Printed and Flexible ECG Electrodes Attached to the Steering Wheel for Continuous Health Monitoring during Driving.

Continuous health monitoring in a vehicle enables the earlier detection of symptoms of cardiovascular diseases. In this work, we designed flexible and thin electrodes made of polyurethane for long-term electrocardiogram (ECG) monitoring while driving. We determined the time for reliable ECG recording to evaluate the effectiveness of the electrodes. We recorded data from 19 subjects under four scenarios: rest, city, highway, and rural. The recording time was five min for rest and 15 min for the other scenarios. The total recording (950 min) is publicly available under a CC BY-ND 4.0 license. We used the simultaneous truth and performance level estimation (STAPLE) algorithm to detect the position of R-waves. Then, we derived the RR intervals to compare the estimated heart rate with the ground truth, which we obtained from ECG electrodes on the chest. We calculated the signal-to-noise ratio (SNR) and averaged it for the different scenarios. Highway had the lowest SNR (-6.69 dB) and rural had the highest (-6.80 dB). The usable time of the steering wheel was 42.46% (city), 46.67% (highway), and 47.72% (rural). This indicates that steering-wheel-based ECG recording is feasible and delivers reliable recordings from about 45.62% of the driving time. In summary, the developed electrodes allow continuous in-vehicle heart rate monitoring, and our publicly available recordings provide the opportunity to apply more sophisticated data analytics.

Continuous Monitoring of Vital Signs Using Cameras: A Systematic Review.

In recent years, noncontact measurements of vital signs using cameras received a great amount of interest. However, some questions are unanswered: (i) Which vital sign is monitored using what type of camera? (ii) What is the performance and which factors affect it? (iii) Which health issues are addressed by camera-based techniques? Following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) statement, we conduct a systematic review of continuous camera-based vital sign monitoring using Scopus, PubMed, and the Association for Computing Machinery (ACM) databases. We consider articles that were published between January 2018 and April 2021 in the English language. We include five vital signs: heart rate (HR), respiratory rate (RR), blood pressure (BP), body skin temperature (BST), and oxygen saturation (SpO2). In total, we retrieve 905 articles and screened them regarding title, abstract, and full text. One hundred and four articles remained: 60, 20, 6, 2, and 1 of the articles focus on HR, RR, BP, BST, and SpO2, respectively, and 15 on multiple vital signs. HR and RR can be measured using red, green, and blue (RGB) and near-infrared (NIR) as well as far-infrared (FIR) cameras. So far, BP and SpO2 are monitored with RGB cameras only, whereas BST is derived from FIR cameras only. Under ideal conditions, the root mean squared error is around 2.60 bpm, 2.22 cpm, 6.91 mm Hg, 4.88 mm Hg, and 0.86 °C for HR, RR, systolic BP, diastolic BP, and BST, respectively. The estimated error for SpO2 is less than 1%, but it increases with movements of the subject and the camera-subject distance. Camera-based remote monitoring mainly explores intensive care, post-anaesthesia care, and sleep monitoring, but also explores special diseases such as heart failure. The monitored targets are newborn and pediatric patients, geriatric patients, athletes (e.g., exercising, cycling), and vehicle drivers. Camera-based techniques monitor HR, RR, and BST in static conditions within acceptable ranges for certain applications. The research gaps are large and heterogeneous populations, real-time scenarios, moving subjects, and accuracy of BP and SpO2 monitoring.

Unobtrusive Health Monitoring in Private Spaces: The Smart Home.

With the advances in sensor technology, big data, and artificial intelligence, unobtrusive in-home health monitoring has been a research focus for decades. Following up our research on smart vehicles, within the framework of unobtrusive health monitoring in private spaces, this work attempts to provide a guide to current sensor technology for unobtrusive in-home monitoring by a literature review of the state of the art and to answer, in particular, the questions: (1) What types of sensors can be used for unobtrusive in-home health data acquisition? (2) Where should the sensors be placed? (3) What data can be monitored in a smart home? (4) How can the obtained data support the monitoring functions? We conducted a retrospective literature review and summarized the state-of-the-art research on leveraging sensor technology for unobtrusive in-home health monitoring. For structured analysis, we developed a four-category terminology (location, unobtrusive sensor, data, and monitoring functions). We acquired 912 unique articles from four relevant databases (ACM Digital Lib, IEEE Xplore, PubMed, and Scopus) and screened them for relevance, resulting in n=55 papers analyzed in a structured manner using the terminology. The results delivered 25 types of sensors (motion sensor, contact sensor, pressure sensor, electrical current sensor, etc.) that can be deployed within rooms, static facilities, or electric appliances in an ambient way. While behavioral data (e.g., presence (n=38), time spent on activities (n=18)) can be acquired effortlessly, physiological parameters (e.g., heart rate, respiratory rate) are measurable on a limited scale (n=5). Behavioral data contribute to functional monitoring. Emergency monitoring can be built up on behavioral and environmental data. Acquired physiological parameters allow reasonable monitoring of physiological functions to a limited extent. Environmental data and behavioral data also detect safety and security abnormalities. Social interaction monitoring relies mainly on direct monitoring of tools of communication (smartphone; computer). In summary, convincing proof of a clear effect of these monitoring functions on clinical outcome with a large sample size and long-term monitoring is still lacking.

Unobtrusive Health Monitoring in Private Spaces: The Smart Vehicle.

Unobtrusive in-vehicle health monitoring has the potential to use the driving time to perform regular medical check-ups. This work intends to provide a guide to currently proposed sensor systems for in-vehicle monitoring and to answer, in particular, the questions: (1) Which sensors are suitable for in-vehicle data collection? (2) Where should the sensors be placed? (3) Which biosignals or vital signs can be monitored in the vehicle? (4) Which purposes can be supported with the health data? We reviewed retrospective literature systematically and summarized the up-to-date research on leveraging sensor technology for unobtrusive in-vehicle health monitoring. PubMed, IEEE Xplore, and Scopus delivered 959 articles. We firstly screened titles and abstracts for relevance. Thereafter, we assessed the entire articles. Finally, 46 papers were included and analyzed. A guide is provided to the currently proposed sensor systems. Through this guide, potential sensor information can be derived from the biomedical data needed for respective purposes. The suggested locations for the corresponding sensors are also linked. Fifteen types of sensors were found. Driver-centered locations, such as steering wheel, car seat, and windscreen, are frequently used for mounting unobtrusive sensors, through which some typical biosignals like heart rate and respiration rate are measured. To date, most research focuses on sensor technology development, and most application-driven research aims at driving safety. Health-oriented research on the medical use of sensor-derived physiological parameters is still of interest.

Quality of Electrocardiography Recorded with Consumer Smart Shirts.

Smart wearables support continuous monitoring of vital signs for early detection of deteriorating health. However, the devices and sensors require sufficient quality to produce meaningful signals, in particular, if data is acquired in motion. In this study, we equipped 48 subjects with smart shirts recording one-lead electrocardiography (ECG), thoracic and abdominal respiratory inductance plethysmography, and three-axis acceleration. For 10 min each, the subjects sit, stand, walk, and run, with a resting period of 5 min in between each activity. We preprocessed the electrocardiogram and applied a signal quality index. We analyzed the signal quality index grouped by the activity and participants. For sitting, standing, walking, and running, the ECG signals provide acceptable quality over 73.20 %, 91.85 %, 12.26 %, and 13.14 % of the recording time. In conclusion, smart wearables may be useful for continuous health monitoring of people with a sedentary lifestyle, but rather not for sportive activities.

Multimodal Signal Fusion for Heartbeat Monitoring on eScooters.

Integrating continuous monitoring into everyday objects enables the early detection of diseases. This paper presents a novel approach to heartbeat monitoring on eScooters using multi-modal signal fusion. We explore heartbeat monitoring using electrocardiography (ECG) and photoplethysmography (PPG) and evaluate four signal fusion approaches based on convolutional neural network (CNN) and long short-term memory (LSTM) architectures. We perform an evaluation study using skin-attached ECG electrodes for ground truth generation. The CNN+LSTM late fusion accurately measures the heartbeat for 76.17% of the driving time.

Robust In-Vehicle Signal Quality Assessment Using Multimodal Signal Fusion.

Continuous monitoring of physiological signals such as electrocardiogram (ECG) in driving environments has the potential to reduce the need for frequent health check-ups by providing real-time information on cardiovascular health. However, capturing ECG from sensors mounted on steering wheels creates difficulties due to motion artifacts, noise, and dropouts. To address this, we propose a novel method for reliable and accurate detection of heartbeats using sensor fusion with a bidirectional long short-term memory (BiLSTM) model. Our dataset contains reference ECG, steering wheel ECG, photoplethysmogram (PPG), and imaging PPG (iPPG) signals, which are more feasible to capture in driving scenarios. We combine these signals for R-wave detection. We conduct experiments with individual signals and signal fusion techniques to evaluate the performance of detected heartbeat positions. The BiLSTMs model achieves a performance of 62.69% in the driving scenario city. The model can be integrated into the system to detect heartbeat positions for further analysis.

Robust in-vehicle respiratory rate detection using multimodal signal fusion.

Continuous health monitoring in private spaces such as the car is not yet fully exploited to detect diseases in an early stage. Therefore, we develop a redundant health monitoring sensor system and signal fusion approaches to determine the respiratory rate during driving. To recognise the breathing movements, we use a piezoelectric sensor, two accelerometers attached to the seat and the seat belt, and a camera behind the windscreen. We record data from 15 subjects during three driving scenarios (15 min each) city, highway, and countryside. An additional chest belt provides the ground truth. We compare the four convolutional neural network (CNN)-based fusion approaches: early, sensor-based late, signal-based late, and hybrid fusion. We evaluate the performance of fusing for all four signals to determine the portion of driving time and the signal combination. The hybrid algorithm fusing all four signals is most effective in detecting respiratory rates in the city ([Formula: see text]), highway ([Formula: see text]), and countryside ([Formula: see text]). In summary, 60% of the total driving time can be used to measure the respiratory rate. The number of signals used in the multi-signal fusion improves reliability and enables continuous health monitoring in a driving vehicle.

Robust in-vehicle heartbeat detection using multimodal signal fusion.

A medical check-up during driving enables the early detection of diseases. Heartbeat irregularities indicate possible cardiovascular diseases, which can be determined with continuous health monitoring. Therefore, we develop a redundant sensor system based on electrocardiography (ECG) and photoplethysmography (PPG) sensors attached to the steering wheel, a red, green, and blue (RGB) camera behind the steering wheel. For the video, we integrate the face recognition engine SeetaFace to detect landmarks of face segments continuously. Based on the green channel, we derive colour changes and, subsequently, the heartbeat. We record the ECG, PPG, video, and reference ECG with body electrodes of 19 volunteers during different driving scenarios, each lasting 15 min: city, highway, and countryside. We combine early, signal-based late, and sensor-based late fusion with a hybrid convolutional neural network (CNN) and integrated majority voting to deliver the final heartbeats that we compare to the reference ECG. Based on the measured and the reference heartbeat positions, the usable time was 51.75%, 58.62%, and 55.96% for the driving scenarios city, highway, and countryside, respectively, with the hybrid algorithm and combination of ECG and PPG. In conclusion, the findings suggest that approximately half the driving time can be utilised for in-vehicle heartbeat monitoring.

The Podcast "Digitization of Medicine" as a Form of Science Communication.

The aim of the podcast Digitization of Medicine is to interest a broader audience and, in particular, young women, in research and work in the field of medical informatics. This article presents the usage figures and discusses their significance for further research on the success of science communication. By 24/02/2022, a total of 24,351 downloads had been made. There were slightly more female than male listeners, and they tended to be younger. Despite the importance podcast are gaining for science communication, little is known about the respective user group and further research is needed. In this context, this paper aims to help make the effectiveness of podcasts comparable.

Sensor Fusion for Robust Heartbeat Detection during Driving.

Private spaces like apartments and vehicles are not yet fully exploited for health monitoring, which includes continuous measurement of biosignals. This work proposes sensor fusion for robust heartbeat detection in the noisy and dynamic driving environment. We use four sensors: electrocardiography (ECG), ballistocardiography (BCG), photoplethysmography (PPG), and image-based PPG (iPPG). As ground truth, we record a 3-lead ECG with wet electrodes attached to the chest. Twelve healthy volunteers are monitored in rest and during driving, each for 11 min. We propose sensor fusion using convolutional neural networks to detect the sensor combination delivering the most accurate heart rate measurement. For rest, we achieve scores of 95.16% (BCG + iPPG), 96.08% (ECG + iPPG), 96.35% (ECG + BCG), 96.53% (ECG + PPG), 96.58% (PPG + iPPG), and 97.15% (BCG + PPG). In motion, the highest scores are 92.46% (BCG + iPPG, PPG + iPPG, ECG + iPPG), 92.83% (ECG + PPG), 93.03% (BCG + PPG), and 93.08% (ECG + BCG). Fusing all four signals with the best fusion approach results in scores of 97.24% (rest) and 94.38% (motion). We conclude that sensor fusion allows robust heartbeat measurement of car drivers to support continuous and unobtrusive health monitoring for early disease detection.

Registered report protocol: Developing an artifact index for capacitive electrocardiography signals acquired with an armchair.

Continuous monitoring of an electrocardiogram (ECG) in private diagnostic spaces such as vehicles or apartments allows early detection of cardiovascular diseases. We will use an armchair with integrated capacitive electrodes to record the capacitive electrocardiogram (cECG) during everyday activities. However, movements and other artifacts affect the signal quality. Therefore, an artifact index is needed to detect artifacts and classify the cECG. The unavailability of cECG data and reliable ground truth information requires new recordings to develop an artifact index. This study is designed to test the hypothesis: an artifact index can be devised, which intends to estimate the signal quality of segments and classify signals. In a single-arm study with 44 subjects, we will record two activities of 11-minute duration: reading and watching television. During recording, we will capture cECG, ECG, and oxygen saturation (SpO2) with time synchronization as well as keypoint-based movement indicators obtained from a video camera. SpO2 provides additional information on the subject's health status. The keypoint-based movements indicate artifacts in the cECG. We will combine all ground truth data to evaluate the index. In the future, we aim at using the artifact index to exclude cECG segments with artifacts from further analysis. This will improve cECG technology for the measurement of cardiovascular parameters.

Lessons Learned: Implementation of a Nationwide Innovative E-Learning Module for Health Enabling Technologies.

E-learning enables students to participate in online courses across universities. As a part of the HiGHmed joint teaching and training program, we developed an e-learning module entitled Health Enabling Technologies and Data based on the Gilly Salmon 5 stage model didactic concept. This course was implemented at a German Technical University in the winter semester 2019/20 and evaluated by the students after completion. Student evaluation indicates good teaching presence but improvable social presence. From the perspective of the lecturers, we have learned that interactivity should be enhanced to improve the students' engagement, and incentives shall be established to foster students' active participation. Therefore, we will revise the course for the next term by (i) web conferences, (ii) assessment of interactivity, and (iii) clear "take-home" messages.

In-Vehicle Respiratory Rate Estimation Using Accelerometers.

The monitoring of vital signs in a dynamic environment is challenging. This work demonstrates an approach to estimate the respiratory rate (RR) under real-driving conditions by using two accelerometers for signal recording and de-noising. One accelerometer was attached to the seatbelt for recording respiratory movements; another one was attached to the left side of the car seat for recording noise. The frequency components of the noise were used to suppress the noise hidden in the signal. The performance of the proposed approach is evaluated for three testers under three driving conditions, i.e., engine on, flat road and uneven road. The estimated RRs for three testers are 11.54 ± 2.28 breaths per minute (bpm), 15.57 ± 5.77 bpm, and 9.63 ± 4.58 bpm. The median estimated RR for three testers are 12.08 bpm, 18.26 bpm, and 7.76 bpm, where the manually counted reference RRs are 12 bpm, 18 bpm, and 7 bpm respectively. The average difference between estimated RRs and reference RRs is 0.71 bpm for the condition engine on, 3.36 bpm for flat road, and 4.58 bpm for uneven road. The results exhibit the ability of the proposed approach to estimate RR under real-driving conditions.

The Vehicle as a Diagnostic Space: Efficient Placement of Accelerometers for Respiration Monitoring During Driving.

In-vehicle monitoring of bio-signals in real time is still an unsolved problem. To support continuous respiration monitoring, this work intends to reveal where such sensors can be deployed and how their signals are affected by noise during autonomous driving. A Shimmer3 IMU module was attached to the passenger seatbelt of a test vehicle for respiration monitoring. Four positions at the seatbelt (Shoulder, Chest, Side-Waist and Waist) were tested under four conditions (Engine Off, Engine On, and Drive on Flat and Uneven Road). The data capture protocol ensures the same respiration rate in all conditions. Three testers were measured with two repetitions each yielding a total of 96 records of 60 s lengths. All signals were low-pass filtered. Then, the fast Fourier transform was applied. We evaluated the highest peak in the frequency domain. If the highest peak in the range of 0.1 - 0.4 Hz was identified at the same position, the condition is counted as true. Surprisingly, side-waist position yields 67% on the uneven road while chest and waist (both in the middle of the subject) are unsuitable. In conclusion, monitoring respiration on the seatbelt is possible with accelerometers while driving, if the right sensor position is chosen. In future, smart textiles will be used to integrate unobtrusive and inexpensive biomonitoring in the vehicle.

Noise Reduction for Efficient In-Vehicle Respiration Monitoring with Accelerometers.

Toward ubiquitous vital sign monitoring, de-noising is still challenging under real driving conditions. This work intends to investigate the performance of de-noising with an additional accelerometer in different positions for respiratory rate (RR) estimation. One accelerometer was attached to the seatbelt to record the signal of respiratory movements. For noise recording, two accelerometers were attached to the car seat in two ways, i.e., (1) on the front and back side (F-B) of the seat and (2) on the left and right side (L-R). The recorded noise information was used to suppress noise in the signal and in the frequency domain. The experiment was conducted under three driving conditions, i.e., engine on, flat road and uneven road. The median of the estimated RR is for F-B 2.15 breaths per minute (bpm), and for L-R 0.93 bpm. The medians for the three driving conditions are 0.81 bpm (engine on), 0.86 bpm (flat road), and 2.53 bpm (uneven road) respectively. In conclusion, suitable positions for the noise accelerometer are on the left and right side of the seat. Further approaches are in demand to achieve a more stable estimation for the most dynamic driving conditions such as uneven roads.