ttym hyperthermia or fever in 94.1% and 97.4% of cases, respectively. Temperature monitoring with a novel wireless non-invasive heat flux-based thermometer could be a reliable alternative to the Ttym method for assessing core temperature in acute ischemic stroke patients.

Research on Product Design of EMG Wearable Device Based on High-Density Electromyography

Core muscles play a fundamental role both in exercises and daily routines. Strong core muscles can enhance the Trunk stability and transition of strength. However, due to the weakness, most rookies can hardly feel the recruitment of core muscles and start compensating or using the wrong form. This may lead to cumulative fatigue in the short term and improper postures or spinal injuries in a long time. Thus, monitoring and protecting the unit for early core-muscle training is necessary. The study focuses on fitness rookies and designs an innovative waistband. High-density electromyography (HD-sEMG) can provide real-time monitoring of muscle conditions once it censors fatigue. The band will remind the user to take a break. And the shape memory polymer (SMP) can protect the waist and back from potential injury if necessary. With the continuous impact of the coronavirus, trainers spend more time at home and face the limitation of space and equipment. Nonetheless, isometric and simplified isotonic training will be enough for starters for core-muscle exercise. The study lists core-muscle strength exercises for athletes and core-muscle stability prescriptions for medical care, then reorganize them for rookies at home. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.