Search Results (25)

In this paper, we propose noncontact heart rate (HR), oxygen saturation (SpO2), and respiratory rate (RR) detection methods using a smartphone camera. HR frequency is detected through filtering after obtaining a remote PPG (rPPG) signal and its power spectral density (PSD) is detected using color difference signal amplification and the plane-orthogonal-to-the-skin method. Additionally, the SpO2 is detected using the HR frequency and the absorption ratio of the G and B color channels based on oxyhemoglobin absorption and reflectance theory. After this, the respiratory frequency is detected using the PSD of rPPG through respiratory frequency band filtering. For the image sequences recorded under various imaging conditions, the proposed method demonstrated superior HR detection accuracy compared to existing methods. The confidence intervals for HR and SpO2 detection were analyzed using Bland–Altman plots. Furthermore, the proposed RR detection method was also verified to be reliable. Full article

Background: The US Food and Drug Administration (FDA) and International Organization for Standardization (ISO) clearance standards for the clinical use of smart device pulse oximetry require in-laboratory human hypoxemia testing in healthy human individuals using arterial blood gas analysis. Methods: We evaluated the SpO₂ measurements of the Samsung smartphone (Galaxy S9/10) and smartwatch (Galaxy 4) at stable arterial oxygen saturations (SaO₂) between 70 and 100% in 24 healthy participants. Testing followed FDA/ISO-stipulated procedures for pulse oximetry performance validation, which include questionnaire estimation of skin tone based on Fitzpatrick estimation of skin types I–VI. During testing, inspired oxygen, nitrogen, and carbon dioxide partial pressures were monitored and adjusted via partial rebreathing circuits to achieve stable target arterial blood oxygen (SaO₂) plateaus between 70% and 100%. Arterial blood samples were taken at each plateau, with device SpO₂ readings taken at each sample extraction. An ABL-90FLEX blood gas analyzer determined arterial blood sample SaO₂. Bias, calculated from device readings minus corresponding arterial blood measurements, was reported as root mean square deviation (RMSD). Results: Combined Participants demographics were: 62.5% female; median age 26 years (range 21–46); and race/ethnicity 16.7% African American, 33.3% Asian, 12.5% multi-ethnic, and 37.5% Caucasian. Fitzpatrick Skin Scale-identified skin tones were: white–fair (I&II), 20.8%; average–light brown (III–IV), 54% and brown–black (V–VI), 25%. There were no adverse events. The RMSD values of SpO₂ measurements were: smartphone 2.6% (257 data pairs) and smartwatch 1.8% (247 data pairs). Conclusions: Device SpO₂ demonstrated RMSD < 3.0% to SaO₂, meeting FDA/ISO clearance standards at the time of study. However, additional testing in persons with darker skin tones is necessary. Smartphones and paired wearables, when cleared for clinical use following revision of FDA clearance standards, may expand access to remote pulse oximetry. Full article

Low-cost technology devices, such as smartphones (SPs) and smart watches (SWs), are widely used today to monitor various health effects and environmental risk factors associated with them. However, the efficacy of using these devices as monitoring tools is largely unknown. The present study attempts to narrow this knowledge gap by reviewing recent studies in which low-cost technological tools were used to monitor sleep and associated environmental risk factors. The study focuses on peer-refereed articles that appear in three major scientific databases, Web of Science, Scopus, and ScienceDirect, and were published between 2002 and 2022. Of the 15,000+ records retrieved from these databases by the systematic literature review (PRISMA) search, 15 studies were identified as the most relevant and consequently analyzed. The analysis shows that nighttime light pollution and noise are environmental factors that are most commonly monitored by low-cost technology tools (eight studies), followed by temperature monitoring (seven studies), humidity monitoring (seven studies), and CO₂ monitoring (four studies). In eight studies, tandems of SPs and SWs were used to monitor sleep, while in six studies, data obtained from SPs and SWs were compared with records obtained from conventional monitoring devices. In general, SP and SW measurements were found to be fairly accurate for monitoring sleep and light pollution and less accurate for monitoring noise. At the same time, no studies conducted to date and analyzed in this review demonstrated the effectiveness of SPs and SWs in monitoring ambient temperature, humidity, and air pressure. Our general conclusion is that although SPs and SWs often lack the precision of professional instruments, they can nevertheless be used for large-scale field research and citizen science initiatives, while their feasibility and effectiveness for monitoring several environmental attributes have yet to be determined. Full article

Introduction: Remote health monitoring plays a crucial role in telehealth services and the effective management of patients, which can be enhanced by vital sign prediction from facial videos. Facial videos are easily captured through various imaging devices like phone cameras, webcams, or surveillance systems. Methods: This study introduces a hybrid deep learning model aimed at estimating heart rate (HR), blood oxygen saturation level (SpO2), and blood pressure (BP) from facial videos. The hybrid model integrates convolutional neural network (CNN), convolutional long short-term memory (convLSTM), and video vision transformer (ViViT) architectures to ensure comprehensive analysis. Given the temporal variability of HR and BP, emphasis is placed on temporal resolution during feature extraction. The CNN processes video frames one by one while convLSTM and ViViT handle sequences of frames. These high-resolution temporal features are fused to predict HR, BP, and SpO2, capturing their dynamic variations effectively. Results: The dataset encompasses 891 subjects of diverse races and ages, and preprocessing includes facial detection and data normalization. Experimental results demonstrate high accuracies in predicting HR, SpO2, and BP using the proposed hybrid models. Discussion: Facial images can be easily captured using smartphones, which offers an economical and convenient solution for vital sign monitoring, particularly beneficial for elderly individuals or during outbreaks of contagious diseases like COVID-19. The proposed models were only validated on one dataset. However, the dataset (size, representation, diversity, balance, and processing) plays an important role in any data-driven models including ours. Conclusions: Through experiments, we observed the hybrid model’s efficacy in predicting vital signs such as HR, SpO2, SBP, and DBP, along with demographic variables like sex and age. There is potential for extending the hybrid model to estimate additional vital signs such as body temperature and respiration rate. Full article

Earthquake disasters are usually very destructive and pose a great threat to human life and property. Based on the relatively mature technology of unmanned aerial vehicles (UAVs) and their high flexibility, these devices are widely used for information collection and processing in post-disaster relief operations. However, UAVs are limited by their battery capacity, which makes it hard for them to perform both large-scale information gathering and data processing at the same time. Nowadays, smartphones (SPs), which have become portable devices for people, have the characteristics of strong computing power, rich communication means and wide distribution. Therefore, in this study, we developed SPs to assist UAVs in computation incentive-based task execution. To balance the cost of UAVs and the execution utility of SPs during the task execution process, a multi-objective optimization problem was established, and the Multi-Objective Mutation-Immune Bat (MOMIB) algorithm was developed to optimize the proposed problem. Additionally, considering the diversity of tasks in real-world scenarios, Quality of Service (QoS) coefficients were introduced to ensure the performance requirements of different types of tasks. A large number of simulation experiments show that the task-offloading scheme that we propose is effective. Full article

Significant advances in reverse engineering and additive manufacturing have the potential to provide a faster, accurate, and cost-effective process chain for preserving, analyzing, and presenting forensic impression evidence in both 3D digital and physical forms. The objective of the present research was to evaluate the capabilities and limitations of five 3D scanning technologies, including laser scanning (LS), structured-light (SL) scanning, smartphone (SP) photogrammetry, Microsoft Kinect v2 RGB-D camera, and iPhone’s LiDAR (iLiDAR) Sensor, for 3D reconstruction of 3D impression evidence. Furthermore, methodologies for 3D reconstruction of latent impression and visible 2D impression based on a single 2D photo were proposed. Additionally, the FDM additive manufacturing process was employed to build impression evidence models created by each procedure. The results showed that the SL scanning system generated the highest reconstruction accuracy. Consequently, the SL system was employed as a benchmark to assess the reconstruction quality of other systems. In comparison to the SL data, LS showed the smallest absolute geometrical deviations (0.37 mm), followed by SP photogrammetry (0.78 mm). In contrast, the iLiDAR exhibited the largest absolute deviations (2.481 mm), followed by Kinect v2 (2.382 mm). Additionally, 3D printed impression replicas demonstrated superior detail compared to Plaster of Paris (POP) casts. The feasibility of reconstructing 2D impressions into 3D models is progressively increasing. Finally, this article explores potential future research directions in this field. Full article

Fasciola hepatica has a complex lifecycle with multiple intermediate and definitive hosts and influenced by environmental factors. The disease causes significant morbidity in children and its prevalent worldwide. There is lack of data about distribution and burden of the disease in endemic regions, owing to poor efficacy of the different diagnostic methods used. A novel PCR-based test was developed by using a portable mini-PCR^® platform to detect Fasciola sp. DNA and interpret the results via a fluorescence viewer and smartphone image analyzer application. Human stool, snail tissue, and water samples were used to extract DNA. Primers targeting the ITS-1 of the 18S rDNA gene of Fasciola sp. were used. The limit of detection of the mini-PCR test was 1 fg/μL for DNA samples diluted in water, 10 fg/μL for Fasciola/snail DNA scramble, and 100 fg/μL for Fasciola/stool DNA scramble. The product detection by agarose gel, direct visualization, and image analyses showed the same sensitivity. The Fh mini-PCR had a sensitivity and specificity equivalent to real-time PCR using the same specimens. Testing was also done on infected human stool and snail tissue successfully. These experiments demonstrated that Fh mini-PCR is as sensitive and specific as real time PCR but without the use of expensive equipment and laboratory facilities. Further testing of multiple specimens with natural infection will provide evidence for feasibility of deployment to resource constrained laboratories. Full article

Indoor navigation is the most challenging environment regarding precise positioning service for a smartphone’s physical quality limitations and interferences for high buildings, trees and multipath fading in the GNSS signal received. A GPS by itself cannot offer a solution; the A-GNSS from a network mobile provided through telecommunication infrastructure provides information that is useful to counteract these issues. A smartphone has full connectivity to the mobile network 24/7 and has access to the GNSS database when required, and the assisted information is sent over an Internet Protocol (IP) and processed by the GNSS chip, increasing the accuracy, TTFF, and availability of data even in harsh environments. The outdoor, light indoor, and urban canyon scenarios are experienced when driving in some places in the city, and they are recorded with Geo++ and processed with RTKlib using a single frequency in a standalone and multi-constellation double-frequency smartphone, Xiaomi Mi 8, with A-GNSS. The results show good accuracy in the SPS for over 10 (m) and in assisted positioning over 50 (m); the TTFF in assisted positioning is always 5 (s), and in the SPS, it reaches 20 (s). Finally, during the trajectory, only the assisted positioning can compute the position; this is because of the data availability from a mobile network. Full article

Electronic tickets (e-tickets) are gradually being adopted as a substitute for paper-based tickets to bring convenience to customers, corporations, and governments. However, their adoption faces a number of practical challenges, such as flexibility, privacy, secure storage, and inability to deploy on IoT devices such as smartphones. These concerns motivate the current research on e-ticket systems, which seeks to ensure the unforgeability and authenticity of e-tickets while simultaneously protecting user privacy. Many existing schemes cannot fully satisfy all these requirements. To improve on the current state-of-the-art solutions, this paper constructs a blockchain-enhanced privacy-preserving e-ticket system for IoT devices, dubbed PriTKT, which is based on blockchain, structure-preserving signatures (SPS), unlinkable redactable signatures (URS), and zero-knowledge proofs (ZKP). It supports flexible policy-based ticket purchasing and ensures user unlinkability. According to the data minimization and revealing principle of GDPR, PriTKT empowers users to selectively disclose subsets of (necessary) attributes to sellers as long as the disclosed attributes satisfy ticket purchasing policies. In addition, benefiting from the decentralization and immutability of blockchain, effective detection and efficient tracing of double spending of e-tickets are supported in PriTKT. Considering the impracticality of existing e-tickets schemes with burdensome ZKPs, we replace them with URS/SPS or efficient ZKP to significantly improve the efficiency of ticket issuing and make it suitable for use on smartphones. Full article

The state-of-the-art Android environment, available on a major market share of smartphones, provides an open playground for sensor data gathering. Moreover, the rise in new types of devices (e.g., wearables/smartwatches) is further extending the market opportunities with a variety of new sensor types. The existing implementations of biometric/medical sensors can allow the general public to directly access their health measurements, such as Electrocardiogram (ECG) or Oxygen Saturation (SpO2). This access greatly increases the possible applications of these devices with the combination of all the onboard sensors that are broadly in use nowadays. In this study, we look beyond the current state of the art into the positioning capacities of Android smart devices and wearables, with a focus on raw Global Navigation Satellite Systems (GNSS) measurements that are still mostly lacking in the research world. We develop a novel open-source Android application working in both smartphone and smartwatch environments for multi-sensor measurement data logging that also includes GNSS, an Inertial Navigation System (INS) magnetometer, and a barometer. Four smartphones and one smartwatch are used to perform surveys in different scenarios. The extraction of GNSS raw data from a wearable device has not been reported yet in the literature and no open-source app has existed so far for extracting GNSS data from wearables. Not only the developed app but also the results of these measurement surveys are provided as an open-access dataset. We start by defining our methodology and the acquisition protocol, and we dive into the structure of the dataset files. We also propose a first analysis of the data logged and evaluate the data according to several performance metrics. A discussion reviewing the capacities of smart devices for advanced positioning is proposed, as well as the current open challenges. Full article

Since the outbreak of COVID-19, as of January 2023, there have been over 670 million cases and more than 6.8 million deaths worldwide. Infections can cause inflammation in the lungs and decrease blood oxygen levels, which can lead to breathing difficulties and endanger life. As the situation continues to escalate, non-contact machines are used to assist patients at home to monitor their blood oxygen levels without encountering others. This paper uses a general network camera to capture the forehead area of a person’s face, using the RPPG (remote photoplethysmography) principle. Then, image signal processing of red and blue light waves is carried out. By utilizing the principle of light reflection, the standard deviation and mean are calculated, and the blood oxygen saturation is computed. Finally, the effect of illuminance on the experimental values is discussed. The experimental results of this paper were compared with a blood oxygen meter certified by the Ministry of Health and Welfare in Taiwan, and the experimental results had only a maximum error of 2%, which is better than the 3% to 5% error rates in other studies The measurement time was only 30 s, which is better than the one minute reported using similar equipment in other studies. Therefore, this paper not only saves equipment expenses but also provides convenience and safety for those who need to monitor their blood oxygen levels at home. Future applications can combine the SpO2 detection software with camera-equipped devices such as smartphones and laptops. The public can detect SpO2 on their own mobile devices, providing a convenient and effective tool for personal health management. Full article

Correction of rotational malalignments caused by fractures is essential as it may cause pain and gait disturbances. This study evaluated the intraoperative use of a smartphone application (SP app) to measure the extent of corrective rotation in patients treated using minimally invasive derotational osteotomy. Intraoperatively, two parallel 5 mm Schanz pins were placed above and below the fractured/injured site, and derotation was performed manually after percutaneous osteotomy. A protractor SP app was used intraoperatively to measure the angle between the two Schanz pins (angle-SP). Intramedullary nailing or minimally invasive plate osteosynthesis was performed after derotation, and computerized tomography (CT) scans were used to assess the angle of correction postoperatively (angle-CT). The accuracy of rotational correction was assessed by comparing angle-SP and angle-CT. The mean preoperative rotational difference observed was 22.1°, while the mean angle-SP and angle-CT were 21.6° and 21.3°, respectively. A significant positive correlation between angle-SP and angle-CT was observed, and 18 out of 19 patients exhibited complete healing within 17.7 weeks (1 patient exhibited nonunion). These findings suggest that using an SP app during minimally invasive derotational osteotomy can result in accurate correction of malrotation of long bones in a reproducible manner. Therefore, SP technology with integrated gyroscope function represents a suitable alternative for determination of the magnitude of rotational correction when performing corrective osteotomy. Full article

In the US, at least one fall occurs in at least 28.7% of community-dwelling seniors 65 and older each year. Falls had medical costs of USD 51 billion in 2015 and are projected to reach USD 100 billion by 2030. This review aims to discuss the extent of smartphone (SP) usage in fall detection and prevention across a range of care settings. A computerized search was conducted on six electronic databases to investigate the use of remote sensing technology, wireless technology, and other related MeSH terms for detecting and preventing falls. After applying inclusion and exclusion criteria, 44 studies were included. Most of the studies targeted detecting falls, two focused on detecting and preventing falls, and one only looked at preventing falls. Accelerometers were employed in all the experiments for the detection and/or prevention of falls. The most frequent course of action following a fall event was an alarm to the guardian. Numerous studies investigated in this research used accelerometer data analysis, machine learning, and data from previous falls to devise a boundary and increase detection accuracy. SP was found to have potential as a fall detection system but is not widely implemented. Technology-based applications are being developed to protect at-risk individuals from falls, with the objective of providing more effective and efficient interventions than traditional means. Successful healthcare technology implementation requires cooperation between engineers, clinicians, and administrators. Full article

Mathematical and signal-processing methods were used to obtain reliable measurements of the heartbeat pulse rate and information on oxygen concentration in the blood using short video recordings of the index finger attached to a smartphone built-in camera. Various types of smartphones were used with different operating systems (e.g., iOS, Android) and capabilities. A range of processing algorithms were applied to the red-green-blue (RGB) component signals, including mean intensity calculation, moving average smoothing, and quadratic filtering based on the Savitzky–Golay filter. Two approaches—gradient and local maximum methods—were used to determine the pulse rate, which provided similar results. A fast Fourier transform was applied to the signal to correlate the signal’s frequency components with the pulse rate. We resolved the signal into its DC and AC components to calculate the ratio-of-ratios of the AC and DC components of the red and green signals, a method which is often used to estimate the oxygen concentration in blood. A series of measurements were performed on healthy human subjects, producing reliable data that compared favorably to benchmark data obtained by commercial and medically approved oximeters. Furthermore, the effect of the video recording duration on the accuracy of the results was investigated. Full article

Current m-Health scenarios in the smart living era, as the interpretation of the smart city at each person’s level, present several challenges associated with interoperability between different clinical devices and applications. The Continua Health Alliance establishes design guidelines to standardize application communication to guarantee interoperability among medical devices. In this paper, we describe the implementation of two IEEE agents for oxygen saturation level (SpO2) measurements and electrocardiogram (ECG) data acquisition, respectively, and a smartphone IEEE manager for validation. We developed both IEEE agents over the Bluetooth Health Device Profile following the Continua guidelines and they are part of a telemonitoring system. This system was evaluated in a sample composed of 10 volunteers (mean age 29.8 ± 7.1 y/o; 5 females) under supervision of an expert cardiologist. The evaluation consisted of measuring the SpO2 and ECG signal sitting and at rest, before and after exercising for 15 min. Physiological measurements were assessed and compared against commercial devices, and our expert physician did not find any relevant differences in the ECG signal. Additionally, the system was assessed when acquiring and processing different heart rate data to prove that warnings were generated when the heart rate was under/above the thresholds for bradycardia and tachycardia, respectively. Full article