Multi-sensor Device Research Articles

Multi-Sensor Platform in Precision Livestock Farming for Air Quality Measurement Based on Open-Source Tools

Monitoring air quality in livestock farming facilities is crucial for ensuring the health and well-being of both animals and workers. As livestock farming can contribute to the emission of various gaseous and particulate pollutants, there is a pressing need for advanced air quality monitoring systems to manage and mitigate these emissions effectively. This study introduces a multi-sensor air quality monitoring system designed specifically for livestock farming environments. Utilizing open-source tools and low-cost sensors, the system can measure multiple air quality parameters simultaneously. The system architecture is based on SOLID principles to ensure robustness, scalability, and ease of maintenance. Understanding a trend of evolution of air quality monitoring from single-parameter measurements to a more holistic approach through the integration of multiple sensors, a multi-sensor platform is proposed in this work. This shift towards multi-sensor systems is driven by the recognition that a comprehensive understanding of air quality requires consideration of diverse pollutants and environmental factors. The aim of this study is to construct a multi-sensor air quality monitoring system with the use of open-source tools and low-cost sensors as a tool for Precision Livestock Farming (PLF). Analysis of the data collected by the multi-sensor device reveals some insights into the environmental conditions in the monitored barn. Time-series and correlation analyses revealed significant interactions between key environmental parameters, such as strong positive correlations between ammonia and hydrogen sulfide, and between total volatile organic compounds and carbon dioxide. These relationships highlight the critical impact of these odorants on air quality, emphasizing the need for effective barn environmental controls to manage these factors.

Improving the short-term prediction of dissolved carbon monoxide using a combination of Light GBM and meta-heuristic algorithms

In this study, the prediction of carbon monoxide pollutants on a short-term scale has been investigated according to some input data sources, comprising gas concentrations related to air quality and weather features. Utilizing a hybrid modeling approach that integrates the Light Gradient Boosting Machine with several meta-heuristic optimization algorithms such as Chaos Game Optimization, Aquila Optimizer, and others, we aimed to optimize the hyperparameters of the Light GBM to enhance predictive accuracy. The application of a K-fold cross-validation technique with K=5 helped in preventing overfitting. By conducting a case study on a real dataset collected from a gas multi-sensor device, it was found that the hybrid model combining the Light Gradient Boosting Machine with Chaos Game Optimization demonstrated superior performance compared to other models. The values of the coefficient of determination, Root Mean Square Error, Mean Absolute Error, and Mean Absolute Percentage Error for this model based on test data are 0.99, 0.0393, 0.0301, and 0.0052, respectively. These results underscore the effectiveness of the hybridization approach in providing highly accurate predictions for short-term carbon monoxide concentrations, offering a valuable tool for environmental monitoring and enhancing public health safeguards.

Daytime actisensitivity predicts nocturnal dipping patterns and morning surge in blood pressure: the Home-Activity ICT-based Japan Ambulatory Blood Pressure Monitoring Prospective study.

Ambulatory blood pressure (BP) is influenced by physical activity and the BP response to physical activity (actisensitivity) differs between individuals. This study investigated associations between daytime actisensitivity and nighttime BP dipping status and morning BP surge. Twenty-four-hour ambulatory BP monitoring (ABPM) with simultaneously monitored physical activity using a multisensor all-in-one device (TM-2441; A&D Company) was performed at baseline in HI-JAMP study participants. Those with complete BP measurements and complete physical activity monitoring data were included in this analysis. Actisensitivity was calculated as the slope of the regression line between daytime SBP and log-transformed physical activity over a 5 min period before each BP reading. Hyper and negative reactivity were defined as actisensitivity greater than 90th and less than 10th percentile, respectively. Data from 2692 individuals (mean age 69.9 ± 11.9 years; mean BMI 24.8 ± 4.1 kg/m 2 , 53.6% men) were analyzed. Those with hyper reactivity had a high prevalence of the extreme dipper pattern of nighttime BP and exaggerated morning BP surge; those with negative reactivity had higher nighttime BP and a riser pattern of nighttime BP. Results remained significant after adjusting for 24-h physical activity. Differences in diurnal BP variability based on actisensitivity were augmented in individuals aged at least 75 years. This study is the first to investigate associations between actisensitivity and 24-h ambulatory BP profiles using an all-in-one multisensor device in a large real-world population. The associations seen between either hyper or negative actisensitivity and abnormal diurnal BP variability, especially in the elderly, could contribute to increased cardiovascular event risk. University Hospital Medical Information Network Clinical Trials Registry, UMIN000029151 (HI-JAMP study).

Vital parameter monitoring in harsh environment by the MedSENS in-ear multisensor device

Accurate assessment of vital parameters is essential for diagnosis and triage of critically ill patients, but not always feasible in out-of-hospital settings due to the lack of suitable devices. We performed an extensive validation of a novel prototype in-ear device, which was proposed for the non-invasive, combined measurement of core body temperature (Tc), oxygen saturation (SpO2), and heart rate (HR) in harsh environments. A pilot study with randomized controlled design was conducted in the terraXcube environmental chamber. Participants were subsequently exposed to three 15 min test sessions at the controlled ambient temperatures of 20 °C, 5 °C, and − 10 °C, in randomized order. Vital parameters measured by the prototype were compared with Tc measurements from commercial esophageal (reference) and tympanic (comparator) probes and SpO2 and HR measurements from a finger pulse-oximeter (reference). Performance was assessed in terms of bias and Lin’s correlation coefficient (CCC) with respect to the reference measurements and analyzed with linear mixed models. Twenty-three participants (12 men, mean (SD) age, 35 (9) years) completed the experimental protocol. The mean Tc bias of the prototype ranged between − 0.39 and − 0.80 °C at ambient temperatures of 20 °C and 5 °C, and it reached − 1.38 °C only after 15 min of exposure to − 10 °C. CCC values ranged between 0.07 and 0.25. SpO2 and HR monitoring was feasible, although malfunctioning was observed in one third of the tests. SpO2 and HR bias did not show any significant dependence on environmental conditions, with values ranging from − 1.71 to − 0.52% for SpO2 and 1.12 bpm to 5.30 bpm for HR. High CCC values between 0.81 and 0.97 were observed for HR in all environmental conditions. This novel prototype device for measuring vital parameters in cold environments demonstrated reliability of Tc measurements and feasibility of SpO2 and HR monitoring. Through non-invasive and accurate monitoring of vital parameters from the ear canal our prototype may offer support in triage and treatment of critically ill patients in harsh out-of-hospital conditions.

PENGEMBANGAN PERANGKAT MULTI SENSOR UNTUK PRECISION FARMING MONITORING MEDIA TANAM

The development of a multi-sensor device and the implementation of a data transmission system to a database for predicting plant suitability parameters are the focus of this paper. This technology enables accurate and continuous data collection for agricultural condition monitoring. The study utilizes a DHT22 sensor for air temperature and humidity, a pH sensor for soil pH, a soil moisture sensor for soil moisture, a rain gauge sensor for rainfall, and a raindrop sensor for rain detection. The data from these sensors are managed by an ESP32 microcontroller and transmitted to a Firebase database. The multi-sensor device was functionally tested and compared with valid reference sensor readings. The results show that all the multi-sensor devices and the recording system in the Firebase database functioned well. The average measurement error for the DHT22 temperature sensor was 1.15°C, with 2.9% for humidity, 0.72% for the pH sensor, and the soil moisture sensor showed 100% accuracy. This system can be applied as a parameter for predicting the suitability of plant types with soil and environmental conditions.

Mitigation of electronic crosstalk interference in graphene transistor biosensors

The need for higher sensitivity and parallel detection of multiple biological targets drives the research on electronic biosensing platforms based on two-dimensional (2D) materials. However, signal fidelity from individual sensors may be compromised due to the existing nonidealities of 2D electronic devices. Here, we use graphene field effect transistors (gFETs) as a model system to investigate a multisensor device architecture. We identify two potential sources of crosstalk interference between sensors in such a platform: resistive interference through shared gFET channels and capacitive coupling through the shared device substrate. Having illustrated these sources of interference, we offer solutions to mitigate them for ensuring the fidelity of the biosensor signal. This study provides important practical insights for researchers working on 2D biosensor platforms.

Portable Arduino-Based Multi-Sensor Device (SBEDAD): Measuring the Built Environment in Street Cycling Spaces.

The built environment's impact on human activities has been a hot issue in urban research. Compared to motorized spaces, the built environment of pedestrian and cycling street spaces dramatically influences people's travel experience and travel mode choice. The streets' built environment data play a vital role in urban design and management. However, the multi-source, heterogeneous, and massive data acquisition methods and tools for the built environment have become obstacles for urban design and management. To better realize the data acquisition and for deeper understanding of the urban built environment, this study develops a new portable, low-cost Arduino-based multi-sensor array integrated into a single portable unit for built environment measurements of street cycling spaces. The system consists of five sensors and an Arduino Mega board, aimed at measuring the characteristics of the street cycling space. It takes air quality, human sensation, road quality, and greenery as the detection objects. An integrated particulate matter laser sensor, a light intensity sensor, a temperature and humidity sensor, noise sensors, and an 8K panoramic camera are used for multi-source data acquisition in the street. The device has a mobile power supply display and a secure digital card to improve its portability. The study took Beijing as a sample case. A total of 127.97 G of video data and 4794 Kb of txt records were acquired in 36 working hours using the street built environment data acquisition device. The efficiency rose to 8474.21% compared to last year. As an alternative to conventional hardware used for this similar purpose, the device avoids the need to carry multiple types and models of sensing devices, making it possible to target multi-sensor data-based street built environment research. Second, the device's power and storage capabilities make it portable, independent, and scalable, accelerating self-motivated development. Third, it dramatically reduces the cost. The device provides a methodological and technological basis for conceptualizing new research scenarios and potential applications.

Multi-Sensor Device for Traceable Monitoring of Indoor Environmental Quality.

The Indoor Environmental Quality (IEQ) combines thermal, visual, acoustic, and air-quality conditions in indoor environments and affects occupants' health, well-being, and comfort. Performing continuous monitoring to assess IEQ is increasingly proving to be important, also due to the large amount of time that people spend in closed spaces. In the present study, the design, development, and metrological characterization of a low-cost multi-sensor device is presented. The device is part of a wider system, hereafter referred to as PROMET&O (PROactive Monitoring for indoor EnvironmenTal quality & cOmfort), that also includes a questionnaire for the collection of occupants' feedback on comfort perception and a dashboard to show end users all monitored data. The PROMET&O multi-sensor monitors the quality conditions of indoor environments thanks to a set of low-cost sensors that measure air temperature, relative humidity, illuminance, sound pressure level, carbon monoxide, carbon dioxide, nitrogen dioxide, particulate matter, volatile organic compounds, and formaldehyde. The device architecture is described, and the design criteria related to measurement requirements are highlighted. Particular attention is paid to the calibration of the device to ensure the metrological traceability of the measurements. Calibration procedures, based on the comparison to reference standards and following commonly employed or ad hoc developed technical procedures, were defined and applied to the bare sensors of air temperature and relative humidity, carbon dioxide, illuminance, sound pressure level, particulate matter, and formaldehyde. The next calibration phase in the laboratory will be aimed at analyzing the mutual influences of the assembled multi-sensor hardware components and refining the calibration functions.

A Multisensor Device Intended as an IoT Element for Indoor Environment Monitoring.

This work presents a multisensor device which is intended as an element of IoT for indoor environment (IE) monitoring. It is a portable, small-size, lightweight, energy-efficient direct-reading instrument. The device has an innovative design and construction. It offers real-time measurements of a wide spectrum of physical and chemical quantities (light intensity, temperature, relative humidity, pressure, CO2 concentration, content of volatile organic compounds including formaldehyde, NO2, and particulate matter), data storage (microSD; server as an option), transmission (WiFi; GSM and Ethernet as options), and visualization (smartphone application; PC as an option). Commercial low-cost sensors were utilized, which have been arranged in the individual sensing modules. In the case of gas sensors, dynamic exposure was chosen to ensure a minimum response time. The MQTT protocol was applied for data transmission and communication with other devices, as well as with the user. The multisensor device can collect huge amounts of data about the indoor environment to provide the respective information to the IoT. The device can be configured to control actuators of various auxiliary devices and equipment including external systems used for ventilation, heating, and air conditioning. The prototype is fully operational. The exemplary results of IE monitoring were shown.

A Preliminary Investigation about the Influence of WIMU PROTM Location on Heart Rate Accuracy: A Comparative Study in Cycle Ergometer.

Technological development has boosted the use of multi-sensor devices to monitor athletes' performance, but the location and connectivity between devices have been shown to affect data reliability. This preliminary study aimed to determine whether the placement of a multi-sensor device (WIMU PROTM) could affect the heart rate signal reception (GARMINTM chest strap) and, therefore, data accuracy. Thirty-two physical education students (20 men and 12 women) performed 20 min of exercise in a cycle ergometer based on the warm-up of the Function Threshold Power 20 test in laboratory conditions, carrying two WIMU PROTM devices (Back: inter-scapula; Bicycle: bicycle's handlebar-20 cm from the chest) and two GARMINTM chest straps. A one-dimensional statistical parametric mapping test found full agreement between the two situations (inter-scapula vs. bicycle's handlebar). Excellent intra-class correlation values were obtained during the warm-up (ICC = 0.99, [1.00-1.00], p < 0.001), the time trial test (ICC = 0.99, [1.00-1.00], p < 0.001) and the cool-down (ICC = 0.99, [1.00-1.00], p < 0.001). The Bland-Altman plots confirmed the total agreement with a bias value of 0.00 ± 0.1 bpm. The interscapular back placement of the WIMU PROTM device does not affect heart rate measurement accuracy with a GARMINTM chest strap during cycling exercise in laboratory conditions.

The HealthTracker System: App and Cloud-Based Wearable Multi-Sensor Device for Patients Health Tracking

Telemedicine has emerged as a vital component of contemporary healthcare, revolutionizing the way medical services are delivered and accessed (e.g., it enables patients living in underserved or rural areas to receive medical consultation and treatment remotely). Moreover, telemedicine plays a pivotal role in improving healthcare efficiency by reducing wait times, minimizing unnecessary hospital visits, and optimizing resource allocation. In this paper, we present HealthTracker, a monitoring infrastructure for patients comprising two Internet of Things (IoT) devices (one of which was designed and created by us) and a mobile app that sends data collected by the IoT devices to a cloud service. All these components work together to provide an innovative system able to monitor patient health condition, provide alerts in emergency cases, and elaborate upon data to improve the quality of medical care. Preliminary tests show that the system works well, and real experimentation will start soon in collaboration with the local health authority.

Kernel random forest with black hole optimization for heart diseases prediction using data fusion.

In recent years, the Internet of Things has played a dominant role in various real-time problems and given solutions via sensor signals. Monitoring the patient health status of Internet of Medical Things (IoMT) facilitates communication between wearable sensor devices and patients through a wireless network. Heart illness is one of the reasons for the increasing death rate in the world. Diagnosing the disease is done by the fusion of multi-sensor device signals. Much research has been done in predicting the disease and treating it correctly. However, the issues are accuracy, consumption time, and inefficiency. To overcome these issues, this paper proposed an efficient algorithm for fusing the multi-sensor signals from wearable sensor devices, classifying the medical signal data and predicting heart disease using the hybrid technique of kernel random forest with the Black Hole Optimization algorithm (KRF-BHO). This KRF-BHO is used for sensor data fusion, while XG-Boost is used to classify echocardiogram images. Accuracy in the training phase with multi-sensor data fusion data set of proposed work KRF-BHO with XGBoost classifier is 94.12%; in the testing phase, the accuracy rate is 95.89%. Similarly, for the Cleveland Dataset, the proposed work KRF-BHO with XGBoost classifier is 95.78%; in the testing phase, the accuracy rate is 96.21%.

Numerical and experimental studies of the electrical communication sensing device for the tunnel boring complex control system

The article considers the issues of studying the magnetic field distribution created by the power electric cable in the loca-tion of the tunneling shield of the tunnel boring complex by numerical modeling with the subsequent full-scale experi-ment on the device layout. The influence of external magnetic field sources on the readings of three-component ferro-probe sensors was determined. The significant influence of parasitic noise caused by external magnetic fields and high sensitivity of sensors has been determined, which requires the use of additional filters. When using ferroprobe sensors, the most informative is the sensor component coinciding with the longitudinal axis of the tunneling shield. Studies have shown that at current values more than 200 A and change of cable location during the experiment, there is a possibility to register signals on other components of the ferroprobe sensor and subsequent location determination of the cable with current. The results of the experiments confirmed the possibility to practically realize the multisensor device of the control system of the tunnel boring complex shield movement process.

Comparative study between scan sampling behavioral observations and an automatic monitoring image system on a commercial fattening pig farm

Automation is an important element in modern livestock farming. Image computer analysis is the automation technology aiming to monitor farm animals by recording continuous images. Further analysis can be carried out to assess more precisely and effectively farm animals’ welfare. The aim of this study was to determine the applicability of the commercial multi-sensor device (Peek Analytics) developed by Copeeks SAS (France), in comparison to human-based observations used to assess behaviors in pigs, including posture (standing/resting), area within the pen (feeding/drinking/resting/enrichment), and activity level (number of active pigs). Two Peek Analytics (Peek 3 and Peek 4) were installed on a commercial fattening pig farm in Murcia (Spain). Each device recorded data of two pens (39 pigs in four pens in total). Scan sampling was the human-based behavioral observation method used in this study. Data was collected for five consecutive days, in the following intervals: 09:00-11:00, 13:00-15:00, and 16:00-18:00 (30 hours of observation in total). Every pig was observed six times per hour and hence the information analyzed includes 7020 observations (180 observations/pig). The comparison between data from human observation and Peek Analytics was performed by using Pearson correlation tests. Posture, areas of interest, and activity level were analyzed separately, as well as data recorded by Peek 3 and 4. Results indicated that Peek Analytics showed a better agreement with human observation, when recording posture(r=0.77, P&amp;lt;0.01) and area within the pen (r=0.77, P&amp;lt;0.01), than when recording activity level (r=0.35, P&amp;lt;0.01). Two devices performed differently in general, with Peek 3 having better agreement than Peek 4 with human observation, regardless of posture, area within the pen, and activity level. The better agreement in Peek 3 may be attributed to the smaller number of pigs in Peek 3 (18) compared to Peek 4 (22). We can conclude from the study that image computer analysis may be reliable in assessing posture and area within the pen of pigs. On the other hand, a moderate agreement in activity level between human observation and computer vision can be due to different methodologies of recording the activity, rather than due to low accuracy of Peek Analytics.

Accuracy and User Acceptability of 24-hour Ambulatory Blood Pressure Monitoring by a Prototype Cuffless Multi-Sensor Device Compared to a Conventional Oscillometric Device

Objective 24-hour ambulatory blood pressure monitoring (24ABPM) is state of the art in out-of-office blood pressure (BP) monitoring. Due to discomfort and technical limitations related to cuff-based 24ABPM devices, methods for non-invasive and continuous estimation of BP without the need for a cuff have gained interest. The main aims of the present study were to compare accuracy of a pulse arrival time (PAT) based BP-model and user acceptability of a prototype cuffless multi-sensor device (cuffless device), developed by Aidee Health AS, with a conventional cuff-based oscillometric device (ReferenceBP) during 24ABPM. Methods Ninety-five normotensive and hypertensive adults underwent simultaneous 24ABPM with the cuffless device on the chest and a conventional cuff-based oscillometric device on the non-dominant arm. PAT was calculated using the electrocardiogram (ECG) and photoplethysmography (PPG) sensors incorporated in the chest-worn device. The cuffless device recorded continuously, while ReferenceBP measurements were taken every 20 minutes during daytime and every 30 minutes during nighttime. Two-minute PAT-based BP predictions corresponding to the ReferenceBP measurements were compared with ReferenceBP measurements using paired t-tests, bias, and limits of agreement. Results Mean (SD) of ReferenceBP compared to PAT-based daytime and nighttime systolic BP (SBP) were 129.7 (13.8) mmHg versus 133.6 (20.9) mmHg and 113.1 (16.5) mmHg versus 131.9 (23.4) mmHg. Ninety-five % limits of agreements were [-26.7, 34.6 mmHg] and [-20.9, 58.4 mmHg] for daytime and nighttime SBP respectively. The cuffless device was reported to be significantly more comfortable and less disturbing than the ReferenceBP device during 24ABPM. Conclusions In the present study, we demonstrated that a general PAT-based BP model had unsatisfactory agreement with ambulatory BP during 24ABPM, especially during nighttime. If sufficient accuracy can be achieved, cuffless BP devices have promising potential for clinical assessment of BP due to the opportunities provided by continuous BP measurements during real-life conditions and high user acceptability.

PENERAPAN TEKNOLOGI CERDAS PENYIRAMAN TANAMAN HIDROPONIK BERBASIS MIKROKONTROLER DAN MULTISENSOR PADA PEMBUDIDAYA TANAMAN HIDROPONIK KABUPATEN MAJENE

Smart technology has provided many benefits for all human activities, including hydroponic farming systems. The consistent challenge in the process of cultivating plants using hydroponic systems is the monitoring of partner farmers in managing plant nutrition. Smart technology, utilizing microcontrollers and multisensors, can enhance hydroponic management, particularly concerning the control of nutrient availability, including saturation, water pH, and water availability in the hydroponic system. The purpose of this activity is to address partner-related issues regarding their lack of knowledge and skills in managing businesses aided by smart technology such as microcontrollers and multisensors and to improve the economic viability of partner businesses by producing nutritious and high-quality vegetables. The method used in this community engagement activity involves initial observation at the partner's business location, theoretical instruction to enhance partner knowledge, and finally, training in the creation and operation of smart technology involving microcontrollers and multisensors in the partner's hydroponic area. Questionnaires were distributed to assess the initial conditions before the training and to measure the success of the training after its completion. The initial observation showed a lack of knowledge about smart technology using microcontrollers and multisensors. Based on this, theoretical training was conducted to improve partner knowledge about smart technology using microcontrollers and multisensors. The theoretical content included 1) technology and innovation in hydroponic plant nutrition, 2) an introduction to microcontrollers and sensors in agriculture/horticulture technology, and 3) a demonstration of monitoring physical parameters and nutrient content of hydroponic plants using the Wokwi simulator. Subsequently, a simulation of microcontroller and multisensory device use was carried out, resulting in an enhancement of partner skills in using microcontrollers and multisensors. The improvement in partner knowledge and skills after participating in this training is evident from the questionnaire results, and partner satisfaction with the conducted training is also evident. The conclusion of this community engagement activity is that partners have gained knowledge and skills after participating in the training on the application of smart technology for microcontroller-based irrigation of hydroponic plants with multisensor systems in hydroponic plant cultivation.

Design of a Multisensory Device for Tomato Volatile Compound Detection Based on a Mixed Metal Oxide-Electrochemical Sensor Array and Optical Reader.

Insufficient control of tomato ripening before harvesting and infection by fungal pests produce large economic losses in world tomato production. Aroma is an indicative parameter of the state of maturity and quality of the tomato. This study aimed to design an electronic system (TOMATO-NOSE) consisting of an array of 12 electrochemical sensors, commercial metal oxide semiconductor sensors, an optical camera for a lateral flow reader, and a smartphone application for device control and data storage. The system was used with tomatoes in different states of ripeness and health, as well as tomatoes infected with Botrytis cinerea. The results obtained through principal component analysis of the olfactory pattern of tomatoes and the reader images show that TOMATO-NOSE is a good tool for the farmer to control tomato ripeness before harvesting and for the early detection of Botrytis cinerea.

BSafe-360: An All-in-One Naturalistic Cycling Data Collection Tool.

The popularity of bicycles as a mode of transportation has been steadily increasing. However, concerns about cyclist safety persist due to a need for comprehensive data. This data scarcity hinders accurate assessment of bicycle safety and identification of factors that contribute to the occurrence and severity of bicycle collisions in urban environments. This paper presents the development of the BSafe-360, a novel multi-sensor device designed as a data acquisition system (DAS) for collecting naturalistic cycling data, which provides a high granularity of cyclist behavior and interactions with other road users. For the hardware component, the BSafe-360 utilizes a Raspberry Pi microcomputer, a Global Positioning System (GPS) antenna and receiver, two ultrasonic sensors, an inertial measurement unit (IMU), and a real-time clock (RTC), which are all housed within a customized bicycle phone case. To handle the software aspect, BSafe-360 has two Python scripts that manage data processing and storage in both local and online databases. To demonstrate the capabilities of the device, we conducted a proof of concept experiment, collecting data for seven hours. In addition to utilizing the BSafe-360, we included data from CCTV and weather information in the data analysis step for verifying the occurrence of critical events, ensuring comprehensive coverage of all relevant information. The combination of sensors within a single device enables the collection of crucial data for bicycle safety studies, including bicycle trajectory, lateral passing distance (LPD), and cyclist behavior. Our findings show that the BSafe-360 is a promising tool for collecting naturalistic cycling data, facilitating a deeper understanding of bicycle safety and improving it. By effectively improving bicycle safety, numerous benefits can be realized, including the potential to reduce bicycle injuries and fatalities to zero in the near future.

The Use of the Multisensor HeartLogic Algorithm for Heart Failure Remote Monitoring in Patients With Left Ventricular Assist Devices.

The Use of the Multisensor HeartLogic Algorithm for Heart Failure Remote Monitoring in Patients With Left Ventricular Assist Devices.

A Novel Multisensor Device for Absolute Intracardiac Pressure Measurement, Detection, and Management of Heart Failure

A Novel Multisensor Device for Absolute Intracardiac Pressure Measurement, Detection, and Management of Heart Failure