Gyroscope Sensor Research Articles

Microcontroller-Based Design of Self-Balancing Two-Wheeler Robotic Vehicles Using Gyroscopic Sensor

Robots have seamlessly integrated into our daily lives, serving a multitude of functions from lifesaving tasks to providing entertainment, highlighting their indispensable role in modern society.As their popularity continues to soar, an abundance of research initiatives has been taken all over the world. One particularly captivating field of robotics research that has captured widespread attention focuses on unstable systems, exemplified by endeavours like the development of self-balancing electric vehicles, such as the inverted pendulum, stimulating innovation and curiosity on a global scale. Self-balancing electric vehicles are a fascinating innovation in the field of personal transportation. These vehicles utilize advanced technologies such as gyroscopes, accelerometers, and sophisticated control algorithms to maintain balance without the need for a rider to actively stabilize the vehicle. One of the most well-known examples of this technology is the Segway, which popularized the concept of self-balancing transportation. The objective of thiswork is to make a functioning prototype model which is used to learn the principles of control theories behind it. Gyroscopic sensors serve the critical function of detecting any deviation from optimal balance by sensing the tilting movements of the device, ensuring prompt corrective actions to maintain stability. It offers several potential benefits, including increased stability and safety for riders, particularly in urban environments where navigating through crowded streets can be challenging.

How the air slows a closing book

When two flat surfaces approach each other, the fluid in between is accelerated and ejected from the sides at large speeds. This situation occurs often in everyday life, such as when you step in a puddle and create splashes of water, or when you clap your hands or close a book and create jets of air. For these systems, the inertia of the fluid resists the acceleration, creating large nonlinear forces on the flat surfaces. In this work, we study the case of a closing book. The fluid motion in this case is relatively easy to model, using the conservation of mechanical energy, and to measure using a MEMS gyroscopic sensor. This study reveals the unusual forces that occur when two plates collide in an experiment that can be performed by students at home.

Wearable Accelerometer and Gyroscope Sensors for Estimating the Severity of Essential Tremor.

Several validated clinical scales measure the severity of essential tremor (ET). Their assessments are subjective and can depend on familiarity and training with scoring systems. We propose a multi-modal sensing using a wearable inertial measurement unit for estimating scores on the Fahn-Tolosa-Marin tremor rating scale (FTM) and determine the classification accuracy within the tremor type. 17 ET participants and 18 healthy controls were recruited for the study. Two movement disorder neurologists who were blinded to prior clinical information viewed video recordings and scored the FTM. Participants drew a guided Archimedes spiral while wearing an inertial measurement unit placed at the mid-point between the lateral epicondyle of the humerus and the anatomical snuff box. Acceleration and gyroscope recordings were analyzed. The ratio of the power spectral density between frequency bands 0.5-4 Hz and 4-12 Hz, and the sum of power spectrum density over the entire spectrum of 2-74 Hz, for both accelerometer and gyroscope data, were computed. FTM was estimated using regression model and classification using SVM was validated using the leave-one-out method. Regression analysis showed a moderate to good correlation when individual features were used, while correlation was high ([Formula: see text] = 0.818) when suitable features of the gyro and accelerometer were combined. The accuracy for two-class classification of the combined features using SVM was 91.42% while for four-class it was 68.57%. Potential applications of this novel wearable sensing method using a wearable Inertial Measurement Unit (IMU) include monitoring of ET and clinical trials of new treatments for the disorder.

The challenges of developing a textile-based device for recognizing swallowing movements during different swallowing tasks

Swallowing difficulty, also known as dysphagia, is a fairly common symptom that may lead to malnutrition, dehydration, pulmonary complications, and a reduced quality of life. Currently, a swallowing evaluation requires a hospital environment, skilled personnel, and expensive devices. Thus, it is important to develop a successful method for evaluating swallowing in an everyday environment. This study developed a textile-based solution that resembles a neck gaiter. Wireless accelerometer and gyroscope sensors embedded inside the gaiter were used to detect swallowing movements. The recognition of swallows was achieved through cervical auscultation and video footage, and this data was subsequently compared with the information obtained from wireless accelerometer and gyroscope sensors. Based on the measurement results for 17 adults without dysphagia, the developed neck gaiter can identify individual swallows of 5 sips of water (with a 97 % identification success rate) and consecutive swallows of 100 ml of water (98 % identification success rate). However, we were not able to recognize swallowing movements from speaking, chewing and head movements. Future research is now needed to develop a prototype to differentiate swallowing movements from other movements, such as chewing and speaking.

A Multi-Layer Classifier Model XR-KS of Human Activity Recognition for the Problem of Similar Human Activity

Sensor-based human activity recognition is now well developed, but there are still many challenges, such as insufficient accuracy in the identification of similar activities. To overcome this issue, we collect data during similar human activities using three-axis acceleration and gyroscope sensors. We developed a model capable of classifying similar activities of human behavior, and the effectiveness and generalization capabilities of this model are evaluated. Based on the standardization and normalization of data, we consider the inherent similarities of human activity behaviors by introducing the multi-layer classifier model. The first layer of the proposed model is a random forest model based on the XGBoost feature selection algorithm. In the second layer of this model, similar human activities are extracted by applying the kernel Fisher discriminant analysis (KFDA) with feature mapping. Then, the support vector machine (SVM) model is applied to classify similar human activities. Our model is experimentally evaluated, and it is also applied to four benchmark datasets: UCI DSA, UCI HAR, WISDM, and IM-WSHA. The experimental results demonstrate that the proposed approach achieves recognition accuracies of 97.69%, 97.92%, 98.12%, and 90.6%, indicating excellent recognition performance. Additionally, we performed K-fold cross-validation on the random forest model and utilized ROC curves for the SVM classifier to assess the model’s generalization ability. The results indicate that our multi-layer classifier model exhibits robust generalization capabilities.

Autonomous and smart cleaning mobile robot system to improve the maintenance efficiency of solar photovoltaic array

A solar photovoltaic (PV) array is part of a PV power plant as a generation unit. PV array that are usually placed on top of buildings or the ground will be very susceptible to dirt and dust. Thus, this dirt and dust will be able to reduce the performance and work efficiency of the generation unit. Cleaning PV arrays by manpower requires high effort, cost, and risk, especially in higher location. This study presents the design of a mobile robot that is used to replace human labor to clean PV arrays. That way, the PV array maintenance steps can reduce operational costs and risks. This intelligent controlled mobile robot can maneuver safely and efficiently over PV arrays. gyroscope and proximity sensors are used to detect and follow the sweep path over the entire PV array area. Proportional integral derivative (PID) control test makes the robot can stabilize in about 5.72 seconds to keep on the track. The smart PV cleaning robot has average operation time about 13 minutes in autonomous mode and 20-24 minutes in manual mode. The operation of the robot is effective to give more efficiency on the use of energy, time, and maintenance costs of PV array system.

Human Activity Recognition Using CNN and Lstm Deep Learning Algorithms

Human Activity Recognition recognizes and classifies the activities performed by the users or people based on the data collected from the sensors of special devices such as smart-watches, smartphones etc. It has become easy to collect a huge amount of data from inertial sensors that are embedded in wearable devices. An accelerometer and gyroscope sensors are most commonly used inertial sensors. There are various already available datasets, in our paper, we are using the Wireless Sensor Data Mining dataset which contains 1,098,207 data of 6 physical activities performed. In this paper, the activities we aim to classify are walking, jogging, going up and downstairs, standing, and sitting. There are various algorithms applied on the various datasets. In our paper, we use Convolutional Neural Network and Long Short-Term Memory deep learning algorithm on the data set, we split the data into training data [80%] and testing data [20%]. By using a confusion matrix, we recognize and classify the activities performed using maximum accuracy.

Head Mounted IMU-Based Driver’s Gaze Zone Estimation Using Machine Learning Algorithm

Predicting the driver’s gaze could be important information in preventing accidents while driving. In this study, machine learning models for estimating the driver’s gaze distraction through head movement data were created and their performance was compared and evaluated. Participants wore glasses-type eye trackers and performed the task of selecting the touch screen buttons while driving. The input variable used in the model was data obtained from a 3-axis accelerometer sensor and a 3-axis gyroscope sensor, and the target variable was eye-gaze data. As a result, it was confirmed that the gaze area could be estimated with a precision, sensitivity, specificity, and F1-score of 72.1%, 72.5%, 66.0%, and 69.3%, respectively, only with the head movement sensing data. The model trained using time-series datasets had higher performance than using non-time series datasets. This study presented one alternative that could be used to determine the driver’s status with an inexpensive sensor.

On-farm assessment of grazing behaviour of dairy cows in two pasture management systems by low-cost and reliable cowtrackers

In dairy farming, sensors are commonly applied to monitor animal health and performance. One key task of such sensors is the behaviour classification of cows. The behaviour classification needs to take into account the differences between stable and pasture: the process of feed intake by grazing is different from feed intake in the stable. Pasture management systems differ and this introduces further variability within pasture-based systems, e.g. by different sward heights. Moreover, on pasture, spatial variables like walking distance allow the inference of further information about cow behaviour. Therefore, data accessibility and customizability of sensors is important for the development of pasture specific behaviour classification algorithms. Our objective was to construct an open-source and neck located cowtracker that combines an accelerometer, gyroscope and global navigation satellite system sensors with an Arduino microcontroller. The blueprints of the cowtracker are provided in the manuscript. Cows were tracked on two commercial dairy farms with different breeds and in different grazing systems to ensure high variability of the data. Animal behaviour was classified by training a random forest algorithm that was used to predict animal behaviour from the sensor data. The training data was obtained from visual observations in the field and differentiating between the behaviours grazing, rumination, walking, resting and drinking. The overall accuracy was 0.94 when predicting test data consisting of data retained randomly from all individuals. The predictive performance slightly decreased to 0.89 overall accuracy when whole animal data sets were retained as test data. The decrease in predictive performance was stronger for the rarely observed behaviour drinking. As a showcase, cow behaviour was analysed and differed between the two grazing systems. In continuous grazing at low sward heights (6 cm) the cows allocated more time to grazing and walking whereas in rotational grazing with pre- and post-grazing sward heights of 11.3 cm and 6.3 cm respectively, the cows allocated more time to rumination and less to grazing. The observed grazing time was comparably short in this experiment. The spatial observations revealed longer walking distances and higher herd scatter in continuous grazing which reflects the greater effort in satisfying the feed demand in low swards.

Digital Phenotyping for Monitoring and Disease Trajectory Prediction of Patients With Cancer: Protocol for a Prospective Observational Cohort Study.

Timely recognition of cancer progression and treatment complications is important for treatment guidance. Digital phenotyping is a promising method for precise and remote monitoring of patients in their natural environments by using passively generated data from sensors of personal wearable devices. Further studies are needed to better understand the potential clinical benefits of digital phenotyping approaches to optimize care of patients with cancer. We aim to evaluate whether passively generated data from smartphone sensors are feasible for remote monitoring of patients with cancer to predict their disease trajectories and patient-centered health outcomes. We will recruit 200 patients undergoing treatment for cancer. Patients will be followed up for 6 months. Passively generated data by sensors of personal smartphone devices (eg, accelerometer, gyroscope, GPS) will be continuously collected using the developed LAIMA smartphone app during follow-up. We will evaluate (1) mobility data by using an accelerometer (mean time of active period, mean time of exertional physical activity, distance covered per day, duration of inactive period), GPS (places of interest visited daily, hospital visits), and gyroscope sensors and (2) sociability indices (frequency of duration of phone calls, frequency and length of text messages, and internet browsing time). Every 2 weeks, patients will be asked to complete questionnaires pertaining to quality of life (European Organization for Research and Treatment of Cancer Core Quality of Life Questionnaire [EORTC QLQ-C30]), depression symptoms (Patient Health Questionnaire-9 [PHQ-9]), and anxiety symptoms (General Anxiety Disorder-7 [GAD-7]) that will be deployed via the LAIMA app. Clinic visits will take place at 1-3 months and 3-6 months of the study. Patients will be evaluated for disease progression, cancer and treatment complications, and functional status (Eastern Cooperative Oncology Group) by the study oncologist and will complete the questionnaire for evaluating quality of life (EORTC QLQ-C30), depression symptoms (PHQ-9), and anxiety symptoms (GAD-7). We will examine the associations among digital, clinical, and patient-reported health outcomes to develop prediction models with clinically meaningful outcomes. As of July 2023, we have reached the planned recruitment target, and patients are undergoing follow-up. Data collection is expected to be completed by September 2023. The final results should be available within 6 months after study completion. This study will provide in-depth insight into temporally and spatially precise trajectories of patients with cancer that will provide a novel digital health approach and will inform the design of future interventional clinical trials in oncology. Our findings will allow a better understanding of the potential clinical value of passively generated smartphone sensor data (digital phenotyping) for continuous and real-time monitoring of patients with cancer for treatment side effects, cancer complications, functional status, and patient-reported outcomes as well as prediction of disease progression or trajectories. PRR1-10.2196/49096.

Construction of features database using different platform navigation solutions for aiding PDR algorithm

Nowadays, navigation can be accomplished based on several technologies. Some of these technologies rely solely on camera vision-based algorithms, whereas others rely solely on motion sensors’ data. Furthermore, some solutions combine vision-based algorithms with data from motion sensors. In this study, three platform navigation algorithms (stereo visual odometry technique, combining the distance calculated from the visual optical flow with estimated heading from gyroscope sensor measurements, and combining the distance calculated from the visual optical flow with estimated heading from magnetometer sensor measurements) were compared to select the most accurate among them to be employed for features database construction. This features database, which contains various well-described features with known three-dimensional world coordinates, can be utilized to help any indoor pedestrian dead reckoning (PDR) navigation solution as well as mapping purposes. Tests' results demonstrated that vision-based technology (stereo visual odometry technique) is more accurate than the others however it should be aided with additional constraints to improve navigation positional accuracy. Afterward, this chosen technology was used to construct a well-described features database which was checked to assess its accuracy.

Utilization of the Accelerometer Sensor in Android Smartphones Development of Ground Movement Detection Tools in Areas Landslide Prone Based on the Internet of Things

The result of this research is an Internet of Things (IoT) program that utilizes the accelerometer sensor on Android smartphones as a tool for detecting ground movement in landslide-prone areas. If the sensor indicates a change in its coordinates, it will be used as an indication of ground movement on the accelerometer sensor. Meanwhile, the gyroscope sensor detects vibrations, and the program will send notifications as a sign of ground movement. Overall, the development of this landslide movement detection device in landslide-prone areas employs the Research and Development (R&D) methodology. The Research and Development method aims to produce specific products and test the effectiveness of these products. The development method for the ground movement system utilizes a prototype method to observe and comprehend how the application functions. The results of this research demonstrate that the accelerometer sensor on Android smartphones can produce sufficiently accurate data for detecting ground movement. Integration with IoT technology opens opportunities for developing an early warning system that is effective and responsive to the potential landslide disasters. It is hoped that this research can provide a significant contribution to the development of more advanced ground movement detection systems in the future.

Hyperlocal air pollution in an urban environment - measured with low-cost sensors

Air pollution levels can vary significantly over short distances, particularly in urban areas and near emission sources. This study examined the performance of low-cost sensor devices for monitoring levels of NO2, O3, and PM2.5 along two closely spaced (average 8 m) routes in Copenhagen, Denmark. One route was located near a lake (Route 1) and the other near a busy road (Route 2). The routes were walked in tandem for 84 h. The mode of deployment was determined using an accelerometer, gyroscope, and light sensor, achieving a 97.4 % accuracy rate. Field calibration with multivariate linear regression proved the most robust calibration model across pollutants, yielding mean R2-values of 0.64, 0.79, and 0.48 for NO2, O3, and PM2.5, respectively. The sensor intervariability was generally low, with mean R2-values of 0.84–0.94 for PM2.5 measured with optical particle sensors and 0.88–0.90 for NO2 and O3 measured with metal-oxide sensors. Results showed significantly higher NO2 concentrations on Route 2 (21.6 ± 6.6 ppb) compared to Route 1 (10.1 ± 4.0 ppb) during mornings. However, no significant differences in O3 and PM2.5 concentrations were observed. Our findings demonstrate that low-cost sensors can accurately quantify air pollution exposure in urban areas with high spatiotemporal resolution.

DeepaMed: Deep learning-based medication adherence of Parkinson's disease using smartphone gait analysis

ObjectivesParkinson's disease (PD) is a neurodegenerative chronic disorder with multiple motor and non-motor symptoms. As PD has no ultimate cure, physicians aim to delay PD complications, especially those that degrade the patient's quality of life such as motor symptoms and dyskinesia. Patients' lack of adherence to prescribed medication is a major challenge for physicians, especially for patients suffering from chronic conditions. The Centers for Disease Control and Prevention (CDC) estimates that medication non-adherence causes 30 to 50 percent of chronic disease treatment failures and 125,000 deaths per year in the USA (U.S. Foods and Drugs Administration (FDA) “Why You Need to Take Your Medications as Prescribed or Instructed” https://www.fda.gov/drugs/special-features/why-you-need-take-your-medications-prescribed-or-instructed. June 2021). In PD patients particularly, adherence varies between 10% and 67% (Straka et al., 2019Straka, Igor, et al. "Adherence to pharmacotherapy in patients with Parkinson's disease taking three and more daily doses of medication." Frontiers in neurology 10 (2019): 799). ObjectiveThe goal of this work is to remotely determine whether PD patients have taken their medication, by analyzing gait data gathered from their smartphone sensors. Using this approach, physicians can track the level of medication adherence of their PD patients. MethodologyUsing data from the mPower study (Bot et al., 2016), we selected 152 PD patients who recorded at least 3 walks before and 3 after taking medications and 304 healthy controls (HC) who recorded 3 walks at minimum. We extracted each subject's gait cycle from their accelerometer and gyroscope sensors data. The sensor data corresponding to gait cycles were fed to DeePaMed; a multilayer Conventional Neural Network (CNN), crafted for patches of gait strides. DeePaMed classified 30 s of a walk as either PD patient “On” vs. “Off” medication, or if the gait data belongs to an HC. ResultsOur DeePaMed model was able to discriminate PD patients on-vs off-medication and baseline HC walk with an accuracy of 98.2%. The accuracy of our CNN model surpassed that of traditional Machine Learning methods by over 17%. We also found that our model performed best with inputs containing a minimum of 10 full gait strides. ConclusionMedication non-adherence can be accurately predicted using smartphone sensing of the motor symptoms of PD, suggesting that PD patients’ medication response and non-adherence can be monitored remotely via smartphone-based measures.

VibWall : Smartphone’s Vibration Challenge-response for Wall Crack Detection

As the building ages, the wall structure may become deteriorated (e.g., wall cracks, discontinuities, and corrosion) due to the variation of the environment (i.e., temperature and humidity). Moreover, these wall cracks, discontinuities, and corrosion will affect the living comfort and coziness. As such, the wall health diagnostic becomes crucial for the safety and comfort of modern buildings. However, the existing wall health detection techniques (e.g., UWB radars, acoustic sensing, and sensor embedding techniques) are high-cost, not ubiquitous, and not robust to the variation of the environment. In this article, we propose VibWall , a system that can use the smartphone’s sensors (i.e., accelerometer, gyroscope, and vibrator) to detect the wall’s structural health. Specifically, the wall cracks can be detected for living safety, comfort, and coziness. Our key idea is that the smartphone’s vibration is absorbed, reflected, and propagated disparately based on the physical structure of the wall. To be specific, we employ a novel challenge-response scheme, where the challenge is a sequence of heterogeneous vibration patterns from the smartphone’s vibrator, and the responses to these vibrations are sensed by the smartphone’s gyroscope and accelerometer sensors. Then, the machine learning-based classifier (e.g., random forest classifier) will be used to discriminate between the healthy wall and the wall with cracks, discontinuities, or corrosion based on these responses. Our experimental results show good performance on the wall’s structural health detection with the wall specimen and real-world walls.

An Evaluation On The Entropy Supplying Capability Of Smartphone Sensors

Abstract Random numbers are very important for the security of computer system. However, generating qualified random numbers is difficult because we cannot always successfully introduce dedicated random number hardware into computer system. Although most operating systems provide random number generation capabilities, the effective entropy supply is still dependent on the hardware platform including memory and clocks etc. However, obtaining hardware events such as clocks requires system privileges, which is not conducive for entropy estimation at the application layer. In contrast, data related to the sensor hardware can be extracted directly at the application layer. These sensor data contain some randomness and may be used as a noise source. In this way, applications can use these sensors to implement their own proprietary random number generators. Before taking these sensors as the noise source, it is necessary to fully evaluate their entropy supply capability. In this paper, 300 Android smartphones and 30 iOS smartphones are selected as samples and their sensor entropy supply capabilities are comprehensively evaluated. Based on the entropy evaluation results, we give some suggestions on how to generate random numbers using these sensor data. We first design a framework for evaluating the entropy supply capability for smartphone sensors, based on the min-entropy estimation method proposed in NIST SP 800-90B. According to this framework, we simulate stationary and mobile working states for each smartphone, and collect sufficient sensor data as the min-entropy estimation dataset. The min-entropy estimation results show that in the stationary working state, each ACCELEROMETER sensor data collection can obtain at least 1.5 bits of entropy in Android, while each GYROSCOPE sensor data collection can obtain at least 20 bits of entropy in iOS. In the mobile working state, each ACCELEROMETER sensor data collection can obtain at least 1.9 bits of entropy, while each GYROSCOPE sensor data acquisition in iOS system can obtain at least 27 bits of entropy. This means that we can still get a stable entropy output from the sensor even when the smartphone is in stationary working state. Statistical analysis of the data using cross correlation methods suggests it is hard for an attacker to guess or predict the random numbers generated by a smartphone through another smartphone put in the similar external environment.

Analysis and design of a fourth-order ΣΔ ADC for MEMS digital gyroscope sensors

This paper presents a fourth-order sigma-delta (ΣΔ) ADC applied to a micro-electro-mechanical system (MEMS) gyroscope system. First, a system-level model of ΣΔ modulator containing non-ideal factors is established, and the optimal system performance of the ΣΔ modulator is determined by optimizing the feedforward path and the feedback coefficient of the feedback path. Then, the operational transconductance amplifier (OTA) with chopping technique and clock timing to control bias current in the first stage integrator of the circuit design is able to reduce not only the flicker noise of the modulator, but also reduce the system power consumption. Third, the design of other essential circuit modules is presented, and the circuit performance meets the requirements and low-power consumption is achieved. The designed ΣΔ ADC uses a 0.18 μm CMOS BCD process, with a ΣΔ modulator area of 0.64 mm2 and a digital filter area of 2.03 mm2. When the sampling frequency is 2 MHz and the signal bandwidth is 7.8 kHz, the experimental measurement results show that the proposed ΣΔ modulator with the fourth-order CIFF structure can achieve a signal-to-noise ratio (SNR) of 66.3 dB and an effective number of bits (ENOB) of 10.07 bits, with a dynamic range (DR) of 89 dB and a power consumption of 9 mW. The SNR of ΣΔ ADC after filtering is 63 dB, which meets the requirements of high DR and low-power consumption of MEMS digital gyroscope sensors.

Block Chain and Machine Learning Models to Evaluate Faults in the Smart Manufacturing System

Smart Manufacturing Systems (SMS) have revolutionized industrial processes by incorporating automation, data analytics, and real-time monitoring to improve efficiency and quality. However, ensuring the reliability and fault tolerance of SMS remains a challenge. This paper proposes an innovative approach that combines Blockchain technology with Machine Learning (ML) models to evaluate faults in SMS. By leveraging the immutability and transparency of the blockchain and the predictive capabilities of ML, this approach enhances fault detection, facilitates traceability, and ultimately contributes to the resilience of smart manufacturing. The industrial sector's increase in data creation has made monitoring systems a crucial idea for management and decision-making. The Internet of Things (IoT), which is sensor-based and one of the most advanced and potent technologies today, can process appropriate ways to monitor the manufacturing process. The research's suggested method combines IoT, machine learning (ML), and monitoring of the industrial system. Temperature, humidity, gyroscope, and accelerometer IoT sensors are used to gather environmental data. Sensor data is produced in unstructured, enormous, and real-time data forms. Many big data approaches are used to process the data further. This system's hybrid prediction model employs the Random Forest classification approach to weed out outliers in the sensor data and aid in defect identification throughout the production process. The suggested approach was examined for South Korean vehicle production. This system uses a strategy to protect and strengthen data trust in order to prevent genuine data changes with fictitious data and system interactions. The efficacy of the suggested methodology in comparison to other methods is provided in the results section. Furthermore, compared to other inputs, the hybrid prediction model offers a respectable fault prediction. The suggested technique is anticipated to improve decision-making and decrease errors during the production process.

The development of a wearable device for Sign Language Gesture Recognition (SLGR) by using Conceptual Design Approach (CDA)

In recent years, there has been a significant focus on researching and developing Sign Language Gesture Recognition (SLGR) for people with hearing and speaking impairments. This is especially important in Indonesia, where there are approximately two million people with these disabilities. However, current research and developments on SLGR devices are specifically designed to understand only particular sign language systems, such as Korean Standard Sign Language (KSDSL) and American Sign Language (ASL), each of which has its unique gestures and models. As a result, no device has been developed to recognize the gestures of the Indonesian sign language system, known as Sistem Isyarat Bahasa Indonesia (SIBI). Therefore, this study aims to develop an SLGR device that can recognize and translate SIBI gestures into output images via text and speech. The development of the SLGR device in this study is conducted by using the Conceptual Design Approach (CDA) methodology. Where in this case, previous research on SLGR devices is first observed as a benchmark for comparison. Furthermore, the benchmark is used as the basis for the function, sub-function, and specification of the proposed design. Based on these stages of benchmarking and concept development, the study concluded that the final design of the proposed SLGR device is constructed by using 5 flex and Gyroscope sensors connected wirelessly to the Raspberry microcontroller. The device is equipped with a voice system and LCR RPi as the output system for translation. Based on the combination of these sensors, the device is able to identify any particular gestures that correspond to words and phrases in SIBI and translate them into speech via the designated speaker and text display on the LCD screen. To fully understand the performance of the device, experimental tests are conducted by analyzing the input of 26 alphabets in the SIBI system. As a result, the device demonstrated an average of 92% accuracy to convert sign language into voice and text, which demonstrates the usefulness of the proposed device

A Machine Learning-Based Correlation Analysis between Driver Behaviour and Vital Signs: Approach and Case Study.

Driving behaviour analysis has drawn much attention in recent years due to the dramatic increase in the number of traffic accidents and casualties, and based on many studies, there is a relationship between the driving environment or behaviour and the driver's state. To the best of our knowledge, these studies mostly investigate relationships between one vital sign and the driving circumstances either inside or outside the cabin. Hence, our paper provides an analysis of the correlation between the driver state (vital signs, eye state, and head pose) and both the vehicle maneuver actions (caused by the driver) and external events (carried out by other vehicles or pedestrians), including the proximity to other vehicles. Our methodology employs several models developed in our previous work to estimate respiratory rate, heart rate, blood pressure, oxygen saturation, head pose, eye state from in-cabin videos, and the distance to the nearest vehicle from out-cabin videos. Additionally, new models have been developed using Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) to classify the external events from out-cabin videos, as well as a Decision Tree classifier to detect the driver's maneuver using accelerometer and gyroscope sensor data. The dataset used includes synchronized in-cabin/out-cabin videos and sensor data, allowing for the estimation of the driver state, proximity to other vehicles and detection of external events, and driver maneuvers. Therefore, the correlation matrix was calculated between all variables to be analysed. The results indicate that there is a weak correlation connecting both the maneuver action and the overtaking external event on one side and the heart rate and the blood pressure (systolic and diastolic) on the other side. In addition, the findings suggest a correlation between the yaw angle of the head and the overtaking event and a negative correlation between the systolic blood pressure and the distance to the nearest vehicle. Our findings align with our initial hypotheses, particularly concerning the impact of performing a maneuver or experiencing a cautious event, such as overtaking, on heart rate and blood pressure due to the agitation and tension resulting from such events. These results can be the key to implementing a sophisticated safety system aimed at maintaining the driver's stable state when aggressive external events or maneuvers occur.