Browse Topic: Physical examination
Electric Vehicles (EVs) are rapidly transforming the automotive landscape, offering a cleaner and more sustainable alternative to internal combustion engine vehicles. As EV adoption grows, optimizing energy consumption becomes critical to enhancing vehicle efficiency and extending driving range. One of the most significant auxiliary loads in EVs is the climate control system, commonly referred to as HVAC (Heating, Ventilation, and Air Conditioning). HVAC systems can consume a substantial portion of the battery's energy—especially under extreme weather conditions—leading to a noticeable reduction in vehicle range. This energy demand poses a challenge for EV manufacturers and users alike, as range anxiety remains a key barrier to widespread EV acceptance. Consequently, developing intelligent climate control strategies is essential to minimize HVAC power consumption without compromising passenger comfort. These strategies may include predictive thermal management, cabin pre-conditioning, zonal climate control, and integration with renewable energy sources. By implementing such energy-efficient solutions, EVs can achieve better range performance, improved user satisfaction, and greater environmental benefits. Modern EV climate control systems increasingly rely on intelligent features such as Auto mode, which dynamically adjusts fan speed, airflow direction, and temperature settings based on real-time cabin and ambient conditions. By leveraging sensor data and adaptive control algorithms, Auto mode optimizes thermal comfort while minimizing unnecessary energy expenditure. This automated regulation plays a crucial role in reducing HVAC-related power consumption, thereby contributing to overall range improvement and enhancing system efficiency without compromising passenger comfort. This study focuses on the development of three distinct Auto mode calibration levels for each set condition, designed to achieve the same cabin temperature with varying dynamic responses and energy consumption profiles. In Auto mode, the cabin temperature is regulated through intelligent control of compressor speed, blower speed, and evaporator temperature. While all Auto levels can maintain the desired setpoint, the time required to reach this temperature and the system’s responsiveness to sudden thermal loads can vary significantly. This study introduces three distinct calibration profiles, each engineered to achieve the same cabin temperature under different dynamic conditions and energy consumption levels. These profiles allow users to choose between faster thermal response or reduced power usage, effectively enabling a trade-off between immediate comfort and extended driving range
Scientists used a “smart” shirt equipped with an electrocardiogram to track participants’ heart-rate recovery after exercise and developed a tool for analyzing the data to predict those at higher or lower risk of heart-related ailments.
This study introduces a novel in-cabin health monitoring system leveraging Ultra-Wideband (UWB) radar technology for real-time, contactless detection of occupants' vital signs within automotive environments. By capturing micro-movements associated with cardiac and respiratory activities, the system enables continuous monitoring without physical contact, addressing the need for unobtrusive vehicle health assessment. The system architecture integrates edge computing capabilities within the vehicle's head unit, facilitating immediate data processing and reducing latency. Processed data is securely transmitted via HTTPS to a cloud-based backend through an API Gateway, which orchestrates data validation and routing to a machine learning pipeline. This pipeline employs supervised classifiers, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Random Forest (RF) to analyze features such as temporal heartbeat variability, respiration rate stability, and heart rate. Empirical evaluations demonstrate the system's proficiency in classifying occupant states, including normal, distressed, and unconscious conditions, achieving high prediction accuracy with low false positive rates. Notably, the system attains sub-10-second detection latency and facilitates end-to-end response actions within a 5-minute window. Experimental deployment in a Mercedes vehicle demonstrated high accuracy in occupancy detection (97%), vital sign monitoring (94%), and full ERS (Emergency Response System) activation within five minutes, meeting Euro NCAP 2025+ Child Presence Detection (CPD) requirements. Furthermore, the cloud infrastructure supports the accumulation of health data, contributing to personalized driver profiles and informed decision-making for future interventions. This research underscores the potential of UWB radar technology in augmenting automotive safety through real-time health monitoring, paving the way for smarter and more secure vehicular environments.
Chronic stress can lead to increased blood pressure and cardiovascular disease, decreased immune function, depression, and anxiety. Unfortunately, the tools we use to monitor stress are often imprecise or expensive, relying on self-reporting questionnaires and psychiatric evaluations.
Advancements in sensor technologies have led to increased interest in detecting and diagnosing “driver states”—collections of internal driver factors generally associated with negative driving performance, such as alcohol intoxication, cognitive load, stress, and fatigue. This is accomplished using imperfect behavioral and physiological indicators that are associated with those states. An example is the use of elevated heart rate variability, detected by a steering wheel sensor, as an indicator of frustration. Advances in sensor technologies, coupled with improvements in machine learning, have led to an increase in this research. However, a limitation is that it often excludes naturalistic driving environments, which may have conditions that affect detection. For example, reductions in visual scanning are often associated with cognitive load [1]; however, these reductions can also be related to novice driver inexperience [2] and alcohol intoxication [3]. Through our analysis of the research, we discover that the tendency to explore these singular driver states with only a comparison to “normal” driving is common. Additionally, research on interventions for these driver states is relatively scarce (fewer than 10% of cognitive load-related papers we examined assessed or discussed intervention solutions) and narrowly tailored to specific states [e.g., 4, vis-à-vis cognitive load]. States that share common behavioral and physiological markers tend to be explored independently when a more universal and integrated approach may be warranted. In this paper, we identify the need for a driver state and intervention framework that addresses these limitations by exploring state indicators and their overlap, interventions for one or multiple states, and major research gaps. Our framework offers practical approaches for handling one or many driver states, including interventions that may be deployed at different timings during a trip.
Biosensors are devices that can monitor physiological states, like heart rate or blood pressure, or detect biological parameters such as glucose levels or the presence of specific proteins in the blood. The information biosensors collect can be used to support a medical diagnosis (for instance, a specific infection) or to provide feedback to the user on parameters of interest (for instance, the number of calories burned in a workout).
A team of researchers at the University of California – San Diego has developed a new and improved wearable ultrasound patch for continuous and noninvasive blood pressure monitoring. Their work marks a major milestone, as the device is the first wearable ultrasound blood pressure sensor to undergo rigorous and comprehensive clinical validation on over 100 patients.
Wearable devices like smartwatches and fitness trackers interact with parts of our bodies to measure and learn from internal processes, such as our heart rate or sleep stages. Now, MIT researchers have developed wearable devices that may be able to perform similar functions for individual cells inside the body.
The healthcare industry is evolving and facing two major challenges. First, the rise of chronic diseases. By 2050, chronic diseases such as cardiovascular diseases, cancer, diabetes, and respiratory illnesses could account for 86 percent of the 90 million deaths each year, according to the World Health Organization (WHO) in its 2023 World Health Statistics report. This increase is due to factors such as an aging population, lifestyle changes, and risk factors like high blood pressure, high blood sugar, and air pollution. Consequently, this creates a second challenge: added strain on healthcare resources. To address this, WHO recommends tackling the root causes of chronic diseases, promoting healthier behaviors, and ensuring universal access to healthcare resources.
Solving a decades-old problem, a multi-disciplinary team of Caltech researchers has figured out a method to noninvasively and continually measure blood pressure anywhere on the body with next to no disruption to the patient. A device based on the new technique holds the promise to enable better vital-sign monitoring at home, in hospitals, and possibly even in remote locations where resources are limited.
iMotions employs neuroscience and AI-powered analysis tools to enhance the tracking, assessment and design of human-machine interfaces inside vehicles. The advancement of vehicles with enhanced safety and infotainment features has made evaluating human-machine interfaces (HMI) in modern commercial and industrial vehicles crucial. Drivers face a steep learning curve due to the complexities of these new technologies. Additionally, the interaction with advanced driver-assistance systems (ADAS) increases concerns about cognitive impact and driver distraction in both passenger and commercial vehicles. As vehicles incorporate more automation, many clients are turning to biosensor technology to monitor drivers' attention and the effects of various systems and interfaces. Utilizing neuroscientific principles and AI, data from eye-tracking, facial expressions and heart rate are informing more effective system and interface design strategies. This approach ensures that automation advancements improve rather than hinder the driving experience.
Recent advances in technology have opened many possibilities for using wearable and implantable sensors to monitor various indicators of patient health. Wearable pressure sensors are designed to respond to very small changes in bodily pressure, so that physical functions such as pulse rate, blood pressure, breathing rates, and even subtle changes in vocal cord vibrations can be monitored in real time with a high degree of sensitivity.
Pump systems are ubiquitous in medical and life science products, from blood pressure monitors and drug-delivery devices, to pipettors and diagnostic instruments. As the demand for smaller, less intrusive — sometimes even wearable — products grow, engineers must meet these expectations without compromising on pump system performance.
Today’s necessity to reduce healthcare costs is generating a greater demand for medical electronics equipment that improves and expands patient diagnostics inside and outside healthcare facilities. For instance, portable medical instruments such as glucose meters, blood pressure monitors, oxygen meters, and automated external defibrillators (AED) have undergone many design considerations to be developed for doctors, paramedics, public use, and at home with at-risk patients. This article delves into the design considerations, challenges, and regulatory aspects of medical electronics and provides a case study involving automated external defibrillators (AEDs).
Made with a laser-modified graphene nanocomposite material, a wearable device can detect specific glucose levels in sweat for three weeks while simultaneously monitoring body temperature and pH levels.
SMARTSHAPE consortium, led from University of Galway, will develop an implantable medical device for continuous blood pressure monitoring. The consortium has developed an IP-protected technologically disruptive sensor for continuous pressure measurement. They plan to address challenges related to biocompatibility, longevity, and delivery to the target tissue. These need to be overcome to deliver the sensor to the market.
Made of graphene, a cuffless device is worn on the underside of the wrist and can measure blood pressure with comparable accuracy to a standard blood pressure cuff. While the technology is still in its early stages, the researchers envision that the monitor will be worn 24/7.
Engineers at the University of California San Diego have developed a thin, flexible, stretchy sweat sensor that can show the level of glucose, lactate, sodium, or pH of your sweat — at the press of a finger. It is the first standalone wearable device that allows the sensor to operate independently — sans any wired or wireless connection to external devices — to directly visualize the measurement’s results.
An active sound design (ASD) technique enables the implementation of a specific sound in addition to the real engine/e-motor sound in a vehicle. However, it is difficult to satisfy the various needs of customers because it can provide only a few sounds designed by the manufacturer. This paper presents the method of providing the appropriate driving sound and soundscape in an electric vehicle according to the driver’s emotion and driving environment in real-time. For this purpose, it is studied how to construct a driving sound library from the various sound sources and how to recognize a driver's total emotion from the multi-modal data such as facial expression, heart rate, and electrodermal activity using the CNN and support vector machine algorithms. Then it is discussed how to generate the driving sound of electric vehicle according to the driver’s emotion. Using these methods, a personalized driving sound suitable to the driver's total emotion is provided by using the ASD system of electric vehicle in real-time. Additionally, it is studied how to recognize the driving environment from the outside image and match the soundscape (e.g. effect sound, background music and so on) to playback in an audio amplifier using the CNN and machine learning algorithms. Finally, it shows the demonstration of a prototype system and the people's response in a real driving situation. It is expected that this system can provide a new user experience through the personalized sound in electric vehicle by understanding the customer's feeling and driving situation.
A new device from Lincoln Laboratory can now alert trainees when they are heading toward injury. The device continuously estimates a person’s core body temperature to determine their risk level for heat strain as they train. This risk is communicated on a smartwatch display, providing early warning to its wearer.
Engineers have created a flexible electronic sensing patch that can be sewn into clothing to analyze sweat for multiple markers. The patch could be used to diagnose and monitor acute and chronic health conditions or to monitor health during athletic or workplace performance. The device consists of special sensing threads, flexible electronic components, and wireless connectivity for real-time data acquisition, storage, and processing.
Stretchable, bendable “smart” textiles are poised to reshape clothes of all kinds, creating new opportunities for integrating advanced monitoring technologies into everyday items. Researchers are applying neuroscience and psychophysiology to build responsive technologies like those integrated in smart textiles. They found that wearable sensor systems don't seem to perform as well in monitoring heart rates as traditional electrodes.
If the smart textiles of the future are going to survive, their components are going to need to be resilient. Researchers have developed an ultra-sensitive, resilient strain sensor that can be embedded in textiles and soft robotic systems.
Telehealth has become a critical way for doctors to still provide health care while minimizing in-person contact during COVID-19. But with phone or online appointments, it’s harder for doctors to get important vital signs from a patient, such as their pulse or respiration rate, in real time.
At present, the research on fatigue driving at home and abroad mainly has the following three methods: (i) driving behavioral (vehicle-based), (ii) driver behavioral (video-based), and (iii) driver physiological signals measure. The physiology-based methods have the highest recognition result. When drivers are in a state of fatigue, the Autonomic Nervous System (ANS) activity will be reflected from the physiological signal. Most of the contact sensors are used to obtain the physiological signal information of the driver. However, the contact sensors will affect the driver's driving operation, so this paper uses the frequency-modulated continuous-wave (FMCW) radar to collect the physiological signals. A fatigue driving simulation experiment was designed to collect experimental subjects' physiological signal data and separate the steady heartbeat and respiratory signals. Perform heart rate variability (HRV) time domain and frequency domain analysis on the heartbeat signal, and get the time domain derived features: mean of heart rate (AVGHR), heart rate root mean square difference (rMSSD). Frequency domain derived features: heart rate low-frequency (LF), heart rate high-frequency (HF), ratio of heart rate low frequency to high frequency (LF/HF). Using the spectrum estimation to get the respiratory frequency and the mean of breathing, heart rate to breathing ratio are selected as the respiratory signal's time-domain derived features. Finally, a two-class model of fatigue driving is established based on the support vector machine (SVM) theory. The above seven feature indicators are used as feature vectors as the SVM input, and the classification model is trained through the k-fold cross-validation method. The test set is used for classification detection. The accuracy rates of normal and fatigued driving are 88.75% and 84.25%, respectively. We also use Random Forests for comparison experiments. The accuracy of the RF are 96.88% and 95.14% respectively.
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