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

Hardware Prototype for Wrist-Worn Simultaneous Monitoring of Environmental, Behavioral, and Physiological Parameters

We designed a low-cost wrist-worn prototype for simultaneously measuring environmental, behavioral, and physiological domains of influencing factors in healthcare. Our prototype continuously monitors ambient elements (sound level, toxic gases, ultraviolet radiation, air pressure, temperature, and humidity), personal activity (motion tracking and body positioning using gyroscope, magnetometer, and accelerometer), and vital signs (skin temperature and heart rate). An innovative three-dimensional hardware, based on the multi-physical-layer approach is introduced. Using board-to-board connectors, several physical hardware layers are stacked on top of each other. All of these layers consist of integrated and/or add-on sensors to measure certain domain (environmental, behavioral, or physiological). The prototype includes centralized data processing, transmission, and visualization. Bi-directional communication is based on Bluetooth Low Energy (BLE) and can connect to smartphones as well as smart cars and smart homes for data analytic and adverse-event alerts. This study aims to develop a prototype for simultaneous monitoring of the all three areas for monitoring of workplaces and chronic obstructive pulmonary disease (COPD) patients with a concentration on technical development and validation rather than clinical investigation. We have implemented 6 prototypes which have been tested by 5 volunteers. We have asked the subjects to test the prototype in a daily routine in both indoor (workplaces and laboratories) and outdoor. We have not imposed any specific conditions for the tests. All presented data in this work are from the same prototype. Eleven sensors measure fifteen parameters from three domains. The prototype delivers the resolutions of 0.1 part per million (PPM) for air quality parameters, 1 dB, 1 index, and 1 °C for sound pressure level, UV, and skin temperature, respectively. The battery operates for 12.5 h under the maximum sampling rates of sensors without recharging. The final expense does not exceed 133€. We validated all layers and tested the entire device with a 75 min recording. The results show the appropriate functionalities of the prototype for further development and investigations

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

A thermostable salmonella phage endolysin, Lys68, with broad bactericidal properties against gram-negative pathogens in presence of weak acids

Resistance rates are increasing among several problematic Gram-negative pathogens, a fact that has encouraged the development of new antimicrobial agents. This paper characterizes a Salmonella phage endolysin (Lys68) and demonstrates its potential antimicrobial effectiveness when combined with organic acids towards Gram-negative pathogens. Biochemical characterization reveals that Lys68 is more active at pH 7.0, maintaining 76.7% of its activity when stored at 4°C for two months. Thermostability tests showed that Lys68 is only completely inactivated upon exposure to 100°C for 30 min, and circular dichroism analysis demonstrated the ability to refold into its original conformation upon thermal denaturation. It was shown that Lys68 is able to lyse a wide panel of Gram-negative bacteria (13 different species) in combination with the outer membrane permeabilizers EDTA, citric and malic acid. While the EDTA/Lys68 combination only inactivated Pseudomonas strains, the use of citric or malic acid broadened Lys68 antibacterial effect to other Gram-negative pathogens (lytic activity against 9 and 11 species, respectively). Particularly against Salmonella Typhimurium LT2, the combinatory effect of malic or citric acid with Lys68 led to approximately 3 to 5 log reductions in bacterial load/CFUs after 2 hours, respectively, and was also able to reduce stationary-phase cells and bacterial biofilms by approximately 1 log. The broad killing capacity of malic/citric acid-Lys68 is explained by the destabilization and major disruptions of the cell outer membrane integrity due to the acidity caused by the organic acids and a relatively high muralytic activity of Lys68 at low pH. Lys68 demonstrates good (thermo)stability properties that combined with different outer membrane permeabilizers, could become useful to combat Gram-negative pathogens in agricultural, food and medical industry.This work was supported by the projects FCOMP-01-0124-FEDER-019446, FCOMP-01-0124-FEDER-027462 and PEst-OE/EQB/LA0023/2013 from "Fundacao para a Ciencia e Tecnologia" (FCT), Portugal. The authors thank the Project "BioHealth - Biotechnology and Bioengineering approaches to improve health quality", Ref. NORTE-07-0124-FEDER-000027, co-funded by the Programa Operacional Regional do Norte (ON. 2 - O Novo Norte), QREN, FEDER. Hugo Oliveira acknowledges the FCT grant SFRH/BD/63734/2009. Maarten Walmagh held a PhD scholarship of the IWT Vlaanderen. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

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

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

Acknowledgements: We would like to thank all the volunteers for their time and effort who supported the data acquisition and recording process.Funder: Technische Universität Braunschweig (1042)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.

Acknowledgements: Our sincere thanks go out to all the volunteers who contributed to the data acquisition process.Funder: Technische Universität Braunschweig (1042)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

Robust in-vehicle heartbeat detection using multimodal signal fusion

Acknowledgements: Our sincere thanks go out to all the volunteers who contributed to the data acquisition process.Funder: Technische Universität Braunschweig (1042)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

Robust In-Vehicle Respiratory Rate Detection Using Multimodal Signal Fusion

Early detection of health issues is critical to ensuring timely and effective medical care. Our study aimed to improve the assessment of a patient's health by utilizing an in-vehicle health monitoring system that incorporates primary vital signs such as respiratory rate. To achieve this, we designed a redundant sensor system composed of an accelerometer, a piezoelectric sensor, and a camera for image photoplethysmogram (iPPG). The sensor system was tested on 15 subjects under four different conditions: rest, city driving, highway driving, and rural driving, with a total recording time of 5 minutes for rest and 15 minutes for each of the driving conditions. The recorded signals and ground truth data were analyzed and are now publicly available, offering a valuable resource for the reproduction of our results and the improvement of existing algorithms for health assessments