This paper explores using RSS measurements on many links in a wireless network to estimate the breathing rate of a person, and the location where the breathing is occurring, in a home, while the person is sitting, laying down, standing, or sleeping. The main challenge in breathing rate estimation is that motion interference, i.e., movements other than a person's breathing, generally cause larger changes in RSS than inhalation and exhalation. We develop a method to estimate breathing rate despite motion interference, and demonstrate its performance during multiple short (3-7 minute) tests and during a longer 66 minute test. Further, for the same experiments, we show the location of the breathing person can be estimated, to within about 2 m average error in a 56 m 2 apartment. Being able to locate a breathing person who is not otherwise moving, without calibration, is important for applications in search and rescue, health care, and security.

ObjectiveThis study evaluates the potential for improving patient safety by introducing a metacognitive attention aid that enables clinicians to more easily access and use existing alarm/alert information. It is hypothesized that this introduction will enable clinicians to easily triage alarm/alert events and quickly recognize emergent opportunities to adapt care delivery. The resulting faster response to clinically important alarms/alerts has the potential to prevent adverse events and reduce healthcare costs.Materials and methodsA randomized within-subjects single-factor clinical experiment was conducted in a high-fidelity 20-bed simulated acute care hospital unit. Sixteen registered nurses, four at a time, cared for five simulated patients each. A two-part highly realistic clinical scenario was used that included representative: tasking; information; and alarms/alerts. The treatment condition introduced an integrated wearable attention aid that leveraged metacognition methods from proven military systems. The primary metric was time for nurses to respond to important alarms/alerts.ResultsUse of the wearable attention aid resulted in a median relative within-subject improvement for individual nurses of 118% (W = 183, p = 0.006). The top quarter of relative improvement was 3,303% faster (mean; 17.76 minutes reduced to 1.33). For all unit sessions, there was an overall 148% median faster response time to important alarms (8.12 minutes reduced to 3.27; U = 2.401, p = 0.016), with 153% median improvement in consistency across nurses (F = 11.670, p = 0.001).Discussion and conclusionExisting device-centric alarm/alert notification solutions can require too much time and effort for nurses to access and understand. As a result, nurses may ignore alarms/alerts as they focus on other important work. There has been extensive research on reducing alarm frequency in healthcare. However, alarm safety remains a top problem. Empirical observations reported here highlight the potential of improving patient safety by supporting the meta-work of checking alarms.

Modern sensors for health surveillance generate high volumes and rates of data that currently overwhelm operational decision-makers. These data are collected with the intention of enabling front-line clinicians to make effective clinical judgments. Ironically, prior human–systems integration (HSI) studies show that the flood of data degrades rather than aids decision-making performance. Health surveillance operations can focus on aggregate changes to population health or on the status of individual people. In the case of clinical monitoring, medical device alarms currently create an information overload situation for front-line clinical workers, such as hospital nurses. Consequently, alarms are often missed or ignored, and an impending patient adverse event may not be recognized in time to prevent crisis. One innovation used to improve decision making in areas of data-rich environments is the Human Alerting and Interruption Logistics (HAIL) technology, which was originally sponsored by the US Office of Naval Research. HAIL delivers metacognitive HSI services that empower end-users to quickly triage interruptions and dynamically manage their multitasking. HAIL informed our development of an experimental prototype that provides a set of context-enabled alarm notification services (without automated alarm filtering) to support users’ metacognition for information triage. This application is called HAIL Clinical Alarm Triage (HAIL-CAT) and was designed and implemented on a smartwatch to support the mobile multitasking of hospital nurses. An empirical study was conducted in a 20-bed virtual hospital with high-fidelity patient simulators. Four teams of four registered nurses (16 in total) participated in a 180-minute simulated patient care scenario. Each nurse was assigned responsibility to care for five simulated patients and high rates of simulated health surveillance data were available from patient monitors, infusion pumps, and a call light system. Thirty alarms per nurse were generated in each 90-minute segment of the data collection sessions, only three of which were clinically important alarms. The within-subjects experimental design included a treatment condition where the nurses used HAIL-CAT on a smartwatch to triage and manage alarms and a control condition without the smartwatch. The results show that, when using the smartwatch, nurses responded three times faster to clinically important and actionable alarms. An analysis of nurse performance also shows no negative effects on their other duties. Subjective results show favorable opinions about utility, usability, training requirement, and adoptability. These positive findings suggest the potential for the HAIL HSI system to be transferrable to the domain of health surveillance to achieve the currently unrealized potential utility of high-volume data.

BACKGROUND: Numerous technologies are used to monitor respiratory rates in nonintubated patients. No technology has emerged as the standard. The primary aim of this study was to assess the limits of agreement between a reference sensor signal (respiratory inductance plethysmography bands) and 7 alternative sensor signals (nasal capnometer, nasal pressure transducer, oronasal thermistor, abdominal accelerometer, transpulmonary electrical impedance, peritracheal microphone, and photoplethysmography) for measuring low respiratory rates in sedated, nonintubated, supine volunteers. A unified approach based on a single breath detection algorithm was applied to each sensor to facilitate comparison. We hypothesized that all of the sensor signals would allow detection of low (<10 breaths per minute) respiratory rates to within ±2 breaths per minute of the reference sensor signal. METHODS: Volunteers received remifentanil and propofol infusions at selected target concentration pairs to induce depression of ventilation. Signals from each sensor were analyzed by an identical threshold-based detection algorithm to compute the breathing rate. Bland-Altman limits of agreement and error rate analyses were used to characterize the performance of each sensor compared to the reference sensor. RESULTS: The analysis of the accelerometer and capnometer signals, using Bland-Altman and error rate analyses, showed the highest breath rate agreement (1.96 × standard deviation) of the 7 sensors with −2.1 to 2.2 and −2.5 to 2.7 breaths per minute, respectively. All other signals exhibited wider limits of agreement, with impedance being the widest at −7.8 to 7.4 breaths per minute. For the abdomen accelerometer, 95% of Bland-Altman data points were within ±2 breaths per minute. For the capnometer, 96% of data points were within ±2 breaths per minute. Nasal pressure, thermistor, and microphone all had >80% of data points within ±2 breaths per minute. Impedance and photoplethysmograph signals had 58% and 64%, respectively. CONCLUSIONS: A unified approach can be applied to a variety of sensor signals to estimate respiratory rates in spontaneously breathing, nonintubated, sedated volunteers. However, detecting clinically relevant low respiratory rates (<6 breaths per minute) is a technical challenge. By our analysis, no single sensor was able to detect slow respiratory rates with adequate precision (<±2 breaths per minute of the reference signal). Of the sensors evaluated, capnometers and abdominal accelerometers may be the most reliable sensors for identifying hypopnea and central apnea.