Wireless patch sensor for remote monitoring of heart rate, respiration, activity, and falls

Digital health technology to support care and improve outcomes of chronic kidney disease patients: as a case illustration, the Withings toolkit health sensing tools

Cardiovascular disease (CVD) is a major burden in dialysis-dependent chronic kidney disease (CKD5D) patients. Several factors contribute to this vulnerability including traditional risk factors such as age, gender, life style and comorbidities, and non-traditional ones as part of dialysis-induced systemic stress. In this context, it appears of utmost importance to bring a closer attention to CVD monitoring in caring for CKD5D patients to ensure early and appropriate intervention for improving their outcomes. Interestingly, new home-used, self-operated, connected medical devices offer convenient and new tools for monitoring in a fully automated and ambulatory mode CKD5D patients during the interdialytic period. Sensoring devices are installed with WiFi or Bluetooth. Some devices are also available in a cellular version such as the Withings Remote Patient Monitoring (RPM) solution. These devices analyze the data and upload the results to Withings HDS (Hybrid data security) platform servers. Data visualization can be viewed by the patient using the Withings Health Mate application on a smartphone, or with a web interface. Health Care Professionals (HCP) can also visualize patient data via the Withings web-based RPM interface. In this narrative essay, we analyze the clinical potential of pervasive wearable sensors for monitoring ambulatory dialysis patients and provide an assessment of such toolkit digital medical health devices currently available on the market. These devices offer a fully automated, unobtrusive and remote monitoring of main vital functions in ambulatory subjects. These unique features provide a multidimensional assessment of ambulatory CKD5D patients covering most physiologic functionalities, detecting unexpected disorders (i.e., volume overload, arrhythmias, sleep disorders) and allowing physicians to judge patient's response to treatment and recommendations. In the future, the wider availability of such pervasive health sensing and digital technology to monitor patients at an affordable cost price will improve the personalized management of CKD5D patients, so potentially resulting in improvements in patient quality of life and survival.

Implementation of a real-time fall detection system based on hybrid threshold analysis algorithm and machine learning algorithm

With the increasing global aging population, the health of the elderly has become a global concern. Accidental falls, as one of the major causes of health and safety issues affecting the elderly, can cause serious hazards. In this paper, a fall detection system is proposed to be able to deliver timely information after a fall. The acceleration and angular velocity time series extracted from motion were used to describe human motion features. Hybrid threshold analysis algorithm and machine learning algorithm are used for classification between falls and activities of daily living (ADLs). The fall detection results showed 98.55% accuracy, 98.16% sensitivity, and 98.73% specificity. The result is higher than the single-threshold algorithm and slightly lower than the machine learning algorithm. In addition, the hybrid algorithm of fall detection in this paper is to put the threshold analysis algorithm in the edge device for calculation and put the machine learning algorithm in the cloud server for calculation. Since the single machine learning algorithm needs to transmit data to the cloud server all the time, the hybrid algorithm has lower power consumption than machine learning algorithms, and the average alarm time is shorter, making it more suitable for actual systems.

Remote Monitoring of Chronic Critically Ill Patients after Hospital Discharge: A Systematic Review

Background: Over the past few decades, critical care has seen many advancements. These advancements resulted in a considerable increase in the prevalence of chronically critically ill patients requiring prolonged medical care, which led to a massive increase in healthcare utilization. Methods: We performed a search for suitable articles using PubMed and Google Scholar from the inception of these databases to 15 May 2021. Results: Thirty-four articles were included in the review and analyzed. We described the following characteristics and problems with chronic critically ill patient management: the patient population, remote monitoring, the monitoring of physiological parameters in chronic critically ill patients, the anatomical location of sensors, the barriers to implementation, and the main technology-related issues. The main challenges in the management of these patients are (1) the shortage of caretakers, (2) the periodicity of vital function monitoring (e.g., episodic measuring of blood pressure leads to missing important critical events such as hypertension, hypotension, and hypoxia), and (3) failure to catch and manage critical physiological events at the right time, which can result in poor outcomes. Conclusions: The prevalence of critically ill patients is expected to grow. Technical solutions can greatly assist medical personnel and caregivers. Wearable devices can be used to monitor blood pressure, heart rate, pulse, respiratory rate, blood oxygen saturation, metabolism, and central nervous system function. The most important points that should be addressed in future studies are the performance of the remote monitoring systems, safety, clinical and economic outcomes, as well as the acceptance of the devices by patients, caretakers, and healthcare professionals.

Perceptions on the Use of Wearable Sensors and Continuous Monitoring in Surgical Patients: Interview Study Among Surgical Staff

BACKGROUND

Continuous vital sign monitoring by using wearable sensors may result in the earlier detection of patient deterioration and sepsis. Few studies have explored the perspectives of surgical team members on the use of such sensors in surgical patients.

OBJECTIVE

This study aims to understand the views of surgical team members regarding novel wearable sensors for surgical patients.

METHODS

Wearable sensors that monitor vital signs (heart rate, respiratory rate, and temperature) continuously were used by acute surgical patients. The opinions of surgical staff who were treating patients with these sensors were collated through in-depth semistructured interviews to thematic saturation. Interviews were audio recorded, transcribed, and analyzed via thematic analysis.

RESULTS

A total of 48 interviews were performed with senior and junior surgeons and senior and junior nurses. The main themes of interest that emerged from the interviews were (1) problems with current monitoring, (2) the anticipated impact of wearables on patient safety, (3) the impact on staff, (4) the impact on patients overall, (5) potential new changes, and (6) the future and views on technology.

CONCLUSIONS

Overall, the feedback from staff who were continuously monitoring surgical patients via wearable sensors was positive, and relatively few concerns were raised. Surgical staff members identify problems with current monitoring and anticipate that sensors will both improve patient safety and be the future of monitoring.

A Wearable Tele-Health System towards Monitoring COVID-19 and Chronic Diseases

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a pandemic since early 2020. The coronavirus disease 2019 (COVID-19) has already caused more than three million deaths worldwide and affected people's physical and mental health. COVID-19 patients with mild symptoms are generally required to self-isolate and monitor for symptoms at least for 14 days in the case the disease turns towards severe complications. In this work, we overviewed the impact of COVID-19 on the patients' general health with a focus on their cardiovascular, respiratory and mental health, and investigated several existing patient monitoring systems. We addressed the limitations of these systems and proposed a wearable telehealth solution for monitoring a set of physiological parameters that are critical for COVID-19 patients such as body temperature, heart rate, heart rate variability, blood oxygen saturation, respiratory rate, blood pressure, and cough. This physiological information can be further combined to potentially estimate the lung function using artificial intelligence (AI) and sensor fusion techniques. The prototype, which includes the hardware and a smartphone app, showed promising results with performance comparable to or better than similar commercial devices, thus potentially making the proposed system an ideal wearable solution for long-term monitoring of COVID-19 patients and other chronic diseases.

Bluetooth Low Energy Beacon Sensors to Document Handheld Magnifier Use at Home by People with Low Vision

We explored the feasibility of using Bluetooth low energy (BLE) beacon sensors to determine when individuals with low vision (LV) use handheld magnifiers at home. Knowing the frequency and duration of magnifier use would be helpful to document increased magnifier use after successful rehabilitation training, or conversely, to know when someone has abandoned a magnifier and requires assistance. Estimote Sticker BLE beacon sensors were attached to the handles of optical handheld magnifiers and dispensed to eight LV subjects to use at home. Temperature and motion data from the BLE beacon sensors were collected every second by a custom mobile application on a nearby smartphone and transmitted to a secure database server. Subjects noted the date and start/end times of their magnifier use in a diary log. Each of the 99 diary-logged self-reports of magnifier use across subjects was associated with BLE beacon sensor recordings of motion (mean 407 instances; SD 365) and increased temperature (mean 0.20 °C per minute; SD 0.16 °C) (mean total magnitude 5.4 °C; SD 2.6 °C). Diary-logged duration of magnifier use (mean 42 min; SD 24) was significantly correlated with instances of motion (p < 0.001) and rate of temperature increase (p < 0.001) recorded by the BLE beacon sensors. The BLE beacon sensors reliably detected meaningfully increased temperature, coupled with numerous instances of motion, when magnifiers were used for typical reading tasks at home by people with LV.

A Chest Patch for Continuous Vital Sign Monitoring: Clinical Validation Study During Movement and Controlled Hypoxia

BACKGROUND

The standard of care in general wards includes periodic manual measurements, with the data entered into track-and-trigger charts, either on paper or electronically. Wearable devices may support health care staff, improve patient safety, and promote early deterioration detection in the interval between periodic measurements. However, regulatory standards for ambulatory cardiac monitors estimating heart rate (HR) and respiratory rate (RR) do not specify performance criteria during patient movement or clinical conditions in which the patient's oxygen saturation varies. Therefore, further validation is required before clinical implementation and deployment of any wearable system that provides continuous vital sign measurements.

OBJECTIVE

The objective of this study is to determine the agreement between a chest-worn patch (VitalPatch) and a gold standard reference device for HR and RR measurements during movement and gradual desaturation (modeling a hypoxic episode) in a controlled environment.

METHODS

After the VitalPatch and gold standard devices (Philips MX450) were applied, participants performed different movements in seven consecutive stages: at rest, sit-to-stand, tapping, rubbing, drinking, turning pages, and using a tablet. Hypoxia was then induced, and the participants' oxygen saturation gradually reduced to 80% in a controlled environment. The primary outcome measure was accuracy, defined as the mean absolute error (MAE) of the VitalPatch estimates when compared with HR and RR gold standards (3-lead electrocardiography and capnography, respectively). We defined these as clinically acceptable if the rates were within 5 beats per minute for HR and 3 respirations per minute (rpm) for RR.

RESULTS

Complete data sets were acquired for 29 participants. In the movement phase, the HR estimates were within prespecified limits for all movements. For RR, estimates were also within the acceptable range, with the exception of the sit-to-stand and turning page movements, showing an MAE of 3.05 (95% CI 2.48-3.58) rpm and 3.45 (95% CI 2.71-4.11) rpm, respectively. For the hypoxia phase, both HR and RR estimates were within limits, with an overall MAE of 0.72 (95% CI 0.66-0.78) beats per minute and 1.89 (95% CI 1.75-2.03) rpm, respectively. There were no significant differences in the accuracy of HR and RR estimations between normoxia (≥90%), mild (89.9%-85%), and severe hypoxia (<85%).

CONCLUSIONS

The VitalPatch was highly accurate throughout both the movement and hypoxia phases of the study, except for RR estimation during the two types of movements. This study demonstrated that VitalPatch can be safely tested in clinical environments to support earlier detection of cardiorespiratory deterioration.

TRIAL REGISTRATION

ISRCTN Registry ISRCTN61535692; https://www.isrctn.com/ISRCTN61535692.

Evaluating the Validity and Utility of Wearable Technology for Continuously Monitoring Patients in a Hospital Setting: Systematic Review

BACKGROUND

The term posthospital syndrome has been used to describe the condition in which older patients are transiently frail after hospitalization and have a high chance of readmission. Since low activity and poor sleep during hospital stay may contribute to posthospital syndrome, the continuous monitoring of such parameters by using affordable wearables may help to reduce the prevalence of this syndrome. Although there have been systematic reviews of wearables for physical activity monitoring in hospital settings, there are limited data on the use of wearables for measuring other health variables in hospitalized patients.

OBJECTIVE

This systematic review aimed to evaluate the validity and utility of wearable devices for monitoring hospitalized patients.

METHODS

This review involved a comprehensive search of 7 databases and included articles that met the following criteria: inpatients must be aged >18 years, the wearable devices studied in the articles must be used to continuously monitor patients, and wearables should monitor biomarkers other than solely physical activity (ie, heart rate, respiratory rate, blood pressure, etc). Only English-language studies were included. From each study, we extracted basic demographic information along with the characteristics of the intervention. We assessed the risk of bias for studies that validated their wearable readings by using a modification of the Consensus-Based Standards for the Selection of Health Status Measurement Instruments.

RESULTS

Of the 2012 articles that were screened, 14 studies met the selection criteria. All included articles were observational in design. In total, 9 different commercial wearables for various body locations were examined in this review. The devices collectively measured 7 different health parameters across all studies (heart rate, sleep duration, respiratory rate, oxygen saturation, skin temperature, blood pressure, and fall risk). Only 6 studies validated their results against a reference device or standard. There was a considerable risk of bias in these studies due to the low number of patients in most of the studies (4/6, 67%). Many studies that validated their results found that certain variables were inaccurate and had wide limits of agreement. Heart rate and sleep were the parameters with the most evidence for being valid for in-hospital monitoring. Overall, the mean patient completion rate across all 14 studies was >90%.

CONCLUSIONS

The included studies suggested that wearable devices show promise for monitoring the heart rate and sleep of patients in hospitals. Many devices were not validated in inpatient settings, and the readings from most of the devices that were validated in such settings had wide limits of agreement when compared to gold standards. Even some medical-grade devices were found to perform poorly in inpatient settings. Further research is needed to determine the accuracy of hospitalized patients' digital biomarker readings and eventually determine whether these wearable devices improve health outcomes.

Elderly Perception on the Internet of Things-Based Integrated Smart-Home System

An integrated smart home system (ISHS) is an effective way to improve the quality of life of the elderly. The elderly’s willingness is essential to adopt an ISHS; to the best of our knowledge, no study has investigated the elderly’s perception of ISHS. Consequently, this study aims to investigate the elderly’s perception of the ISHS by comprehensively evaluating its possible benefits and negative responses. A set of sensors required for an ISHS was determined, and interviews were designed based on four factors: perceived comfort, perceived usability, perceived privacy, and perceived benefit. Subsequently, technological trials of the sensor-set followed by two focus group interviews were conducted on nine independently living elderly participants at a senior welfare center in South Korea. Consistent with previous studies, the results of this investigation indicate that elderly participants elicited negative responses regarding usability complexity, and discomfort to daily activities. Despite such negative responses, after acquiring enough awareness about the ISHS’s benefits, the elderly acknowledged its necessity and showed a high level of willingness. Furthermore, these results indicate that for a better adoption of an ISHS, sufficient awareness regarding its benefits and development of elderly-friendly smart home sensors that minimize negative responses are required.

Use of wireless respiratory rate sensor monitoring during opioid patient-controlled analgesia after gynaecological surgery: A prospective cohort study

Background and Aims:

Respiratory depression is a rare but serious complication during opioid administration. Therefore, early detection of signs of deterioration is paramount. The current standard of care of using manual intermittent respiratory rate (RR) measurement is labour intensive and inefficient. We evaluated a wireless sensor monitor, Aingeal (Renew Health Ltd, Ireland), to continuously monitor RR, heart rate (HR) and temperature compared to standard clinical measurements.

Methods:

Patients who underwent major gynaecological operations and received postoperative opioid patient-controlled analgesia were recruited. Patients were connected to the sensor monitor via a central station software platform. The primary outcome was comparison of RR between sensor and nursing monitoring, with secondary outcomes being HR and temperature between two methods. Feedback from patients and healthcare providers was also collected. Bland-Altman analyses were used to compare the vital signs recorded in sensor against those in patient's electronic record.

Results:

A total of 1121 hours of vital signs data were analysed. Bias for RR was -0.90 (95% confidence interval (CI): -9.39, 7.60) breaths/min between nursing and averaged sensor readings. Bias for heart rate was -1.12 (95% CI: -26.27, 24.03) and bias for temperature was 1.45 (95% CI: -5.67, 2.76) between the two methods.

Conclusion:

There is satisfactory agreement of RR measurements, as well as HR and temperature measurements, by the wireless sensor monitor with standard clinical intermittent monitoring with overall good user experience.

A Machine Learning Multi-Class Approach for Fall Detection Systems Based on Wearable Sensors with a Study on Sampling Rates Selection

Falls are dangerous for the elderly, often causing serious injuries especially when the fallen person stays on the ground for a long time without assistance. This paper extends our previous work on the development of a Fall Detection System (FDS) using an inertial measurement unit worn at the waist. Data come from SisFall, a publicly available dataset containing records of Activities of Daily Living and falls. We first applied a preprocessing and a feature extraction stage before using five Machine Learning algorithms, allowing us to compare them. Ensemble learning algorithms such as Random Forest and Gradient Boosting have the best performance, with a Sensitivity and Specificity both close to 99%. Our contribution is: a multi-class classification approach for fall detection combined with a study of the effect of the sensors' sampling rate on the performance of the FDS. Our multi-class classification approach splits the fall into three phases: pre-fall, impact, post-fall. The extension to a multi-class problem is not trivial and we present a well-performing solution. We experimented sampling rates between 1 and 200 Hz. The results show that, while high sampling rates tend to improve performance, a sampling rate of 50 Hz is generally sufficient for an accurate detection.

Methods for Adventitious Respiratory Sound Analyzing Applications Based on Smartphones: A Survey

Detection and classification of adventitious acoustic lung sounds plays an important role in diagnosing, monitoring, controlling and, caring the patients with lung diseases. Such systems can be presented as different platforms like medical devices, standalone software or smartphone application. Ubiquity of smartphones and widespread use of the corresponding applications make such a device an attractive platform for hosting the detection and classification systems for adventitious lung sounds. In this paper, the smartphone-based systems for automatic detection and classification of the adventitious lung sounds are surveyed. Such adventitious sounds include cough, wheeze, crackle and, snore. Relevant sounds related to abnormal respiratory activities are considered as well. The methods are shortly described and the analyzing algorithms are explained. The analysis includes detection and/or classification of the sound events. A summary of the main surveyed methods together with the classification parameters and used features for the sake of comparison is given. Existing challenges, open issues and future trends will be discussed as well.

Vital Sign Monitoring Using FMCW Radar in Various Sleeping Scenarios

Remote monitoring of vital signs for studying sleep is a user-friendly alternative to monitoring with sensors attached to the skin. For instance, remote monitoring can allow unconstrained movement during sleep, whereas detectors requiring a physical contact may detach and interrupt the measurement and affect sleep itself. This study evaluates the performance of a cost-effective frequency modulated continuous wave (FMCW) radar in remote monitoring of heart rate and respiration in scenarios resembling a set of normal and abnormal physiological conditions during sleep. We evaluate the vital signs of ten subjects in different lying positions during various tasks. Specifically, we aim for a broad range of both heart and respiration rates to replicate various real-life scenarios and to test the robustness of the selected vital sign extraction methods consisting of fast Fourier transform based cepstral and autocorrelation analyses. As compared to the reference signals obtained using Embla titanium, a certified medical device, we achieved an overall relative mean absolute error of 3.6% (86% correlation) and 9.1% (91% correlation) for the heart rate and respiration rate, respectively. Our results promote radar-based clinical monitoring by showing that the proposed radar technology and signal processing methods accurately capture even such alarming vital signs as minimal respiration. Furthermore, we show that common parameters for heart rate variability can also be accurately extracted from the radar signal, enabling further sleep analyses.

Acoustic Matching Layer Films Using B-Stage Thermosetting Polymer Resins for Ultrasound Transducer Applications

Acoustic matching layer films (MLFs) were fabricated using B-stage thermosetting polymer resins with various volume fractions of alumina and tungsten powders. After making certain thickness MLFs, ultrasonic matching layers were fabricated using a simple molding process. The thickness of the matching layers can be precisely adjusted from several micrometer to hundreds of micrometer, without any grinding process. Experimental values of acoustic impedances of the matching layers were in good agreement with theoretical values calculated by the Devaney model. Using the optimized acoustic matching layer by the MLFs, the maximum intensity and the fractional bandwidth of the ultrasonic transducer were increased by 10% and 37% respectively. The effectiveness of the matching layer using MLFs was successfully verified.

Sensing Systems for Respiration Monitoring: A Technical Systematic Review

Respiratory monitoring is essential in sleep studies, sport training, patient monitoring, or health at work, among other applications. This paper presents a comprehensive systematic review of respiration sensing systems. After several systematic searches in scientific repositories, the 198 most relevant papers in this field were analyzed in detail. Different items were examined: sensing technique and sensor, respiration parameter, sensor location and size, general system setup, communication protocol, processing station, energy autonomy and power consumption, sensor validation, processing algorithm, performance evaluation, and analysis software. As a result, several trends and the remaining research challenges of respiration sensors were identified. Long-term evaluations and usability tests should be performed. Researchers designed custom experiments to validate the sensing systems, making it difficult to compare results. Therefore, another challenge is to have a common validation framework to fairly compare sensor performance. The implementation of energy-saving strategies, the incorporation of energy harvesting techniques, the calculation of volume parameters of breathing, or the effective integration of respiration sensors into clothing are other remaining research efforts. Addressing these and other challenges outlined in the paper is a required step to obtain a feasible, robust, affordable, and unobtrusive respiration sensing system.

Current Status and Future Challenges of Sleep Monitoring Systems: Systematic Review

Background

Sleep is essential for human health. Considerable effort has been put into academic and industrial research and in the development of wireless body area networks for sleep monitoring in terms of nonintrusiveness, portability, and autonomy. With the help of rapid advances in smart sensing and communication technologies, various sleep monitoring systems (hereafter, sleep monitoring systems) have been developed with advantages such as being low cost, accessible, discreet, contactless, unmanned, and suitable for long-term monitoring.

Objective

This paper aims to review current research in sleep monitoring to serve as a reference for researchers and to provide insights for future work. Specific selection criteria were chosen to include articles in which sleep monitoring systems or devices are covered.

Methods

This review investigates the use of various common sensors in the hardware implementation of current sleep monitoring systems as well as the types of parameters collected, their position in the body, the possible description of sleep phases, and the advantages and drawbacks. In addition, the data processing algorithms and software used in different studies on sleep monitoring systems and their results are presented. This review was not only limited to the study of laboratory research but also investigated the various popular commercial products available for sleep monitoring, presenting their characteristics, advantages, and disadvantages. In particular, we categorized existing research on sleep monitoring systems based on how the sensor is used, including the number and type of sensors, and the preferred position in the body. In addition to focusing on a specific system, issues concerning sleep monitoring systems such as privacy, economic, and social impact are also included. Finally, we presented an original sleep monitoring system solution developed in our laboratory.

Results

By retrieving a large number of articles and abstracts, we found that hotspot techniques such as big data, machine learning, artificial intelligence, and data mining have not been widely applied to the sleep monitoring research area. Accelerometers are the most commonly used sensor in sleep monitoring systems. Most commercial sleep monitoring products cannot provide performance evaluation based on gold standard polysomnography.

Conclusions

Combining hotspot techniques such as big data, machine learning, artificial intelligence, and data mining with sleep monitoring may be a promising research approach and will attract more researchers in the future. Balancing user acceptance and monitoring performance is the biggest challenge in sleep monitoring system research.

Current Evidence for Continuous Vital Signs Monitoring by Wearable Wireless Devices in Hospitalized Adults: Systematic Review

BACKGROUND

Continuous monitoring of vital signs by using wearable wireless devices may allow for timely detection of clinical deterioration in patients in general wards in comparison to detection by standard intermittent vital signs measurements. A large number of studies on many different wearable devices have been reported in recent years, but a systematic review is not yet available to date.

OBJECTIVE

The aim of this study was to provide a systematic review for health care professionals regarding the current evidence about the validation, feasibility, clinical outcomes, and costs of wearable wireless devices for continuous monitoring of vital signs.

METHODS

A systematic and comprehensive search was performed using PubMed/MEDLINE, EMBASE, and Cochrane Central Register of Controlled Trials from January 2009 to September 2019 for studies that evaluated wearable wireless devices for continuous monitoring of vital signs in adults. Outcomes were structured by validation, feasibility, clinical outcomes, and costs. Risk of bias was determined by using the Mixed Methods Appraisal Tool, quality assessment of diagnostic accuracy studies 2nd edition, or quality of health economic studies tool.

RESULTS

In this review, 27 studies evaluating 13 different wearable wireless devices were included. These studies predominantly evaluated the validation or the feasibility outcomes of these devices. Only a few studies reported the clinical outcomes with these devices and they did not report a significantly better clinical outcome than the standard tools used for measuring vital signs. Cost outcomes were not reported in any study. The quality of the included studies was predominantly rated as low or moderate.

CONCLUSIONS

Wearable wireless continuous monitoring devices are mostly still in the clinical validation and feasibility testing phases. To date, there are no high quality large well-controlled studies of wearable wireless devices available that show a significant clinical benefit or cost-effectiveness. Such studies are needed to help health care professionals and administrators in their decision making regarding implementation of these devices on a large scale in clinical practice or in-home monitoring.

Simple Prediction of Metabolic Equivalents of Daily Activities Using Heart Rate Monitor without Calibration of Individuals

Background: Heart rate (HR) during physical activity is strongly affected by the level of physical fitness. Therefore, to assess the effects of fitness, we developed predictive equations to estimate the metabolic equivalent (MET) of daily activities, which includes low intensity activities, by % HR reserve (%HRR), resting HR, and multiple physical characteristics. Methods: Forty volunteers between the ages of 21 and 55 performed 20 types of daily activities while recording HR and sampling expired gas to evaluate METs values. Multiple regression analysis was performed to develop prediction models of METs with seven potential predictors, such as %HRR, resting HR, and sex. The contributing parameters were selected based on the brute force method. Additionally, leave-one-out method was performed to validate the prediction models. Results: %HRR, resting HR, sex, and height were selected as the independent variables. %HRR showed the highest contribution in the model, while the other variables exhibited small variances. METs were estimated within a 17.3% difference for each activity, with large differences in document arrangement while sitting (+17%), ascending stairs (−8%), and descending stairs (+8%). Conclusions: The results showed that %HRR is a strong predictor for estimating the METs of daily activities. Resting HR and other variables were mild contributors. (201 words)

Remote Patient Monitoring in Adults Receiving Transfusion or Infusion for Hematological Disorders Using the VitalPatch and accelerateIQ Monitoring System: Quantitative Feasibility Study

Background

Frequent vital sign monitoring during and after transfusion of blood products and certain chemotherapies or immunotherapies is critical for detecting infusion reactions and treatment management in patients. Currently, patients return home with instructions to contact the clinic if they feel unwell. Continuous monitoring of vital signs for hematological patients treated with immunotherapy or chemotherapy or receiving blood transfusions using wearable electronic biosensors during and post treatment may improve the safety of these treatments and make remote data collection in an outpatient care setting possible.

Objective

This study aimed to evaluate patient experiences with the VitalPatch wearable sensor (VitalConnect) and to evaluate the usability of data generated by the physIQ accelerateIQ monitoring system for the investigator and nurse.

Methods

A total of 12 patients with hematological disorders receiving red blood cell transfusions, an intravenous (IV) proteasome inhibitor, or an IV immunotherapy agent were included in the study and wore the VitalPatch for 12 days. Patients completed questionnaires focusing on wearability and nurses completed questionnaires focusing on the usability of the VitalPatch.

Results

A total of 12 patients were enrolled over 9 months, with 4 receiving red blood cell transfusions, 4 receiving IV proteasome inhibitors, and 4 receiving IV immunotherapy. These patients were treated for diseases such as multiple myeloma, myelodysplastic syndrome, and non-Hodgkin lymphoma. Of these patients, 83% (10/12) were aged 60 years and older. A total of 4 patients (4/12, 33%) withdrew from the study (3 because of skin irritation and 1 because of patch connection issues). Patients wore biosensor patches at baseline and for 1-week post administration. Patient-reported outcomes (PROs) were collected at baseline, day 1, day 5, and day 8. No difference in the PRO was observed when nurses or patients applied the patch. PRO data indicated minimal impact on the patient’s life. Ease of use, influence on sleep, impact on follow-up of health, or discomfort with continuous monitoring did not change between baseline and day 8. Changes in PRO were observed on day 5, where a 20% (2/10) increase in skin irritation was reported. Withdrawals because of skin irritation were reported in all cases when wearing the second patch. Nurses reported the placement of the VitalPatch to be easy and felt measurements to be reliable.

Conclusions

Generally, the VitalPatch was well tolerated and shown to be an attractive device because of its wearability and low impact on daily activities in patients, therefore making it suitable for implementation in future studies.

Exploring barriers to physical activity of patients at the internal medicine and surgical wards: a retrospective analysis of continuously collected data

PURPOSE

To analyse physical activity of patients during their hospital stay and to explore the relationship between physical activity and barriers to physical activity.

METHODS

This was a secondary analysis of physical activity data for patients admitted to the internal medicine and surgical wards. Physical activity data, collected with a wireless patch sensor, was operationalized as time spent lying, sitting/standing, and walking. Barriers to physical activity included patients' pain levels, the use of urinary catheters, intravenous tubing, oxygen lines, drains, and level of dependence. Regression analysis explored the relationship between physical activity and barriers to physical activity.

RESULTS

Physical activity data were collected in 39 patients (aged 27-88, mean 54 years) during hospital stay. Patients were admitted for a median of 10 d (interquartile range [IQR]: 7-15 d). These patients were lying for a median of 12.1 h (7.6-17.7), sitting/standing 11.8 h (6.3-15.7), and walking 0.1 h (0-0.3) per day. Time lying during the day related to pain levels (β = 0.4 h per unit increase in pain, p < 0.01) and drain use (β = 3.1 h, p < 0.01).

CONCLUSIONS

Patients spent the most time during the hospital stay lying in bed. Improved pain management and decreased drain use may be worth exploring to increase inpatient physical activity.Implications for rehabilitationContinuous monitoring of physical activity in patients during hospital stay is an important tool for health care professionals to improve multidisciplinary care and rehabilitation.Health care professionals should be aware of the necessity of adequate pain management and critically review the use of drains in order to improve physical activity of patients during hospital stay.Patients need extra support of health care professionals to increase physical activity during consecutive days of their hospital stay.

Fully Disposable Wireless Patch Sensor for Continuous Remote Patient Monitoring

Continuous remote monitoring with convenient wireless sensors is attractive for early detection of patient deterioration, preventing adverse events and leading to better patient care. This article presents an innovative sensor design of VitalPatch, a fully disposable wireless biosensor, for remote continuous monitoring, and details the performance assessments from bench testing and laboratory validation in 57 subjects. The bench testing results reveal that VitalPatch's QRS detection had a positive predictive value of $> 99$% from testing with ECG databases. The accuracies of HR, BR and skin temp (in mean absolute error, MAE) from bench testing were $< 5$ bpm, $< 1$ brpm, $< 1 ^{ \circ}C$ respectively. The laboratory testing in 57 subjects revealed the accuracy of HR and BR to be $2.2 \pm 1.5$ bpm and $1.7 \pm 0.7$ brpm respectively for stationary periods. The absolute percent error in detecting steps was $4.7 \pm 4.6$%, and the accuracy in detecting posture was $96.4 \pm 3.1$%. Meanwhile, the specificity and sensitivity of fall detection $( \mathrm {n}=20)$ was found to be 100% and 93.8%, respectively. In conclusion, VitalPatch biosensor demonstrated clinically acceptable accuracies for its vital signs and actigraphy metrics applicable for continuous unobtrusive patient monitoring.

Validation of a wearable biosensor device for vital sign monitoring in septic emergency department patients in Rwanda

Objective

Critical care capabilities needed for the management of septic patients, such as continuous vital sign monitoring, are largely unavailable in most emergency departments (EDs) in low- and middle-income country (LMIC) settings. This study aimed to assess the feasibility and accuracy of using a wireless wearable biosensor device for continuous vital sign monitoring in ED patients with suspected sepsis in an LMIC setting.

Methods

This was a prospective observational study of pediatric (≥2 mon) and adult patients with suspected sepsis at the Kigali University Teaching Hospital ED. Heart rate, respiratory rate and temperature measurements were continuously recorded using a wearable biosensor device for the duration of the patients’ ED course and compared to intermittent manually collected vital signs.

Results

A total of 42 patients had sufficient data for analysis. Mean duration of monitoring was 32.8 h per patient. Biosensor measurements were strongly correlated with manual measurements for heart rate (r = 0.87, p < 0.001) and respiratory rate (r = 0.75, p < 0.001), although were less strong for temperature (r = 0.61, p < 0.001). Mean (SD) differences between biosensor and manual measurements were 1.2 (11.4) beats/min, 2.5 (5.5) breaths/min and 1.4 (1.0)°C. Technical or practical feasibility issues occurred in 12 patients (28.6%) although were minor and included biosensor detachment, connectivity problems, removal for a radiologic study or exam, and patient/parent desire to remove the device.

Conclusions

Wearable biosensor devices can be feasibly implemented and provide accurate continuous heart rate and respiratory rate monitoring in acutely ill pediatric and adult ED patients with sepsis in an LMIC setting.

Wearable sensors to improve detection of patient deterioration

INTRODUCTION

Monitoring a patient's vital signs forms a basic component of care, enabling the identification of deteriorating patients and increasing the likelihood of improving patient outcomes. Several paper-based track and trigger warning scores have been developed to allow clinical evaluation of a patient and guidance on escalation protocols and frequency of monitoring. However, evidence suggests that patient deterioration on hospital wards is still missed, and that patients are still falling through the safety net. Wearable sensor technology is currently undergoing huge growth, and the development of new light-weight wireless wearable sensors has enabled multiple vital signs monitoring of ward patients continuously and in real time.

AREAS COVERED

In this paper, we aim to closely examine the benefits of wearable monitoring applications that measure multiple vital signs; in the context of improving healthcare and delivery. A review of the literature was performed.

EXPERT COMMENTARY

Findings suggest that several sensor designs are available with the potential to improve patient safety for both hospital patients and those at home. Larger clinical trials are required to ensure both diagnostic accuracy and usability.

JA, Jortberg E, Bhatkar V, Ceruolo M, Ghaffari R, Clason DL, Maynard JP, Combs AH. A Pivotal Study to Validate the Performance of a Novel Wearable Sensor and System for Biometric Monitoring in Clinical and Remote Environments

BACKGROUND

Increasingly, drug and device clinical trials are tracking activity levels and other quality of life indices as endpoints for therapeutic efficacy. Trials have traditionally required intermittent subject visits to the clinic that are artificial, activity-intensive, and infrequent, making trend and event detection between visits difficult. Thus, there is an unmet need for wearable sensors that produce clinical quality and medical grade physiological data from subjects in the home. The current study was designed to validate the BioStamp nPoint® system (MC10 Inc., Lexington, MA, USA), a new technology designed to meet this need.

OBJECTIVE

To evaluate the accuracy, performance, and ease of use of an end-to-end system called the BioStamp nPoint. The system consists of an investigator portal for design of trials and data review, conformal, low-profile, wearable biosensors that adhere to the skin, a companion technology for wireless data transfer to a proprietary cloud, and algorithms for analyzing physiological, biometric, and contextual data for clinical research.

METHODS

A prospective, nonrandomized clinical trial was conducted on 30 healthy adult volunteers over the course of two continuous days and nights. Supervised and unsupervised study activities enabled performance validation in clinical and remote (simulated "at home") environments. System outputs for heart rate (HR), heart rate variability (HRV) (including root mean square of successive differences [RMSSD] and low frequency/high frequency ratio), activity classification during prescribed activities (lying, sitting, standing, walking, stationary biking, and sleep), step count during walking, posture characterization, and sleep metrics including onset/wake times, sleep duration, and respiration rate (RR) during sleep were evaluated. Outputs were compared to FDA-cleared comparator devices for HR, HRV, and RR and to ground truth investigator observations for activity and posture classifications, step count, and sleep events.

RESULTS

Thirty participants (77% male, 23% female; mean age 35.9 ± 10.1 years; mean BMI 28.1 ± 3.6) were enrolled in the study. The BioStamp nPoint system accurately measured HR and HRV (correlations: HR = 0.957, HRV RMSSD = 0.965, HRV ratio = 0.861) when compared to ActiheartTM. The system accurately monitored RR (mean absolute error [MAE] = 1.3 breaths/min) during sleep when compared to a Capnostream35TM end-tidal CO2 monitor. When compared with investigator observations, the system correctly classified activities and posture (agreement = 98.7 and 92.9%, respectively), step count (MAE = 14.7, < 3% of actual steps during a 6-min walk), and sleep events (MAE: sleep onset = 6.8 min, wake = 11.5 min, sleep duration = 13.7 min) with high accuracy. Participants indicated "good" to "excellent" usability (average System Usability Scale score of 81.3) and preferred the BioStamp nPoint system over both the Actiheart (86%) and Capnostream (97%) devices.

CONCLUSIONS

The present study validated the BioStamp nPoint system's performance and ease of use compared to FDA-cleared comparator devices in both the clinic and remote (home) environments.

Validation of a wireless patch sensor to monitor mobility tested in both an experimental and a hospital setup: A cross-sectional study

Purpose

To assess the concurrent validity of a wireless patch sensor to monitor time lying, sitting/standing, and walking in an experimental and a hospital setup.

Methods

Healthy adults participated in two testing sessions: an experimental and real-world hospital setup. Data on time lying, sitting/standing, and walking was collected with the HealthPatch and concurrent video recordings. Validity was assessed in three ways: 1. test for mean differences between HealthPatch data and reference values; 2. Intraclass Correlation Coefficient analysis (ICC 3.1 agreement); and 3. test for mean differences between posture detection accuracies.

Results

Thirty-one males were included. Significant mean differences were found between HealthPatch data and reference values for sitting/standing (mean 14.4 minutes, reference: 12.0 minutes, p<0.01) and walking (mean 6.4 minutes, reference: 9.0 minutes, p<0.01) in the experimental setup. Good correlations were found between the HealthPatch data and video data for lying (ICC: 0.824) and sitting/standing (ICC: 0.715) in the hospital setup. Posture detection accuracies of the HealthPatch were significantly higher for lying and sitting/standing in the experimental setup.

Conclusions

Overall, the results show a good validity of the HealthPatch to monitor lying and poor validity to monitor sitting/standing or walking. In addition, the validity outcomes were less favourable in the hospital setup.

Wearables and the medical revolution

Wearable sensors are already impacting healthcare and medicine by enabling health monitoring outside of the clinic and prediction of health events. This paper reviews current and prospective wearable technologies and their progress toward clinical application. We describe technologies underlying common, commercially available wearable sensors and early-stage devices and outline research, when available, to support the use of these devices in healthcare. We cover applications in the following health areas: metabolic, cardiovascular and gastrointestinal monitoring; sleep, neurology, movement disorders and mental health; maternal, pre- and neo-natal care; and pulmonary health and environmental exposures. Finally, we discuss challenges associated with the adoption of wearable sensors in the current healthcare ecosystem and discuss areas for future research and development.

Respiratory Waveform Estimation From Multiple Accelerometers: An Optimal Sensor Number and Placement Analysis

Respiratory patterns are commonly measured to monitor and diagnose cardiovascular, metabolic, and sleep disorders. Electronic devices such as masks used to record respiratory waveforms usually require medical staff support and obstruct the patients' breathing, causing discomfort. New techniques are being investigated to overcome such limitations. An emerging approach involves accelerometers to estimate the respiratory waveform based on chest motion. However, most of the existing techniques employ a single accelerometer placed on an arbitrary thorax position. The present work investigates the use and optimal placement of multiple accelerometers located on the thorax and the abdomen. The study population is composed of 30 healthy volunteers in three different postures. By means of a custom-made microcontrolled system, data are acquired from an array of ten accelerometers located on predefined positions and a pneumotachograph used as reference. The best sensor locations are identified by optimal linear reconstruction of the reference waveform from the accelerometer data in the minimum mean square error sense. The analysis shows that right-hand side locations contribute more often to optimal respiratory waveform estimates, a sound finding given that the right lung has a larger volume than the left lung. In addition, we show that the respiratory waveform can be blindly extracted from the recorded accelerometer data by means of independent component analysis. In conclusion, linear processing of multiple accelerometers in optimal positions can successfully recover respiratory information in clinical settings, where the use of masks may be contraindicated.

Estimating metabolic equivalents for activities in daily life using acceleration and heart rate in wearable devices

BACKGROUND

Herein, an algorithm that can be used in wearable health monitoring devices to estimate metabolic equivalents (METs) based on physical activity intensity data, particularly for certain activities in daily life that make MET estimation difficult.

RESULTS

Energy expenditure data were obtained from 42 volunteers using indirect calorimetry, triaxial accelerations and heart rates. The proposed algorithm used the percentage of heart rate reserve (%HRR) and the acceleration signal from the wearable device to divide the data into a middle-intensity group and a high-intensity group (HIG). The two groups were defined in terms of estimated METs. Evaluation results revealed that the classification accuracy for both groups was higher than 91%. To further facilitate MET estimation, five multiple-regression models using different features were evaluated via leave-one-out cross-validation. Using this approach, all models showed significant improvements in mean absolute percentage error (MAPE) of METs in the HIG, which included stair ascent, and the maximum reduction in MAPE for HIG was 24% compared to the previous model (HJA-750), which demonstrated a 70.7% improvement ratio. The most suitable model for our purpose that utilized heart rate and filtered synthetic acceleration was selected and its estimation error trend was confirmed.

CONCLUSION

For HIG, the MAPE recalculated by the most suitable model was 10.5%. The improvement ratio was 71.6% as compared to the previous model (HJA-750C). This result was almost identical to that obtained from leave-one-out cross-validation. This proposed algorithm revealed an improvement in estimation accuracy for activities in daily life; in particular, the results included estimated values associated with stair ascent, which has been a difficult activity to evaluate so far.

Reliability of wireless monitoring using a wearable patch sensor in high-risk surgical patients at a step-down unit in the Netherlands: a clinical validation study

BACKGROUND AND OBJECTIVES

Intermittent vital signs measurements are the current standard on hospital wards, typically recorded once every 8 hours. Early signs of deterioration may therefore be missed. Recent innovations have resulted in 'wearable' sensors, which may capture patient deterioration at an earlier stage. The objective of this study was to determine whether a wireless 'patch' sensor is able to reliably measure respiratory and heart rate continuously in high-risk surgical patients. The secondary objective was to explore the potential of the wireless sensor to serve as a safety monitor.

DESIGN

In an observational methods comparisons study, patients were measured with both the wireless sensor and bedside routine standard for at least 24 hours.

SETTING

University teaching hospital, single centre.

PARTICIPANTS

Twenty-five postoperative surgical patients admitted to a step-down unit.

OUTCOME MEASURES

Primary outcome measures were limits of agreement and bias of heart rate and respiratory rate. Secondary outcome measures were sensor reliability, defined as time until first occurrence of data loss.

RESULTS

1568 hours of vital signs data were analysed. Bias and 95% limits of agreement for heart rate were -1.1 (-8.8 to 6.5) beats per minute. For respiration rate, bias was -2.3 breaths per minute with wide limits of agreement (-15.8 to 11.2 breaths per minute). Median filtering over a 15 min period improved limits of agreement of both respiration and heart rate. 63% of the measurements were performed without data loss greater than 2 min. Overall data loss was limited (6% of time).

CONCLUSIONS

The wireless sensor is capable of accurately measuring heart rate, but accuracy for respiratory rate was outside acceptable limits. Remote monitoring has the potential to contribute to early recognition of physiological decline in high-risk patients. Future studies should focus on the ability to detect patient deterioration on low care environments and at home after discharge.

A Pilot Study Evaluating the Physiological Parameters of Performance-Induced Stress in Undergraduate Music Students

Music performance anxiety (MPA) is a specific condition for musicians. Although it can have a negative influence on their music careers, little attention is paid to this phenomenon both in the professional environment and in stress research. In the current pilot study, insight was gained into the physiology of the autonomic stress response related to anxiety in musicians when performing on stage by using a wearable biosensor patch for registration of a range of physiological parameters. Also, the validity of two different psychometric questionnaires in objectifying the stress response on stage to predict the individual stress response was explored. The autonomic physiological parameters (heart rate, respiratory rate, skin temperature) of 11 violists and violinists were collected while performing on stage and in resting state using the VitalConnect HealthPatch®. In addition, scores on validated questionnaires in research on MPA (State Anxiety Inventory, Kenny Music Performance Anxiety Inventory, Short Form Health Survey) were collected in order to try to objectify the magnitude of the subjective level of both MPA and experienced stress. The registration of the autonomic parameters showed a significant increase in heart rate, respiratory rate, and stress level from resting state measurements during stage performance. Analysis of heart rate variability showed a shift from indices of parasympathetic nervous system activity during baseline measurements towards indices of sympathetic nervous system activity during stress measurements. Surprisingly, none of the questionnaires was correlated to the physiological stress parameters on stage. In conclusion, the wearable biosensor patch proved to be an adequate tool to assess physiological stress parameters on stage. The different questionnaires did not contribute to the prediction of its occurrence in a group of musicians.

Performance Evaluation of Bluetooth Low Energy: A Systematic Review

Small, compact and embedded sensors are a pervasive technology in everyday life for a wide number of applications (e.g., wearable devices, domotics, e-health systems, etc.). In this context, wireless transmission plays a key role, and among available solutions, Bluetooth Low Energy (BLE) is gaining more and more popularity. BLE merges together good performance, low-energy consumption and widespread diffusion. The aim of this work is to review the main methodologies adopted to investigate BLE performance. The first part of this review is an in-depth description of the protocol, highlighting the main characteristics and implementation details. The second part reviews the state of the art on BLE characteristics and performance. In particular, we analyze throughput, maximum number of connectable sensors, power consumption, latency and maximum reachable range, with the aim to identify what are the current limits of BLE technology. The main results can be resumed as follows: throughput may theoretically reach the limit of ~230 kbps, but actual applications analyzed in this review show throughputs limited to ~100 kbps; the maximum reachable range is strictly dependent on the radio power, and it goes up to a few tens of meters; the maximum number of nodes in the network depends on connection parameters, on the network architecture and specific device characteristics, but it is usually lower than 10; power consumption and latency are largely modeled and analyzed and are strictly dependent on a huge number of parameters. Most of these characteristics are based on analytical models, but there is a need for rigorous experimental evaluations to understand the actual limits.

Smart monitoring of fluid intake and bladder voiding using pressure sensitive mats

Pressure sensitive mats have been used in noninvasive smart monitoring for a variety of problems including breathing rate monitoring, sleep monitoring, mobility, and weight. This paper describes a proof of concept application of pressure mats to monitor fluid intake/output (fluid cycle) events during the night. The ability to more accurately track such events has potential implications for monitoring those individuals who have nocturia, a condition where a person wakes at night to urinate. Data were collected from a healthy young female subject instructed to drink as much water as was comfortable (700mL) and lie in a supine position on a mattress located directly on three pressure mats. This was compared to an initial data set collected immediately after voiding but before drinking, 30 minutes after drinking, 60 minutes after drinking and a final data set after again voiding the bladder. The additional pressure from the 700mL of water was detectible and tracked over the course of the hour-long testing session under idealized conditions. This provides a proof-of-concept that nocturnal fluid intake and bladder voiding events can be tracked using non-invasive pressure-sensitive mats, however additional testing and development is required to achieve a deployable monitoring system.

Psychological acute stress measurement using a wireless adhesive biosensor

Stress management is essential in this modern civilization to maintain one's stress level low and reduce health risks, since stress is one of the primary causes leading to major chronic health disorders. The present study investigates the validity of stress index (SI) metric that objectively quantifies the psychological acute stress using a disposable adhesive biosensor worn on the chest called as HealthPatch(®). Eleven healthy volunteers (n=11) were attached with one HealthPatch sensor at left pectoralis major muscle along the cardiac axis to record modified Lead-II ECG. The subjects carried out a standard Trier Social Stress Test (TSST) protocol. During the study, the subjects filled out state anxiety form-Y1 of the State Anxiety Inventory questionnaire (sSTAI); salivary samples were obtained for salivary alpha-amylase (sAA) and salivary cortisol (sC) measurements; and the HealthPatch sensor data were wirelessly acquired. The data analyses revealed that sSTAI scores were significantly increased (P<0.001) due to TSST compared to the baseline. But, the changes in both sAA and sC measurements were not significant (P=0.281 and P=0.792, respectively). On the other hand, SI metric of HealthPatch showed significant (P<0.001) increase (~50%) during TSST, and shown to be sensitive to objectively track acute changes in psychological stress. Thus, HealthPatch biosensor can be valuable for continuous monitoring of psychological health and effective management of stress leading to healthy life.

Physical activity group classification algorithm using triaxial acceleration and heart rate

As described in this paper, a physical activity classification algorithm is proposed for energy expenditure estimation. The proposed algorithm can improve the classification accuracy using both the triaxial acceleration and heart rate. The optimal classification also contributes to improvement of the accuracy of the energy expenditures estimation. The proposed algorithm employs three indices: the heart rate reserve (%HRreserve), the filtered triaxial acceleration, and the ratio of filtered and unfiltered acceleration. The percentage HRreserve is calculated using the heart rate at rest condition and the maximum heart rate, which is calculated using Karvonen Formula. Using these three indices, a decision tree is constructed to classify physical activities into five classes: sedentary, household, moderate (excluding locomotive), locomotive, and vigorous. Evaluation results show that the average classification accuracy for 21 activities is 91%.

Ambulatory respiratory rate detection using ECG and a triaxial accelerometer

Continuous monitoring of respiratory rate in ambulatory conditions has widespread applications for screening of respiratory diseases and remote patient monitoring. Unfortunately, minimally obtrusive techniques often suffer from low accuracy. In this paper, we describe an algorithm with low computational complexity for combining multiple respiratory measurements to estimate breathing rate from an unobtrusive chest patch sensor. Respiratory rates derived from the respiratory sinus arrhythmia (RSA) and modulation of the QRS amplitude of electrocardiography (ECG) are combined with a respiratory rate derived from tri-axial accelerometer data. The three respiration rates are combined by a weighted average using weights based on quality metrics for each signal. The algorithm was evaluated on 15 elderly subjects who performed spontaneous and metronome breathing as well as a variety of activities of daily living (ADLs). When compared to a reference device, the mean absolute error was 1.02 breaths per minute (BrPM) during metronome breathing, 1.67 BrPM during spontaneous breathing, and 2.03 BrPM during ADLs.

Automated prediction of the apnea-hypopnea index using a wireless patch sensor

Polysomnography (PSG) is the gold standard that manually quantifies the apnea-hypopnea index (AHI) to assess the severity of sleep apnea syndrome (SAS). This study presents an algorithm that automatically estimates the AHI value using a disposable HealthPatch(TM) sensor. Volunteers (n=53, AHI: 0.1-85.8) participated in an overnight PSG study with patch sensors attached to their chest at three specified locations and data were wirelessly acquired. Features were computed for 150-second epochs of patch sensor data using analyses of heart rate variability, respiratory signals, posture and movements. Linear Support Vector Machine classifier was trained to detect the presence/absence of apnea/hypopnea events for each epoch. The number of epochs identified with events was subsequently mapped to AHI values using quadratic regression analysis. The classifier and regression models were optimized to minimize the mean-square error of AHI based on leave-one-out cross-validation. Comparison of predicted and reference AHI values resulted in linear correlation coefficients of 0.87, 0.88 and 0.92 for the three locations, respectively. The predicted AHI values were subsequently used to classify the control-to-mild apnea group (AHI<;15) and moderate-to-severe apnea (AHI≥15) with an accuracy (95% confidence intervals) of 89.4% (77.4-95.4%), 85.0% (70.9-92.9%), and 82.9% (67.3-91.9%) for the three locations, respectively. Overnight physiological monitoring using a wireless patch sensor provides an accurate estimate of AHI.