Motion Artifact Quantification and Sensor Fusion for Unobtrusive Health Monitoring

Clinical applications of smart wearable sensors

Smart wearable sensors are electronic devices worn on the body that collect, process, and transmit various physiological data. Compared to traditional devices, their advantages in terms of portability and comfort have made them increasingly important in the medical field. This review takes a unique clinical physician's standpoint, diverging from conventional sensor-type-based classifications, and provides a comprehensive overview of the diverse clinical applications of wearable sensors in recent years. In this review, we categorize these applications according to different diseases, encompassing skin diseases and injuries, cardiovascular diseases, abnormal human motion, as well as endocrine and metabolic disorders. Additionally, we discuss the challenges and perspectives hindering the development of sensors for clinical use, emphasizing the critical need for interdisciplinary collaboration between medical and engineering professionals. Overall, this review would serve as an important reference for the future direction of sensor devices in clinical use.

Automated Signal Quality Assessment of Single-Lead ECG Recordings for Early Detection of Silent Atrial Fibrillation

Atrial fibrillation (AF) is an arrhythmic cardiac disorder with a high and increasing prevalence in aging societies, which is associated with a risk for stroke and heart failure. However, early detection of onset AF can become cumbersome since it often manifests in an asymptomatic and paroxysmal nature, also known as silent AF. Large-scale screenings can help identifying silent AF and allow for early treatment to prevent more severe implications. In this work, we present a machine learning-based algorithm for assessing signal quality of hand-held diagnostic ECG devices to prevent misclassification due to insufficient signal quality. A large-scale community pharmacy-based screening study was conducted on 7295 older subjects to investigate the performance of a single-lead ECG device to detect silent AF. Classification (normal sinus rhythm or AF) of the ECG recordings was initially performed automatically by an internal on-chip algorithm. The signal quality of each recording was assessed by clinical experts and used as a reference for the training process. Signal processing stages were explicitly adapted to the individual electrode characteristics of the ECG device since its recordings differ from conventional ECG tracings. With respect to the clinical expert ratings, the artificial intelligence-based signal quality assessment (AISQA) index yielded strong correlation of 0.75 during validation and high correlation of 0.60 during testing. Our results suggest that large-scale screenings of older subjects would greatly benefit from an automated signal quality assessment to repeat measurements if applicable, suggest additional human overread and reduce automated misclassifications.

ECG performance in simultaneous recordings of five wearable devices using a new morphological noise-to-signal index and Smith-Waterman-based RR interval comparisons

Background

Numerous wearables are used in a research context to record cardiac activity although their validity and usability has not been fully investigated. The objectives of this study is the cross-model comparison of data quality at different realistic use cases (cognitive and physical tasks). The recording quality is expressed by the ability to accurately detect the QRS complex, the amount of noise in the data, and the quality of RR intervals.

Methods

Five ECG devices (eMotion Faros 360°, Hexoskin Hx1, NeXus-10 MKII, Polar RS800 Multi and SOMNOtouch NIBP) were attached and simultaneously tested in 13 participants. Used test conditions included: measurements during rest, treadmill walking/running, and a cognitive 2-back task. Signal quality was assessed by a new local morphological quality parameter morphSQ which is defined as a weighted peak noise-to-signal ratio on percentage scale. The QRS detection performance was evaluated with eplimited on synchronized data by comparison to ground truth annotations. A modification of the Smith-Waterman algorithm has been used to assess the RR interval quality and to classify incorrect beat annotations. Evaluation metrics includes the positive predictive value, false negative rates, and F1 scores for beat detection performance.

Results

All used devices achieved sufficient signal quality in non-movement conditions. Over all experimental phases, insufficient quality expressed by morphSQ values below 10% was only found in 1.22% of the recorded beats using eMotion Faros 360°whereas the rate was 8.67% with Hexoskin Hx1. Nevertheless, QRS detection performed well across all used devices with positive predictive values between 0.985 and 1.000. False negative rates are ranging between 0.003 and 0.017. eMotion Faros 360°achieved the most stable results among the tested devices with only 5 false positive and 19 misplaced beats across all recordings identified by the Smith-Waterman approach.

Conclusion

Data quality was assessed by two new approaches: analyzing the noise-to-signal ratio using morphSQ, and RR interval quality using Smith-Waterman. Both methods deliver comparable results. However the Smith-Waterman approach allows the direct comparison of RR intervals without the need for signal synchronization whereas morphSQ can be computed locally.

Signal Quality Index Based on Template Cross-Correlation in Multimodal Biosignal Chair for Smart Healthcare

We investigated the effects of a quality screening method on unconstrained measured signals, including electrocardiogram (ECG), photoplethysmogram (PPG), and ballistocardiogram (BCG) signals, in our collective chair system for smart healthcare. Such an investigation is necessary because unattached or unbound sensors have weaker connections to body parts than do conventional methods. Using the biosignal chair, the physiological signals collected during sessions included a virtual driving task, a physically powered wheelchair drive, and three types of body motions. The signal quality index was defined by the similarity between the observed signals and noise-free signals from the perspective of the cross-correlations of coefficients with appropriate individual templates. The goal of the index was to qualify signals without a reference signal to assess the practical use of the chair in daily life. As expected, motion artifacts have adverse effects on the stability of physiological signals. However, we were able to observe a supplementary relationship between sensors depending on each movement trait. Except for extreme movements, the signal quality and estimated heart rate (HR) remained within the range of criteria usable for status monitoring. By investigating the signal reliability, we were able to confirm the suitability of using the unconstrained biosignal chair to collect real-life measurements to improve safety and healthcare.

Dual-modality Volume Measurement integrated on a Ventricular Assist Device

OBJECTIVE

Ventricular assist devices (VADs) are implanted in patients suffering from end-stage heart failure to sustain the blood circulation. Real-time volume measurement could be a valuable tool to monitor patients and enable physiological control strategies to provide individualized therapy. However, volume measurement using one sensor modality requires re-calibration in the critical time post VAD implantation.

METHODS

To overcome this limitation, we have integrated ultrasound and impedance volume measurement techniques into a cannula of an apical VAD. We tested both modalities across a volume range from 140-420 mL using two differently sized and shaped biventricular silicon heart phantoms, which were subjected to physiological pressures in an in-vitro test bench. We compared results from standard calibrated measurements with calculations found by a quadratic optimization for the single modality and their combination (dual-modality) and validated the results using twofold cross-validation.

RESULTS

The dual-modality approach resulted in most favorable limits of agreement (LOA) of -0.83 ± 1.54% compared to -13.88 ± 5.90% for ultrasound and -43.45 ± 10.28% for electric impedance, separately.

CONCLUSION

The results of the dual-modality approach were as accurate as the standard calibrated measurement and valid over a large range of volumes (140-420 mL). In this in-vitro study, we show how a dual-modality ventricular volume measurement of ultrasound and electric impedance increases the robustness and renders calibration obsolete.

SIGNIFICANCE

Ventricular volumes could be measured accurately in the critical period post VAD implantation despite ventricular remodeling.

Vital Sign Monitoring in Car Seats Based on Electrocardiography, Ballistocardiography and Seismocardiography: A Review

This paper focuses on a thorough summary of vital function measuring methods in vehicles. The focus of this paper is to summarize and compare already existing methods integrated into car seats with the implementation of inter alia capacitive electrocardiogram (cECG), mechanical motion analysis Ballistocardiography (BCG) and Seismocardiography (SCG). In addition, a comprehensive overview of other methods of vital sign monitoring, such as camera-based systems or steering wheel sensors, is also presented in this article. Furthermore, this work contains a very thorough background study on advanced signal processing methods and their potential application for the purpose of vital sign monitoring in cars, which is prone to various disturbances and artifacts occurrence that have to be eliminated.

Practical and Durable Flexible Strain Sensors Based on Conductive Carbon Black and Silicone Blends for Large Scale Motion Monitoring Applications

Presented is a flexible capacitive strain sensor, based on the low cost materials silicone (PDMS) and carbon black (CB), that was fabricated by casting and curing of successive silicone layers—a central PDMS dielectric layer bounded by PDMS/CB blend electrodes and packaged by exterior PDMS films. It was effectively characterized for large flexion-angle motion wearable applications, with strain sensing properties assessed over large strains (50%) and variations in temperature and humidity. Additionally, suitability for monitoring large tissue deformation was established by integration with an in vitro digestive model. The capacitive gauge factor was approximately constant at 0.86 over these conditions for the linear strain range (3 to 47%). Durability was established from consistent relative capacitance changes over 10,000 strain cycles, with varying strain frequency and elongation up to 50%. Wearability and high flexion angle human motion detection were demonstrated by integration with an elbow band, with clear detection of motion ranges up 90°. The device’s simple structure and fabrication method, low-cost materials and robust performance, offer promise for expanding the availability of wearable sensor systems.

Accuracy and Usability of a Novel Algorithm for Detection of Irregular Pulse Using a Smartwatch Among Older Adults: Observational Study

BACKGROUND

Atrial fibrillation (AF) is often paroxysmal and minimally symptomatic, hindering its diagnosis. Smartwatches may enhance AF care by facilitating long-term, noninvasive monitoring.

OBJECTIVE

This study aimed to examine the accuracy and usability of arrhythmia discrimination using a smartwatch.

METHODS

A total of 40 adults presenting to a cardiology clinic wore a smartwatch and Holter monitor and performed scripted movements to simulate activities of daily living (ADLs). Participants' clinical and sociodemographic characteristics were abstracted from medical records. Participants completed a questionnaire assessing different domains of the device's usability. Pulse recordings were analyzed blindly using a real-time realizable algorithm and compared with gold-standard Holter monitoring.

RESULTS

The average age of participants was 71 (SD 8) years; most participants had AF risk factors and 23% (9/39) were in AF. About half of the participants owned smartphones, but none owned smartwatches. Participants wore the smartwatch for 42 (SD 14) min while generating motion noise to simulate ADLs. The algorithm determined 53 of the 314 30-second noise-free pulse segments as consistent with AF. Compared with the gold standard, the algorithm demonstrated excellent sensitivity (98.2%), specificity (98.1%), and accuracy (98.1%) for identifying irregular pulse. Two-thirds of participants considered the smartwatch highly usable. Younger age and prior cardioversion were associated with greater overall comfort and comfort with data privacy with using a smartwatch for rhythm monitoring, respectively.

CONCLUSIONS

A real-time realizable algorithm analyzing smartwatch pulse recordings demonstrated high accuracy for identifying pulse irregularities among older participants. Despite advanced age, lack of smartwatch familiarity, and high burden of comorbidities, participants found the smartwatch to be highly acceptable.

Modelling and Synthesizing Motion Artifacts in Unobtrusive Multimodal Sensing using Copulas

The use of non-contact sensing modalities to estimate apatient's vital signs is a promising approach to improve remote monitoring. One of the main challenges in non-contact sensing are motion artifacts, which can cause severe problems and must not be disregarded when designing non-contact systems. Combining multiple sensors and using intelligent sensor-fusion algorithms can reduce the influence of motion artifacts and improve the robustness of the vital sign estimation. Training and validating algorithms are important parts of the development process, but acquiring real data is usually a time-consuming task. Therefore a method to generate a large number of multi-sensor motion artifacts is needed. In this paper we investigate motion artifacts and their inter-dependence in a multi-sensor system. From these analyses, a multivariate mathematical artifact model is derived. Further-more, we propose a general synthesizing algorithm for artificial motion artifacts that allows creating an arbitrary number of multi-sensor motion artifacts. Finally, we compare the artificially created artifacts with real artifacts and evaluate our algorithm. Both qualitative indicators, e.g. signal morphology, and quantitative analyses, e.g. statistical distance measures, show a good accuracy of our model.

Unobtrusive Vital Sign Monitoring in Automotive Environments-A Review

This review provides an overview of unobtrusive monitoring techniques that could be used to monitor some of the human vital signs (i.e., heart activity, breathing activity, temperature and potentially oxygen saturation) in a car seat. It will be shown that many techniques actually measure mechanical displacement, either on the body surface and/or inside the body. However, there are also techniques like capacitive electrocardiogram or bioimpedance that reflect electrical activity or passive electrical properties or thermal properties (infrared thermography). In addition, photopleythysmographic methods depend on optical properties (like scattering and absorption) of biological tissues and-mainly-blood. As all unobtrusive sensing modalities are always fragile and at risk of being contaminated by disturbances (like motion, rapidly changing environmental conditions, triboelectricity), the scope of the paper includes a survey on redundant sensor arrangements. Finally, this review also provides an overview of automotive demonstrators for vital sign monitoring.

Evaluation of a Multichannel Non-Contact ECG System and Signal Quality Algorithms for Sleep Apnea Detection and Monitoring

Sleep-related conditions require high-cost and low-comfort diagnosis at the hospital during one night or longer. To overcome this situation, this work aims to evaluate an unobtrusive monitoring technique for sleep apnea. This paper presents, for the first time, the evaluation of contactless capacitively-coupled electrocardiography (ccECG) signals for the extraction of sleep apnea features, together with a comparison of different signal quality indicators. A multichannel ccECG system is used to collect signals from 15 subjects in a sleep environment from different positions. Reference quality labels were assigned for every 30-s segment. Quality indicators were calculated, and their signal classification performance was evaluated. Features for the detection of sleep apnea were extracted from capacitive and reference signals. Sleep apnea features related to heart rate and heart rate variability achieved high similarity to the reference values, with p-values of 0.94 and 0.98, which is in line with the more than 95% beat-matching obtained. Features related to signal morphology presented lower similarity with the reference, although signal similarity metrics of correlation and coherence were relatively high. Quality-based automatic classification of the signals had a maximum accuracy of 91%. Best-performing quality indicators were based on template correlation and beat-detection. Results suggest that using unobtrusive cardiac signals for the automatic detection of sleep apnea can achieve similar performance as contact signals, and indicates clinical value of ccECG. Moreover, signal segments can automatically be classified by the proposed quality metrics as a pre-processing step. Including contactless respiration signals is likely to improve the performance and provide a complete unobtrusive cardiorespiratory monitoring solution; this is a promising alternative that will allow the screening of more patients with higher comfort, for a longer time, and at a reduced cost.