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Background Automatic measurement of respiratory rate in general hospital patients is difficult. Patient movement degrades the signal and variation of the breathing cycle means that accurate observation for ≥60 s is needed for adequate precision. Methods We studied acutely ill patients recently admitted to a teaching hospital. Breath duration was measured from a triaxial accelerometer attached to the chest wall and compared with a signal from a nasal cannula. We randomly divided the patient records into a training (n=54) and a test set (n=7). We used machine learning to train a neural network to select reliable signals, automatically identifying signal features associated with accurate measurement of respiratory rate. We used the test records to assess the accuracy of the device, indicated by the median absolute difference between respiratory rates, provided by the accelerometer and by the nasal cannula. Results In the test set of patients, machine classification of the respiratory signal reduced the median absolute difference (interquartile range) from 1.25 (0.56–2.18) to 0.48 (0.30–0.78) breaths per min. 50% of the recording periods were rejected as unreliable and in one patient, only 10% of the signal time was classified as reliable. However, even only 10% of observation time would allow accurate measurement for 6 min in an hour of recording, giving greater reliability than nurse charting, which is based on much less observation time. Conclusion Signals from a body-mounted accelerometer yield accurate measures of respiratory rate, which could improve automatic illness scoring in adult hospital patients., A machine learning method was developed to classify sections of breathing records from acutely ill patients wearing a small wireless motion sensor. This would allow accurate and automatic measurement, recording, and charting of respiratory rate. https://bit.ly/301P8XW

Background Respiratory rate is a basic clinical measurement used for illness assessment. Errors in measuring respiratory rate are attributed to observer and equipment problems. Previous studies commonly report rate differences ranging from 2 to 6 breaths·min−1 between observers. Methods To study why repeated observations should vary so much, we conducted a virtual experiment, using continuous recordings of breathing from acutely ill patients. These records allowed each breathing cycle to be precisely timed. We made repeated random measures of respiratory rate using different sample durations of 30, 60 and 120 s. We express the variation in these repeated rate measurements for the different sample durations as the interquartile range of the values obtained for each subject. We predicted what values would be found if a single measure, taken from any patient, were repeated and inspected boundary values of 12, 20 or 25 breaths·min−1, used by the UK National Early Warning Score, for possible mis-scoring. Results When the sample duration was nominally 30 s, the mean interquartile range of repeated estimates was 3.4 breaths·min−1. For the 60 s samples, the mean interquartile range was 3 breaths·min−1, and for the 120 s samples it was 2.5 breaths·min−1. Thus, repeat clinical counts of respiratory rate often differ by >3 breaths·min−1. For 30 s samples, up to 40% of National Early Warning Scores could be misclassified. Conclusions Early warning scores will be unreliable when short sample durations are used to measure respiratory rate. Precision improves with longer sample duration, but this may be impractical unless better measurement methods are used., In acutely ill patients, the usual 30 s to count breathing are insufficient to give a reliable measurement https://bit.ly/31DNhKU

Clinical trials employing manual processes for data collection and administering of questionnaires are time-consuming, expensive to run and result in noisy data. Wireless body-worn sensors coupled with mobile applications can be harnessed to automate the data collection process during clinical trials. This paper describes the use of the Respeck monitor, worn as a plaster on the chest, for characterising breathing and physical activity patterns in the general population during their normal everyday lives. Respeck data collected from 93 subjects for periods ranging between 24 to 72 h, amounting to a total of 106 days of continuous Respeck data. Analysis of the data revealed new insights, such as the respiratory rate levels dropped by 4.39 breaths per minute (BrPM) on average during sleeping periods, compared to the preceding day-time periods. This change is higher than typically reported levels when normally measured directly before the subjects fall asleep. Previous research in activity patterns in the general population were based on high-level activities logged using questionnaires. A method is presented for clustering simple, yet high-dimensional, activity patterns based on the Respeck data, by first extracting relevant features for each day. The results reveal four distinct groups in the cohort corresponding to different identifiable lifestyles: “Sedentary”, “Moderately active”, “Active walkers” and “Active movers”.

Background Respiratory rate is an important measurement in patient care, but accurate measurement is often difficult. We have developed a simple non-invasive device to measure respiratory movements in clinical circumstances, with minimal interference with the patient. We investigated respiratory patterns in patients receiving postoperative morphine analgesia to assess the capacity of the device to detect abnormalities. Methods We studied subjects during self-administered opioid analgesia after major gynaecological surgery, and related the derived signals with a signal from a nasal cannula. Respiratory movement signals were transmitted wirelessly to a recorder from two encapsulated tri-axial accelerometer (RESpeck) sensors. We analysed the signals using two different sensor placements, each for 30 min. The nasal cannula signal was used to classify breathing patterns as obstructive or non-obstructed. Results We studied 20 patients for a mean duration of 49 min each. Breathing patterns were very variable, between and within patients. The median breathing rates ranged from 6.4 to 19.5 bpm. Breathing was partly obstructed in 10 patients, and six patients had repeated cycles of obstruction and transient recovery. In these patients, we found a consistent and statistically significant pattern of changes in chest wall movement, with increased abdominal and decreased rib cage movement during obstruction. In patients with slow respiratory rates, breath-to-breath times were highly variable. Conclusions In undisturbed subjects receiving patient-controlled morphine analgesia after surgery, abnormal breathing patterns are extremely common. Cyclical airway obstruction is frequent and associated with a typical pattern of changes in chest wall movement.

The Automatic Urban and Rural Network (AURN) [1] is a set of high quality reference monitoring sites for recording air quality in the United Kingdom. They are costly to install and expensive to run, and are therefore limited in numbers. The data from these networks are used to inform regulatory compliance with the Ambient Air Quality Directives [2]. There is also a requirement to monitor air pollution at sufficiently high spatial and temporal resolutions around people to estimate personal exposure to particulates, and gases such as Nitrogen Dioxide and Ozone for better understanding their health impacts. Such high resolution measurements can also be used for validating the air quality models’ estimates of variability over space and time due to complex interactions. Networks of air-quality monitors using inexpensive sensors offer a cost effective alternative approach for recording trends in air quality at a higher spatial resolution, albeit not as accurately as the reference monitoring sites. This paper describes the design, implementation, and deployment of a family of air quality monitors: stationary (AirSpeck-S) monitors for measuring ambient air quality, and mobile wearable AirSpeck-P for monitoring personal exposure to air borne particulates (PM10, PM2.5 and PM1), and the gases - Nitrogen Dioxide and Ozone. Results are presented for characterising the ambient air quality in public spaces gathered from people wearing the AirSpeck-P monitors who are out and about in two cities as pedestrians (Edinburgh, Scotland) and as car passengers (Delhi, India). The paper demonstrates the viability of using inexpensive static and mobile AirSpeck monitors for mapping trends in particulate concentrations in urban spaces. Results are presented for comparisons of the mobile personal exposure data from pedestrians with static AirSpeck-S monitors along the same route, and the characterization of urban spaces based on levels of particulate concentration using the AirSpeck-P monitor.

The non-surgical management of patients with traumatic brain injury is the treatment and prevention of secondary insults, such as low cerebral perfusion pressure (CPP). Most clinical pressure monitoring systems measure pressure relative to atmospheric pressure. If a patient is managed with their head tilted up, relative to their arterial pressure transducer, then a hydrostatic pressure gradient (HPG) can act against arterial pressure and cause significant errors in calculated CPP.

Background Respiratory rate is an important measurement in patient care but frequently poorly assessed. We set out to develop a simple non-invasive device to reliably measure respiratory movements and estimate respiratory rate, in clinical circumstances. Methods Respiratory movement was detected with an encapsulated tri-axial accelerometer (Orient speck) and the data transmitted wirelessly to a computer for analysis. We studied subjects after gynaecological surgery who received opioid analgesia, and compared the derived signal with a signal from nasal cannula using directly matched breaths and within the same 5 min epoch. We analysed the signals for 5 min epochs over a 15 h recording period. Results For matched breath analysis, the instantaneous respiratory rates matched within 2 bpm on 86% of occasions. A similar match was found between epoch averages of the respiratory rate. The mean absolute difference between the respiratory rate measured by nasal cannula and Orient speck was 0.6 bpm. The Orient speck generated reliable measures of respiratory rate every 5 min in 95.4% of epochs. Conclusions The Orient speck provides a reliable measure of respiratory rate at frequent intervals in subjects receiving patient-controlled morphine analgesia after surgery.

Manual muscle testing and its variants have a long history of use for classifying muscle strengths. For the first time, inexpensive wearable wireless sensors combined with machine learning techniques are used to classify different levels of muscle strength, which addresses some limitations of the manual method. A mean accuracy of 93% was obtained across ten subjects using gyroscope and accelerometer data in classifying four distinct levels of strengths of the biceps brachii muscle when performing muscle contraction under appropriate load. This was reduced by 2% for accelerometer-only data, thus offering a potentially inexpensive and viable solution for testing muscle strength.

A novel approach, using wireless inertial sensors (the Orient specks), is presented for the automated assessment of surgical skills in a physical laparoscopic ('keyhole') surgery simulator. Performance metrics are applied to inertial data from subjects categorized by previous laparoscopic experience: novices (less than 2 months); intermediates (less than 2 years) and experts (6 to 10 years of active laparoscopic operating experience). These metrics are shown to distinguish between groups, demonstrating 'construct validity' of the system. The method is evaluated against an optical instrument tracking system and compares favourably, thus demonstrating 'concurrent validity' with an existing method. Our findings suggest that a combination of the proposed metrics can be used to generate a 'technical performance score' for a given laparoscopic simulated task. This has the potential to help guide surgical training and monitoring of surgical performance.

This paper explores the use of Orient specks as on-body network of wireless inertial-magnetic sensors to capture the parameters of the human gait for mobile clinical gait analysis. A range of kinematic and temporal parameters were measured for normal humans using Orient specks and compared to values obtained from a commercial Vicon optical motion capture system. There was a good correlation between the joint angle data from the two systems, most notably in the sagittal plane. Hip flexion graphs showed the highest correlation value of 0.973, with knee flexion at 0.855 and pelvic rotation at 0.943, followed by pelvic obliquity at 0.689 and ankle flexion at 0.626. We conclude that the Orient specks have the potential for obtaining gait parameters outside the laboratory environment by measuring temporal parameters, and detecting the shape and trend of kinematic parameters of the patients when they are out and about during their everyday lives.

Respiratory complications may occur in post-operative patients but are often difficult to detect due to the lack of a well-tolerated and reliable monitoring method. We have developed a wireless accelerometer-based device that measures chest wall rotations due to breathing and has been shown to provide an accurate measure of respiratory rate in a clinical setting, when validated against rates obtained from nasal pressure. This paper describes a method for detecting instances of respiratory problems due to physical airway obstruction and central nervous system (CNS) respiratory depression using a combination of nasal pressure and chest wall rotation signals in gynaecological patients during the first night after surgery. Waxing and waning of breath amplitude and irregularity of breath length are found to be useful indicators. A clinical study involving 19 post-operative patients shows that the proposed method is able to detect and distinguish between both forms of respiratory complications.

There is a strong medical need for continuous, unobstrusive respiratory monitoring, and many shortcomings to existing methods. Previous work shows that MEMS accelerometers worn on the torso can measure inclination changes due to breathing, from which a respiratory rate can be obtained. There has been limited validation of these methods. The problem of practical continuous monitoring, in which patient movement disrupts the measurements and the axis of interest changes, has also not been addressed. We demonstrate a method based on tri-axial accelerometer data from a wireless sensor device, which tracks the axis of rotation and obtains angular rates of breathing motion. The resulting rates are validated against gyroscope measurements and show high correlation to flow rate measurements using a nasal cannula. We use a movement detection method to classify periods in which the patient is static and breathing signals can be observed accurately. Within these periods we obtain a close match to cannula measurements, for both the flow rate waveform and derived respiratory rates, over multi-hour datasets obtained from wireless sensor devices on hospital patients. We discuss future directions for improvement and potential methods for estimating absolute airflow rate and tidal volume.