Clinical applications of wearable technology

Clinical Evaluation of Respiratory Rate Measurements on COPD (Male) Patients Using Wearable Inkjet-Printed Sensor

Introduction: Chronic Obstructive Pulmonary Disease (COPD) is a progressive disease that causes long-term breathing problems. The reliable monitoring of respiratory rate (RR) is very important for the treatment and management of COPD. Based on inkjet printing technology, we have developed a stretchable and wearable sensor that can accurately measure RR on normal subjects. Currently, there is a lack of comprehensive evaluation of stretchable sensors in the monitoring of RR on COPD patients. We aimed to investigate the measurement accuracy of our sensor on COPD patients. Methodology: Thirty-five patients (Mean ± SD of age: 55.25 ± 13.76 years) in different stages of COPD were recruited. The measurement accuracy of our inkjet-printed (IJPT) sensor was evaluated at different body postures (i.e., standing, sitting at 90°, and lying at 45°) on COPD patients. The RR recorded by the IJPT sensor was compared with that recorded by the reference e-Health sensor using paired T-test and Wilcoxon signed-rank test. Analysis of variation (ANOVA) was performed to investigate if there was any significant effect of individual difference or posture on the measurement error. Statistical significance was defined as p-value less than 0.05. Results: There was no significant difference between the RR measurements collected by the IJPT sensor and the e-Health reference sensor overall and in three postures (p > 0.05 in paired T-tests and Wilcoxon signed-rank tests). The sitting posture had the least measurement error of −0.0542 ± 1.451 bpm. There was no significant effect of posture or individual difference on the measurement error or relative measurement error (p > 0.05 in ANOVA). Conclusion: The IJPT sensor can accurately measure the RR of COPD patients at different body postures, which provides the possibility for reliable monitoring of RR on COPD patients.

Accuracy of a Wrist-Worn Heart Rate Sensing Device during Elective Pediatric Surgical Procedures

The reliability of wearable photoplethysmography (PPG) sensors to measure heart rate (HR) in hospitalized patients has only been demonstrated in adults. We evaluated the accuracy of HR monitoring with a personal fitness tracker (PFT) in children undergoing surgery. HR monitoring was performed using a wrist-worn PFT (Fitbit Charge HR) in 30 children (8.21 ± 3.09 years) undergoing laparoscopy (n = 8) or open surgery (n = 22). HR values were analyzed preoperatively and during surgery. The accuracy of HR recordings was compared with measurements recorded during continuous electrocardiographic (cECG) monitoring; HRs derived from continuous monitoring with pulse oximetry (SpO2R) were used as a positive control. PFT-derived HR values were in agreement with those recorded during cECG (r = 0.99) and SpO2R (r = 0.99) monitoring. PFT performance remained high in children < 8 years (r = 0.99), with a weight < 30 kg (r = 0.99) and when the HR was < 70 beats per minute (bpm) (r = 0.91) or > 140 bpm (r = 0.99). PFT accuracy was similar during laparoscopy and open surgery, as well as preoperatively and during the intervention (r > 0.9). PFT–derived HR showed excellent accuracy compared with HRs measured by cECG and SpO2R during pediatric surgical procedures. Further clinical evaluation is needed to define whether PFTs can be used in different health care settings.

Accuracy of a Wrist-Worn Wearable Device for Monitoring Heart Rates in Hospital Inpatients: A Prospective Observational Study

Background

As the sensing capabilities of wearable devices improve, there is increasing interest in their application in medical settings. Capabilities such as heart rate monitoring may be useful in hospitalized patients as a means of enhancing routine monitoring or as part of an early warning system to detect clinical deterioration.

Objective

To evaluate the accuracy of heart rate monitoring by a personal fitness tracker (PFT) among hospital inpatients.

Methods

We conducted a prospective observational study of 50 stable patients in the intensive care unit who each completed 24 hours of heart rate monitoring using a wrist-worn PFT. Accuracy of heart rate recordings was compared with gold standard measurements derived from continuous electrocardiographic (cECG) monitoring. The accuracy of heart rates measured by pulse oximetry (Spo₂.R) was also measured as a positive control.

Results

On a per-patient basis, PFT-derived heart rate values were slightly lower than those derived from cECG monitoring (average bias of −1.14 beats per minute [bpm], with limits of agreement of 24 bpm). By comparison, Spo₂.R recordings produced more accurate values (average bias of +0.15 bpm, limits of agreement of 13 bpm, P<.001 as compared with PFT). Personal fitness tracker device performance was significantly better in patients in sinus rhythm than in those who were not (average bias −0.99 bpm vs −5.02 bpm, P=.02).

Conclusions

Personal fitness tracker–derived heart rates were slightly lower than those derived from cECG monitoring in real-world testing and not as accurate as Spo₂.R-derived heart rates. Performance was worse among patients who were not in sinus rhythm. Further clinical evaluation is indicated to see if PFTs can augment early warning systems in hospitals.

Trial Registration

ClinicalTrials.gov NCT02527408; https://clinicaltrials.gov/ct2/show/NCT02527408 (Archived by WebCite at  http://www.webcitation.org/6kOFez3on)

An Internet-based program for depression using activity and physiological sensors: efficacy, expectations, satisfaction, and ease of use

PURPOSE

Computerized cognitive behavioral therapy (CCBT) has been shown to be efficacious. Moreover, CCBT can be enhanced by using physiological and activity sensors, but there is no evidence about the acceptability of all these tools. The objective of this study is to examine the efficacy, expectations, satisfaction, and ease of use of an Internet-based CCBT program for preventing depression, with and without sensors (electroencephalography, electrocardiograhpy ECG, and actigraphy), in a high-risk population (unemployed men).

PATIENTS AND METHODS

Sixty participants at risk of depression (unemployed men) were randomly assigned to three experimental conditions: 1) intervention program (N=22), 2) intervention program plus sensors (N=19), and 3) control group (N=19). Participants completed depression, anxiety, positive and negative affect, and perceived stress measures. Furthermore, they also completed the measures for expectation, satisfaction, and the ease of use of the program.

RESULTS

Results showed that the two intervention groups improved significantly more than the control group on the clinical variables, and the improvements were greater in the group that used sensors than in the group that did not use them. Furthermore, participants in both intervention groups scored high on expectations and satisfaction with the CCBT program (with and without sensors). The mean score for usability was 88 out of 100 (standard deviation =12.32). No significant differences were found between groups on any of these variables.

CONCLUSION

This is the first study to analyze the efficacy, expectations, satisfaction, and ease of use of an Internet-based program using physiological and activity sensors. These results suggest that an Internet program for depression with or without physiological and activity sensors is effective, satisfactory, and easy to use.

A multi-channel opto-electronic sensor to accurately monitor heart rate against motion artefact during exercise

This study presents the use of a multi-channel opto-electronic sensor (OEPS) to effectively monitor critical physiological parameters whilst preventing motion artefact as increasingly demanded by personal healthcare. The aim of this work was to study how to capture the heart rate (HR) efficiently through a well-constructed OEPS and a 3-axis accelerometer with wireless communication. A protocol was designed to incorporate sitting, standing, walking, running and cycling. The datasets collected from these activities were processed to elaborate sport physiological effects. t-test, Bland-Altman Agreement (BAA), and correlation to evaluate the performance of the OEPS were used against Polar and Mio-Alpha HR monitors. No differences in the HR were found between OEPS, and either Polar or Mio-Alpha (both p > 0.05); a strong correlation was found between Polar and OEPS (r: 0.96, p < 0.001); the bias of BAA 0.85 bpm, the standard deviation (SD) 9.20 bpm, and the limits of agreement (LOA) from -17.18 bpm to +18.88 bpm. For the Mio-Alpha and OEPS, a strong correlation was found (r: 0.96, p < 0.001); the bias of BAA 1.63 bpm, SD 8.62 bpm, LOA from -15.27 bpm to +18.58 bpm. These results demonstrate the OEPS to be capable of carrying out real time and remote monitoring of heart rate.

Smartphone-based recognition of states and state changes in bipolar disorder patients

Today's health care is difficult to imagine without the possibility to objectively measure various physiological parameters related to patients' symptoms (from temperature through blood pressure to complex tomographic procedures). Psychiatric care remains a notable exception that heavily relies on patient interviews and self-assessment. This is due to the fact that mental illnesses manifest themselves mainly in the way patients behave throughout their daily life and, until recently there were no "behavior measurement devices." This is now changing with the progress in wearable activity recognition and sensor enabled smartphones. In this paper, we introduce a system, which, based on smartphone-sensing is able to recognize depressive and manic states and detect state changes of patients suffering from bipolar disorder. Drawing upon a real-life dataset of ten patients, recorded over a time period of 12 weeks (in total over 800 days of data tracing 17 state changes) by four different sensing modalities, we could extract features corresponding to all disease-relevant aspects in behavior. Using these features, we gain recognition accuracies of 76% by fusing all sensor modalities and state change detection precision and recall of over 97%. This paper furthermore outlines the applicability of this system in the physician-patient relations in order to facilitate the life and treatment of bipolar patients.

Estimation of human trunk movements by wearable strain sensors and improvement of sensor's placement on intelligent biomedical clothes

Background

The aim of this study was to evaluate the concept of a wearable device and, specifically: 1) to design and implement analysis procedures to extract clinically relevant information from data recorded using the wearable system; 2) to evaluate the design and placement of the strain sensors.

Methods

Different kinds of trunk movements performed by a healthy subject were acquired as a comprehensive data set of 639 multivariate time series and off-line analyzed. The space of multivariate signals recorded by the strain sensors was reduced by means of Principal Components Analysis, and compared with the univariate angles contemporaneously measured by an inertial sensor.

Results

Very high correlation between the two kinds of signals showed the usefulness of the garment for the quantification of the movements’ range of motion that caused at least one strain sensor to lengthen or shorten accordingly. The repeatability of signals was also studied. The layout of a next garment prototype was designed, with additional strain sensors placed across the front and hips, able to monitor a wider set of trunk motor tasks.

Conclusions

The proposed technologies and methods would offer a low-cost and unobtrusive approach to trunk motor rehabilitation.

Recommended number of strides for automatic assessment of gait symmetry and regularity in above-knee amputees by means of accelerometry and autocorrelation analysis

Background

Symmetry and regularity of gait are essential outcomes of gait retraining programs, especially in lower-limb amputees. This study aims presenting an algorithm to automatically compute symmetry and regularity indices, and assessing the minimum number of strides for appropriate evaluation of gait symmetry and regularity through autocorrelation of acceleration signals.

Methods

Ten transfemoral amputees (AMP) and ten control subjects (CTRL) were studied. Subjects wore an accelerometer and were asked to walk for 70 m at their natural speed (twice). Reference values of step and stride regularity indices (Ad1 and Ad2) were obtained by autocorrelation analysis of the vertical and antero-posterior acceleration signals, excluding initial and final strides. The Ad1 and Ad2 coefficients were then computed at different stages by analyzing increasing portions of the signals (considering both the signals cleaned by initial and final strides, and the whole signals). At each stage, the difference between Ad1 and Ad2 values and the corresponding reference values were compared with the minimum detectable difference, MDD, of the index. If that difference was less than MDD, it was assumed that the portion of signal used in the analysis was of sufficient length to allow reliable estimation of the autocorrelation coefficient.

Results

All Ad1 and Ad2 indices were lower in AMP than in CTRL (P < 0.0001). Excluding initial and final strides from the analysis, the minimum number of strides needed for reliable computation of step symmetry and stride regularity was about 2.2 and 3.5, respectively. Analyzing the whole signals, the minimum number of strides increased to about 15 and 20, respectively.

Conclusions

Without the need to identify and eliminate the phases of gait initiation and termination, twenty strides can provide a reasonable amount of information to reliably estimate gait regularity in transfemoral amputees.

Just enough measurement: a proposed paradigm for designing medical instrumentation

Our research group hypothesizes that one way to provide low-cost healthcare delivery efficiently is through the use of a large number of inexpensive sensors that can provide meaningful medical data. Typical development of medical instrumentation pursues increased resolution and higher accuracy - accompanied by a corresponding increase in cost; it is no secret that high costs impose a heavy burden on healthcare. We seek to invert the adage that quality is more important than quantity by extracting high quality biomedical information from a large quantity of low-cost measurements, and to demonstrate this using measurement instrumentation developed in our lab for extra-clinical assessment and rehabilitation tools. This will be discussed in terms of our initial experiments in evaluating balance and postural stability. This is an area of critical clinical importance: 2.6 million non-fatal fall injuries in persons over age 65 resulted in direct health care costs of $19 billion (in 2000) in the U.S., and the number of persons over age 65 in the U.S. is projected to more than double between 2000 and 2030.

Feature selection for accelerometer-based posture analysis in Parkinson's disease

Posture analysis in quiet standing is a key component of the clinical evaluation of Parkinson's disease (PD), postural instability being one of PD's major symptoms. The aim of this study was to assess the feasibility of using accelerometers to characterize the postural behavior of early mild PD subjects. Twenty PD and 20 control subjects, wearing an accelerometer on the lower back, were tested in five conditions characterized by sensory and attentional perturbation. A total of 175 measures were computed from the signals to quantify tremor, acceleration, and displacement of body sway. Feature selection was implemented to identify the subsets of measures that better characterize the distinctive behavior of PD and control subjects. It was based on different classifiers and on a nested cross validation, to maximize robustness of selection with respect to changes in the training set. Several subsets of three features achieved misclassification rates as low as 5%. Many of them included a tremor-related measure, a postural measure in the frequency domain, and a postural displacement measure. Results suggest that quantitative posture analysis using a single accelerometer and a simple test protocol may provide useful information to characterize early PD subjects. This protocol is potentially usable to monitor the disease's progression.