Workout Detection by Wearable Device Data Using Machine Learning

There are many reports that workouts relieve daily stress and are effective in improving mental and physical health. In recent years, there has been a demand for quick and easy methods to analyze and evaluate living organisms using biological information measured from wearable sensors. In this study, we attempted workout detection for one healthy female (40 years old) based on multiple types of biological information, such as the number of steps taken, activity level, and pulse, obtained from a wristband-type wearable sensor using machine learning. Data were recorded intermittently for approximately 64 days and 57 workouts were recorded. Workouts adopted for exercise were yoga and the workout duration was 1 h. We extracted 3416 min of biometric information for each of three categories: workout, awake activities (activities other than workouts), and sleep. Classification was performed using random forest (RF), SVM, and KNN. The detection accuracy of RF and SVM was high, and the recall, precision, and F-score values when using RF were 0.962, 0.963, and 0.963, respectively. The values for SVM were 0.961, 0.962, and 0.962, respectively. In addition, as a result of calculating the importance of the feature values used for detection, sleep state (39.8%), skin temperature (33.3%), and pulse rate (13.2%) accounted for approximately 86.3% of the total. By applying RF or SVM to the biological information obtained from the wearable wristband sensor, workouts could be detected every minute with high accuracy.

Swimming × Online Coaching: Online Conditioning of Competitive Swimmers - Focusing on the Thorax - via Smartphones and Wearable Sensors

In response to the decline in motor function (centered on the thorax) caused by chronic muscle tension associated with strengthening exercises for competitive swimmers, we devised a training program that promotes awareness of the functional coordination of the thorax;spine, ribs, and core muscles, and restores natural and efficient body movement. This article presents the results of supporting athlete training during the novel coronavirus pandemic by providing regular coaching remotely using a web-conference system with smartphones, video recording, and a multi-sensor belt equipped with hitoe™ for measuring myoelectricity, respiration, and motion. © 2022 Nippon Telegraph and Telephone Corp.. All rights reserved.

Free-living core body temperature monitoring using a wrist-worn sensor after COVID-19 booster vaccination: a pilot study.

Core body temperature (CBT) is a key vital sign and fever is an important indicator of disease. In the past decade, there has been growing interest for vital sign monitoring technology that may be embedded in wearable devices, and the COVID-19 pandemic has highlighted the need for remote patient monitoring systems. While wrist-worn sensors allow continuous assessment of heart rate and oxygen saturation, reliable measurement of CBT at the wrist remains challenging. In this study, CBT was measured continuously in a free-living setting using a novel technology worn at the wrist and compared to reference core body temperature measurements, i.e., CBT values acquired with an ingestible temperature-sensing pill. Fifty individuals who received the COVID-19 booster vaccination were included. The datasets of 33 individuals were used to develop the CBT prediction algorithm, and the algorithm was then validated on the datasets of 17 participants. Mean observation time was 26.4 h and CBT > 38.0 °C occurred in 66% of the participants. CBT predicted by the wrist-worn sensor showed good correlation to the reference CBT (r = 0.72). Bland-Altman statistics showed an average bias of 0.11 °C of CBT predicted by the wrist-worn device compared to reference CBT, and limits of agreement were - 0.67 to + 0.93 °C, which is comparable to the bias and limits of agreement of commonly used tympanic membrane thermometers. The small size of the components needed for this technology would allow its integration into a variety of wearable monitoring systems assessing other vital signs and at the same time allowing maximal freedom of movement to the user.

An ultralow power wearable vital sign sensor using an electromagnetically reactive near field.

Despite coronavirus disease 2019, cardiovascular disease, the leading cause of global death, requires timely detection and treatment for a high survival rate, underscoring the 24 h monitoring of vital signs. Therefore, telehealth using wearable devices with vital sign sensors is not only a fundamental response against the pandemic but a solution to provide prompt healthcare for the patients in remote sites. Former technologies which measured a couple of vital signs had features that disturbed practical applications to wearable devices, such as heavy power consumption. Here, we suggest an ultralow power (100 µW) sensor that collects all cardiopulmonary vital signs, including blood pressure, heart rate, and the respiration signal. The small and lightweight (2 g) sensor designed to be easily embedded in the flexible wristband generates an electromagnetically reactive near field to monitor the contraction and relaxation of the radial artery. The proposed ultralow power sensor measuring noninvasively continuous and accurate cardiopulmonary vital signs at once will be one of the most promising sensors for wearable devices to bring telehealth to our lives.

Impact of COVID-19 Pandemic on Physical Activity, Pain, Mood, and Sleep in Adults with Knee Osteoarthritis.

Objective: To examine changes in physical activity, sleep, pain and mood in people with knee osteoarthritis (OA) during the ongoing COVID-19 pandemic by leveraging an ongoing randomized clinical trial (RCT). Methods: Participants enrolled in a 12-month parallel two-arm RCT (NCT03064139) interrupted by the COVID-19 pandemic wore an activity monitor (Fitbit Charge 3) and filled out custom weekly surveys rating knee pain, mood, and sleep as part of the study. Data from 30 weeks of the parent study were used for this analysis. Daily step count and sleep duration were extracted from activity monitor data, and participants self-reported knee pain, positive mood, and negative mood via surveys. Metrics were averaged within each participant and then across all participants for pre-pandemic, stay-at-home, and reopening periods, reflecting the phased re-opening in the state of Massachusetts. Results: Data from 28 participants showed small changes with inconclusive clinical significance during the stay-at-home and reopening periods compared to pre-pandemic for all outcomes. Summary statistics suggested substantial variability across participants with some participants showing persistent declines in physical activity during the observation period. Conclusion: Effects of the COVID-19 pandemic on physical activity, sleep, pain, and mood were variable across individuals with OA. Specific reasons for this variability could not be determined. Identifying factors that could affect individuals with knee OA who may exhibit reduced physical activity and/or worse symptoms during major lifestyle changes (such as the ongoing pandemic) is important for providing targeted healthcare services and management advice towards those that could benefit from it the most.

Low-Cost IoT Based Wearable Respiratory Sensor for Covid-19 Patients

The three main Covid-19 symptoms are shortness of breath, coughing and fever. Currently, most of the patients who tested positive for COVID-19 are self-quarantined at home. Unfortunately, some home quarantine Covid-19 patients are brought in death to hospital. Therefore, e-health remote patient monitoring systems are needed. Although many e-health monitoring systems are proposed by the researcher, not many dedicated systems are suitable for COVID-19 specifically. Mostly do not have a respiratory rate monitoring function. Furthermore, many e-health devices in the market only feature local data storage and do not include Internet of Things (IoT) integration. In this work, we proposed a low-cost IoT based respiratory sensor for home quarantine Covid-19 patients to monitor the respiratory rate. The measured respiratory rate will be transmitted to Google Clould via WiFi connection and the user can read it through their computer or smartphone. Alert message will be generated if the respiratory rate reaches an unsafe threshold. The proposed device was tested with five samples and gave a 100% accuracy on respiratory rate measurement. The proposed prototype cost is much lower than the other respiratory monitoring devices in the market. The proposed device could reduce the mortality of home quarantine Covid-19 patients. © 2022 IJETAE Publication House. All rights reserved.

A Wearable Temperature Sensor Network to Address the COVID-19 Pandemic Emergency †

In the current COVID-19 emergency, to reduce the infection risk, several types of body temperature sensors, e.g., thermal imaging cameras and infrared thermometers, have been used to monitor people who access enclosed public spaces. In some buildings, where people are located for several hours, continuous monitoring could be useful. For this reason, in three schools, we have proposed and tested a body temperature sensor network based on wearable temperature sensors monitored via Bluetooth 5.0 using smartphones and/or custom gateways. The data are collected on a server via the internet, and custom software is used to control the measured temperature and to produce warnings automatically. © 2022 by the authors.

An IoT Prototype for Monitoring Covid19 Patients Using Real Time Data from Wearable Sensor Through Android App

In the age of modern technology peoples are still facing a great challenges to manage and monitor the infected patients of COVID-19. Many systems have been implemented to track the location of infected person to reduce the spread of diseases. In today's world IoT with the health care system plays an important role specially in this COVID situation. In this research an IoT based monitoring system is designed to monitor and measure different signs of COVID-19 using wearable device. It also sends notification to the proper authority by monitoring the activity of infected patient. To determine the condition of patient, sensor data are analyzed which is passed from edge node, as body sensor are connected to IoT cloud via edge node. Three layered architecture is implemented in our proposed design, wearable sensor layer, Peripheral Interface (API) layer and Android web layer. Different layer have different work, at first health symptom is determined by analyzing data from IoT sensor layer. In next layer information is stored in the cloud database to take immediate actions. Finally android application layer is used to send notifications and alerts for the infected patient. To predict the health condition and alarming the situation both API and mobile application communicate with each other. The designed system has simple structure and helps the authority to find the infected person. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Surface-enhanced Raman scattering as a potential strategy for wearable flexible sensing and point-of-care testing non-invasive medical diagnosis.

As a powerful and effective analytical tool, surface-enhanced Raman scattering (SERS) has attracted considerable research interest in the fields of wearable flexible sensing and non-invasive point-of-care testing (POCT) medical diagnosis. In this mini-review, we briefly summarize the design strategy, the development progress of wearable SERS sensors and its applications in this field. We present SERS substrate analysis of material design requirements for wearable sensors and highlight the benefits of novel plasmonic particle-in-cavity (PIC)-based nanostructures for flexible SERS sensors, as well as the unique interfacial adhesion effect and excellent mechanical properties of natural silk fibroin (SF) derived from natural cocoons, indicating promising futures for applications in the field of flexible electronic, optical, and electrical sensors. Additionally, SERS wearable sensors have shown great potential in the fields of different disease markers as well as in the diagnosis testing for COVID-19. Finally, the current challenges in this field are pointed out, as well as the promising prospects of combining SERS wearable sensors with other portable health monitoring systems for POCT medical diagnosis in the future.

A Review of Recent Advances in Vital Signals Monitoring of Sports and Health via Flexible Wearable Sensors.

In recent years, vital signals monitoring in sports and health have been considered the research focus in the field of wearable sensing technologies. Typical signals include bioelectrical signals, biophysical signals, and biochemical signals, which have applications in the fields of athletic training, medical diagnosis and prevention, and rehabilitation. In particular, since the COVID-19 pandemic, there has been a dramatic increase in real-time interest in personal health. This has created an urgent need for flexible, wearable, portable, and real-time monitoring sensors to remotely monitor these signals in response to health management. To this end, the paper reviews recent advances in flexible wearable sensors for monitoring vital signals in sports and health. More precisely, emerging wearable devices and systems for health and exercise-related vital signals (e.g., ECG, EEG, EMG, inertia, body movements, heart rate, blood, sweat, and interstitial fluid) are reviewed first. Then, the paper creatively presents multidimensional and multimodal wearable sensors and systems. The paper also summarizes the current challenges and limitations and future directions of wearable sensors for vital typical signal detection. Through the review, the paper finds that these signals can be effectively monitored and used for health management (e.g., disease prediction) thanks to advanced manufacturing, flexible electronics, IoT, and artificial intelligence algorithms; however, wearable sensors and systems with multidimensional and multimodal are more compliant.

The distribution of acquired peripheral nerve injuries associated with severe COVID-19 implicate a mechanism of entrapment neuropathy: a multicenter case series and clinical feasibility study of a wearable, wireless pressure sensor.

We diagnosed 66 peripheral nerve injuries in 34 patients who survived severe coronavirus disease 2019 (COVID-19). We combine this new data with published case series re-analyzed here (117 nerve injuries; 58 patients) to provide a comprehensive accounting of lesion sites. The most common are ulnar (25.1%), common fibular (15.8%), sciatic (13.1%), median (9.8%), brachial plexus (8.7%) and radial (8.2%) nerves at sites known to be vulnerable to mechanical loading. Protection of peripheral nerves should be prioritized in the care of COVID-19 patients. To this end, we report proof of concept data of the feasibility for a wearable, wireless pressure sensor to provide real time monitoring in the intensive care unit setting.

Biosensor Development

Research and development of biosensors has become the focus of many research disciplines due to the COVID-19 pandemic. The existence of biosensors has had a huge positive impact towards users due to their simple, rapid, cost effective, highly selective, and sensitive nature. The technology advancement has contributed to the improvement of healthcare systems and medicine. This chapter overviews the evolution of biosensors from the beginning until now. Three generations of biosensors with their commercialized products are highlighted. Besides that, a list of advance biomaterials which are bioresorbable and flexible are itemized. Then, the standard protocol of bio-sensing is emphasized.

Characterization of a Wearable LiDAR Sensor for Social Distancing

LiDAR sensors are widely used in autonomous driving, mobile robotics, aerospace, manufacturing, and many other fields. The speed, reliability, and range of LiDAR sensors can be affected by environmental conditions and usage patterns. Each application requires a deep understanding of sensor limitations. This paper explores the operational parameters of one of the most miniature and least expensive LiDAR sensors packaged in a wearable case. The target application, in this case, is social distancing during pandemics. This study focuses on the performance of miniature LiDAR sensors under different levels of light intensity, temperature, distance to the object, object size, angle of view, and object color. The experimental data enables a match between the sensor capabilities and application scenarios and provides direction for future work in improving the wearable sensors of this class. © 2022 IEEE.

LESSONS LEARNED FROM THE MYPAL DIGITAL HEALTH INTERVENTION: CAPITALIZING ON THE PATIENT REPORTED OUTCOMES (PRO) PARADIGM TOWARDS A PARTICIPATORY HEALTHCARE FOR PATIENTS WITH HEMATOLOGICAL MALIGNANCIES

Background: Chronic lymphocytic leukemia (CLL) and myelodysplastic syndromes (MDS) are two of the most frequent hematological malignancies. CLL and MDS are also considerably heterogeneous in terms of clinical course and response to treatment, ranging from relatively indolent to extremely aggressive. Thus, open issues abound regarding the impact of CLL and MDS and their treatment on patients' quality of life (QoL). Patient-reported outcomes (PROs) have been identified as an emerging paradigm, aiming to capture the patient's perspective onselfassessed health status. Obviously, these data are critical with regards to the evaluation of the treatment effects and the patients' QoL, while also enabling the positioning of the patient as a key stakeholder within the healthcare decision making process. Novel methodologies and eHealth approaches can be valuable for the adoption of the PRO paradigm in real-world settings as they can promote richer, less obtrusive and preemptive communication which could facilitate early recognition of potential symptoms of disease or treatment adverse effects (e.g., adverse drug reactions, lack of physical activity, worsening of QoL etc.). Aims: In this , we present the lessons learned thus far from the implementation of the MyPal project, a Horizon 2020 Research & Innovation Action aiming to foster palliative care for patients with CLL and MDS by leveraging the ePRO paradigm. Methods: MyPal aspires to empower patients and their caregivers to more accurately capture their symptoms/conditions, communicate them in a seamless and effective way to their healthcare providers (HCPs);and, ultimately, to foster action through advanced methods of identification of important deviations relevant to the patient's state and QoL. To this end, MyPal developed a technical platform including a mobile app for patients with CLL and MDS, collecting information via standardized questionnaires and other information sources (e.g., wearable sensors), also enabling spontaneous symptoms reporting, educational material provision, motivational messages, discussion guides, notifications etc. A data intensive web-based dashboard platform is also provided for healthcare professionals, providing real-time analytics, enabling a better view of collected PROs and other relevant information on patients' health status. Currently, a randomised clinical study is being conducted in 4 European countries to evaluate the proposed intervention and its potential impact on patients' QoL. Results: Based on this experience, a number of key issues have emerged: (a) while patients are generally positive about the use of eHealth, they are still reluctant about engaging in eHealth clinical studies;(b) digital literacy levels differ across different age groups as well as among different cultural contexts;(c) the COVID-19 pandemic seriously hindered patient recruitment due to the widely adopted recommendations for patients to avoid visits to hospitals unless absolutely necessary but (d) the COVID-19 pandemic also highlighted the potential benefits for HCPs of using eHealth tools in order to deliver patient care in a more decentralized and patient-centric fashion. Summary/Conclusion: In conclusion, MyPal is likely to provide important new evidence about how digital health systems can be used to improve QoL and facilitate better communication between patients with hematological malignancies and HCPs.

A Smart Wristband Integrated with an IoT-Based Alarming System for Real-Time Sweat Alcohol Monitoring.

Breathalyzer is a common approach to measuring blood alcohol concentration (BAC) levels of individuals suspected of drunk driving. Nevertheless, this device is relatively high-cost, inconvenient for people with limited breathing capacity, and risky for COVID-19 exposure. Here, we designed and developed a smart wristband integrating a real-time noninvasive sweat alcohol metal oxide (MOX) gas sensor with a Drunk Mate, an Internet of Thing (IoT)-based alarming system. A MOX sensor acquired transdermal alcohol concentration (TAC) which was converted to BAC and sent via the IoT network to the Blynk application platform on a smartphone, triggering alarming messages on LINE Notify. A user would receive an immediate alarming message when his BAC level reached an illegal alcohol concentration limit (BAC 50 mg%; TAC 0.70 mg/mL). The sensor readings showed a high linear correlation with TAC (R2 = 0.9815; limit of detection = 0.045 mg/mL) in the range of 0.10-1.05 mg/mL alcohol concentration in artificial sweat, achieving an accuracy of 94.66%. The sensor readings of ethanol in water were not statistically significantly different (p > 0.05) from the measurements in artificial sweat and other sweat-related solutions, suggesting that the device responded specifically to ethanol and was not affected by other electrolytes in the artificial sweat. Moreover, the device could continuously monitor TAC levels simulated in real-time in an artificial sweat testing system. With the integration of an IoT-based alarming system, the smart wristband developed from a commercial gas sensor presented here offers a promising low-cost MOX gas sensor monitoring technology for noninvasive and real-time sweat alcohol measurement and monitoring.

An IoT-enabled Electronic Textile-based Flexible Body Sensor Network for Real-time Health Monitoring in Assisted Living during Pandemic

This paper presents a cost-effective and flexible electronic textile sensor with high sensitivity and fast response and demonstrates its versatile applications, including real-time measurements of finger kinematics, phonation, cough patterns, as well as subtle muscle movements (i.e., eye reflex). The sensor can discriminate between speech and cough patterns, thereby expanding its applications to COVID-19 detection, speech rehabilitation training, and human/machine interactions. A combination of different sensor data is essential to acquire clinically significant information. Therefore, a sensor array is interfaced with the LoRa communication protocol to establish an Internet of Things (IoT)-based electronic textile framework. The IoT integration allows remote monitoring of body kinematics and physiological parameters. Therefore, the proposed IoT-based framework holds the potential to provide real-time and continuous health monitoring to allow immediate intervention during this pandemic. © 2022 IEEE.

The Impact of COVID-19 on People with Parkinson's: Insights from Wearable Sensors

Objective: To describe changes in daily activity measured by wearable sensors in participants with Parkinson's disease (PD) following the COVID-19 pandemic. Background: Digital tools provide objective, frequent and sensitive data collection in real-world settings. In a natural history study of PD, participants used wearable sensors before and after COVID-19 shutdowns. Design/Methods: At research visits throughout this two-year study at the University of Rochester Medical Center, participants wore sensors with accelerometer and gyroscopic capabilities and completed questionnaires. Following each visit, participants wore sensors remotely for 7 days during waking hours. Participant position and activity from days 1-6 of wear was classified from sensor data. Results: Prior to March 14 2020, when COVID-19 shutdowns began in Monroe County, NY, 17 participants with PD (70.4 (6.3) years) and 13 controls (61.1 (13.5) years) completed a baseline visit. All 30 later completed a month 12 visit after COVID-19 shutdowns. Sensor wear was comparable at baseline (13.9 (1.4) hours/day) and month 12 (13.74 (2.1) hours/day). At month 12, PD participants walked an average of 1709 (1457) steps/day, approximately 17% less than at baseline (2048 (1416) steps/day), with considerable individual variation. PD participants spent approximately 20% more time lying while awake at month 12 (112.7 (149.9) min) than at baseline (93.6 (103.1) min). Daytime sleep did not increase from baseline (39.6 (39.3) min) to month 12 (39.2 (32.8) min). PD and control participants reported greater anxiety and depression at month 12. From baseline to month 12, controls had similar activity trends as participants with PD, but walked more, spent less time lying, had less daytime sleep, and reported less depression and anxiety at both time points. Conclusions: Following the emergence of COVID-19, participants with PD walked less and spent more time resting. These data provide an objective measure of the pandemic's impact on a small cohort of individuals with PD.

Ambient and Wearable Sensor Technologies for Energy Expenditure Quantification of Ageing Adults.

COVID-19 has affected daily life in unprecedented ways, with dramatic changes in mental health, sleep time and level of physical activity. These changes have been especially relevant in the elderly population, with important health-related consequences. In this work, two different sensor technologies were used to quantify the energy expenditure of ageing adults. To this end, a technological platform based on Raspberry Pi 4, as an elaboration unit, was designed and implemented. It integrates an ambient sensor node, a wearable sensor node and a coordinator node that uses the information provided by the two sensor technologies in a combined manner. Ambient and wearable sensors are used for the real-time recognition of four human postures (standing, sitting, bending and lying down), walking activity and for energy expenditure quantification. An important first aim of this work was to realize a platform with a high level of user acceptability. In fact, through the use of two unobtrusive sensors and a low-cost processing unit, the solution is easily accessible and usable in the domestic environment; moreover, it is versatile since it can be used by end-users who accept being monitored by a specific sensor. Another added value of the platform is the ability to abstract from sensing technologies, as the use of human posture and walking activity for energy expenditure quantification enables the integration of a wide set of devices, provided that they can reproduce the same set of features. The obtained results showed the ability of the proposed platform to automatically quantify energy expenditure, both with each sensing technology and with the combined version. Specifically, for posture and walking activity classification, an average accuracy of 93.8% and 93.3% was obtained, respectively, with the wearable and ambient sensor, whereas an improvement of approximately 4% was reached using data fusion. Consequently, the estimated energy expenditure quantification always had a relative error of less than 3.2% for each end-user involved in the experimentation stage, classifying the high level information (postures and walking activities) with the combined version of the platform, justifying the proposed overall architecture from a hardware and software point of view.

Internet of Things based Wearable Smart Gadget for COVID-19 Patients Monitoring

It is extremely difficult to monitor and manage infected patients during the COVID-19 pandemic. This IoT wearable monitoring gadget is developed to measure the indicators of COVID-19. Patients' GPS data is used to notify medical authorities of their infection status. A wearable sensor is affixed to the body and connected to an edge node in the IoT cloud where the data is processed and analyzed in order to monitor health. A temperature sensor, GPS, SpO2 sensor, IR sensor, and accelerometer make up the system. The Arduino UNO processor is used in this gadget. The patient's body temperature is obtained using the temperature sensor. The location of the infected patient is tracked using a GPS sensor. Human movement is detected using an accelerometer. The SpO2 sensor measures the blood oxygen saturation level. The heart rate is detected using a pulse sensor. Information about preventive measures, warnings, and actions is stored in a cloud database. COVID-19 symptom readings are measured using this approach for monitoring and analysis. © 2022 IEEE.

Design of IoT Enabled Cloud Assisted Health Monitoring System for Covid-19 Patients

Covid-19 is one of the life-threatening diseases which requires intensifying attention to combat disease by designing a smart and effective healthcare system for patients towards diagnosing and managing the Covid-19 disease. Various systems have been developed for diagnosing patient with diseases, but intelligent and feasible solution to explore and monitor the accurate predictive health conditions of affected patients has not been provided yet. In this paper, a new Contactless IoT-enabled cloud-assisted health monitoring system has been designed and developed. The system is made up of unobtrusive sensors, a data acquisition unit, a microcontroller, wi-fi Module, Web server, and Web application or mobile application. It illustrates the design of the system to monitor and detect the severity of the coronavirus in the patients using various unobtrusive sensors to measure disease-specific vital parameters such as heart rate, temperature, oxygen level and pulse rate as main symptoms of the coronavirus are high fever, fatigue, and difficult breathing. Sensor acquired patient data is transformed using the HTTP protocol to cloud server using microcontroller and wi-fi module in real-time. Transformed data of patient condition is processed in the cloud server using data predictive algorithms such as Severity Defined Convolution Neural Network with respect to data collected and severity specific data thresholds and severity class predicted patient information will alarm the healthcare provider on the abnormalities detected in the patient health. A particular model is capable of forecasting the health situation of the patients. Experimental analysis of the proposed architectures finds it effective in monitoring the status of the severity of breathing on the patients. Finally, the performance of the architecture is validated over accuracy and scalability measures. © 2022 IEEE.