Accelerometer Sensor Research Articles

Abiotic drivers of the space use and activity of gray reef sharks Carcharhinus amblyrhynchos in a dynamic tidal environment.

Predators display rhythms in behavior and habitat use, often with the goal of maximizing foraging success. The underlying mechanisms behind these rhythms are generally linked to abiotic conditions related to diel, lunar, or seasonal cycles. To understand their effects on the space use, activity, and swimming depth of gray reef sharks (Carcharhinus amblyrhynchos), we tagged 38 individuals with depth and accelerometer sensors in a French Polynesian atoll channel exposed to strong tidal flow, and monitored them over a year. C. amblyrhynchos used a larger space during nighttime and were more active at night and during outgoing currents. Shark activity also peaked during the full and new moons. The swimming depth of sharks was mostly influenced by diel cycles, with sharks swimming deeper during the day compared to nighttime. The dynamic energyscape may promote the emergence of discrete behavioral strategies in reef sharks that use the south channel of Fakarava for resting and foraging purposes. Turbulence imposed by outgoing tides induces additional foraging cost on sharks, shifting their hunting areas to the southern part of the channel, where turbulence is less pronounced. Understanding when and where sharks are active and foraging is important for our understanding of predator-prey dynamics and ecosystem dynamics. This study highlights how abiotic rhythms in a highly dynamic environment likely generate spatiotemporal heterogeneity in the distribution of predation pressure.

Advancing Objective Mobile Device Use Measurement in Children Ages 6–11 Through Built-In Device Sensors: A Proof-of-Concept Study

Mobile devices (e.g., tablets and smartphones) have been rapidly integrated into the lives of children and have impacted how children engage with digital media. The portability of these devices allows for sporadic, on-demand interaction, reducing the accuracy of self-report estimates of mobile device use. Passive sensing applications objectively monitor time spent on a given device but are unable to identify who is using the device, a significant limitation in child screen time research. Behavioral biometric authentication, using embedded mobile device sensors to continuously authenticate users, could be applied to address this limitation. This study examined the preliminary accuracy of machine learning models trained on iPad sensor data to identify the unique user of the device in a sample of children ages 6 to 11. Data was collected opportunistically from nine participants (8.2 ± 1.75 years, 5 female) in the sedentary portion of two semistructured physical activity protocols. SensorLog was downloaded onto study iPads and collected data from the accelerometer, gyroscope, and magnetometer sensors while the participant interacted with the iPad. Five machine learning models, logistic regression (LR), support vector machine, neural net (NN), k-nearest neighbors (k-NN), and random forest (RF), were trained using 57 features generated from the sensor output to perform multiclass classification. A train-test split of 80%–20% was used for model fitting. Model performance was evaluated using F1 score, accuracy, precision, and recall. Model performance was high, with F1 scores ranging from 0.75 to 0.94. RF and k-NN had the highest performance across metrics, with F1 scores of 0.94 for both models. This study highlights the potential of using existing mobile device sensors to continuously identify the user of a device in the context of screen time measurement. Future research should explore the performance of this technology in larger samples of children and in free-living environments.

Exploratory study to evaluate the acceptability of a wearable accelerometer to assess motor progression in motor neuron disease

Motor neuron disease (MND) is a rapidly progressive condition traditionally assessed using a questionnaire to evaluate physical function, the revised amyotrophic lateral sclerosis functional rating scale (ALSFRS-R). Its use can be associated with poor sensitivity in detecting subtle changes over time and there is an urgent need for more sensitive and specific outcome measures. The ActiGraph GT9X is a wearable device containing multiple sensors that can be used to provide metrics that represent physical activity. The primary aim of this study was to investigate the initial suitability and acceptability of limb-worn wearable devices to group of people with MND in Scotland. A secondary aim was to explore the preliminary associations between the accelerometer sensor data within the ActiGraph GT9X and established measures of physical function. 10 participants with MND completed a 12-week schedule of assessments including fortnightly study visits, both in-person and over videoconferencing software. Participants wore the device on their right wrist and right ankle for a series of movements, during a 6-min walking test and for a period of 24-h wear, including overnight. Participants also completed an ALSFRS-R and questionnaires on their experience with the devices. 80% of the participants found wearing these devices to be a positive experience and no one reported interference with daily living or added burden. However, 30% of the participants experienced technical issues with their devices. Data from the wearable devices correlated with established measures of physical function.

Edge Machine Learning-Enabled Predictive Fault Detection System for Conveyor Belt Maintenance Optimization in Industrial Settings

Digital transformation is essential for industrial and manufacturing sectors, especially in conveyor belt systems. On the other hand, Innovative electronic components and software applications offer opportunities for advanced fault prediction, minimizing operational disruptions, reducing inefficiencies, and enhancing overall industrial efficiency. Efficient conveyor belt systems are crucial for industrial operations, requiring early fault detection to prevent costly downtime and ensure safety. The utilization of embedded machine learning via the Edge Impulse platform enhances data collection and spectral feature extraction using wavelet approximation. This article presents a novel methodology for predictive fault detection in conveyor belt systems employing edge machine learning. Leveraging the ESP32 microcontroller and accelerometer sensor for real-time vibration data capture, a four-layer Artificial Neural Network (ANN) model, trained on 336 feature samples, is deployed via Edge Impulse and TensorFlow scripts. TensorFlow Lite compresses the model for microcontroller integration. The ESP32 microcontroller, acting as an edge device, achieves real-time predictions with 97.5% accuracy and 0.20 minimal loss. This work promises significant strides in predictive maintenance, offering cost savings, operational reliability, and efficiency gains in industrial settings, revolutionizing maintenance strategies.

Landslide Detection using IoT

The paper aims to decrease the number of accidents that occur on curved roadways. To do this, a warning LCD Display that displays as a vehicle approaches from the other side of the bend serves as a message to the driver. The IR transmitter and receiver sensor, which is connected to the Arduino Uno microcontroller, is used to detect the vehicle. And Motor operated gates are fixed upon each sides for free passage of vehicles from one side to other side. On the winding roads in the ghat portion, this might save thousands of lives. By implementing a new technique, they come up with a plan to prevent accidents after determining their causes and effects. Two IR sensors make up the new method, which alerts the vehicle on the opposite road. Landslide is one of the hazardous and critical geographical process, which damages to civil infrastructure and property as well as causes loss of life. This paper is an attempt with regard to the expansion of a landslide susceptible approach by using Accelerometer Sensor. And Rain Sensor is used for detecting heavy rainfall. Upon detecting the landslide condition or Heavy Rains it warns on display as a message and closes gates on either sides of ghat till road condition gets normal

BIKE RIDER SAFETY SYSTEM USING STM 32 MICROCONTROLLER

This study focuses on the development of an innovative safety jacket designed for horse riders, high-altitude workers, and particularly two- wheeler riders to mitigate the impact of accidents and enhance life-saving measures. Given the high incidence of road accidents in India, with 499,628 reported in 2010 leading to 134,513 fatalities and over 500,000 injuries, the need for improved safety mechanisms is critical. The proposed safety jacket integrates a micro controller-based system to detect falls and trigger an emergency response, inflating air cushions around the jacket to cushion impacts. Additionally, the system incorporates an alcohol sensor and a helmet detection sensor to ensure the bike starts only if the rider is sober and wearing a helmet. Visibility issues for riders are also addressed, with the jacket featuring turn indicators and other visibility enhancements to alert other road users. The jacket is designed to be water-resistant and washable, ensuring durability and ease of maintenance. The Bike Rider Safety System (BRSS) further enhances safety through real-time monitoring and adaptive response to potential hazards, using accelerometer, gyroscopes, and proximity sensors. The Arduino-based system comprises four main functionalities: helmet and alcohol detection, accident detection, airbag deployment, and live location transmission to authorities. In summary, the smart jacket aims to significantly reduce the risks faced by riders by incorporating advanced technology and a robust design to ensure their safety on the road.

Alzheimer's Patient Support System Based on IoT and ML

Alzheimer's disease poses significant challenges as it progressively erodes memory and identity, severely impacting daily functioning. Patients often experience disorientation, wandering, and are at risk of falls, leading to heightened concerns for caregivers. These difficulties can result in a loss of independence and increased caregiver burden. In response to these challenges, this study introduces an innovative assistive system designed to enhance the safety and quality of life for Alzheimer's patients. The system comprises of two main components: a smart arm band and a facial recognition system. The smart arm band is equipped with a suite of sensors including GPS, accelerometer, and heart rate sensor. These sensors enable real-time monitoring of the patient's location, movement, and physiological parameters. By leveraging these data streams, caregivers can track the patient's activities, detect falls or emergencies, and provide timely assistance when needed. The facial recognition system employs state-of-the-art machine learning techniques, specifically the CAFFE and Local Binary Patterns Histograms (LBPH), to recognize familiar faces in the patient's environment. This capability promotes social interaction and enhances the patient's sense of familiarity and security. Through rigorous testing, the facial recognition system achieves an impressive accuracy of 97% with a low error rate of 3%, validating its effectiveness in real-world scenarios. Overall, the integrative assistive system presented in this study offers a promising solution to address the multifaceted challenges associated with Alzheimer's disease. This system provides caregivers with invaluable support in ensuring the safety and well-being of Alzheimer's patients while fostering social engagement and autonomy.

Accident Prediction and Emergency Response System Using Accelerometer Sensor

Accidents, being unforeseeable events, can result in serious consequences, particularly when immediate medical attention is necessary. To address this issue, we propose the development of an Accident Prediction and Emergency Response System (APERS) that utilizes mobile accelerometer sensors. The APERS comprises two key components: a mobile application installed on users' smartphones and a web-based application for hospitals. The mobile application enables users to input and update their personal information and medical records, which are securely encrypted and stored. Moreover, the application continuously monitors accelerometer sensor data in real-time. Upon detecting a sudden spike in accelerometer readings indicative of an accident, the user's encrypted data and medical records are automatically transmitted to the hospital-side web application. On the hospital side, the web-based application receives and decrypts the user data, granting medical professionals access to vital information even if the user is unconscious. This facilitates timely and well-informed medical interventions, potentially saving lives. The system operates seamlessly, providing a robust solution for accident prediction and emergency response. The APERS offers numerous benefits, including proactive accident prediction, automated data sharing, and swift medical response. By leveraging mobile technology and accelerometer sensors, it addresses the pressing need for efficient emergency services in accident scenarios. Looking ahead, users could potentially share data while crossing the reception, further enhancing the system's effectiveness.

Sleep staging algorithm based on smartwatch sensors for healthy and sleep apnea populations

ObjectiveSleep stages can provide valuable insights into an individual's sleep quality. By leveraging movement and heart rate data collected by modern smartwatches, it is possible to enable the sleep staging feature and enhance users' understanding about their sleep and health conditions. MethodIn this paper, we present and validate a recurrent neural network based model with 23 input features extracted from accelerometer and photoplethysmography sensors data for both healthy and sleep apnea populations. We designed a lightweight and fast solution to enable the prediction of sleep stages for each 30-s epoch. This solution was developed using a large dataset of 1522 night recordings collected from a highly heterogeneous population and different versions of Samsung smartwatch. ResultsIn the classification of four sleep stages (wake, light, deep, and rapid eye movements sleep), the proposed solution achieved 71.6 % of balanced accuracy and a Cohen's kappa of 0.56 in a test set with 586 recordings. ConclusionThe results presented in this paper validate our proposal as a competitive wearable solution for sleep staging. Additionally, the use of a large and diverse data set contributes to the robustness of our solution, and corroborates the validation of algorithm's performance. Some additional analysis performed for healthy and sleep apnea population demonstrated that algorithm's performance has low correlation with demographic variables.

Effect of sensor number and location on accelerometry-based vertical ground reaction force estimation during walking.

Knee osteoarthritis is a major cause of global disability and is a major cost for the healthcare system. Lower extremity loading is a determinant of knee osteoarthritis onset and progression; however, technology that assists rehabilitative clinicians in optimizing key metrics of lower extremity loading is significantly limited. The peak vertical component of the ground reaction force (vGRF) in the first 50% of stance is highly associated with biological and patient-reported outcomes linked to knee osteoarthritis symptoms. Monitoring and maintaining typical vGRF profiles may support healthy gait biomechanics and joint tissue loading to prevent the onset and progression of knee osteoarthritis. Yet, the optimal number of sensors and sensor placements for predicting accurate vGRF from accelerometry remains unknown. Our goals were to: 1) determine how many sensors and what sensor locations yielded the most accurate vGRF loading peak estimates during walking; and 2) characterize how prescribing different loading conditions affected vGRF loading peak estimates. We asked 20 young adult participants to wear 5 accelerometers on their waist, shanks, and feet and walk on a force-instrumented treadmill during control and targeted biofeedback conditions prompting 5% underloading and overloading vGRFs. We trained and tested machine learning models to estimate vGRF from the various sensor accelerometer inputs and identified which combinations were most accurate. We found that a neural network using one accelerometer at the waist yielded the most accurate loading peak vGRF estimates during walking, with average errors of 4.4% body weight. The waist-only configuration was able to distinguish between control and overloading conditions prescribed using biofeedback, matching measured vGRF outcomes. Including foot or shank acceleration signals in the model reduced accuracy, particularly for the overloading condition. Our results suggest that a system designed to monitor changes in walking vGRF or to deploy targeted biofeedback may only need a single accelerometer located at the waist for healthy participants.

Reliable detection of generalized convulsive seizures using an off-the-shelf digital watch: A multisite phase 2 study.

The aim of this study was to develop a machine learning algorithm using an off-the-shelf digital watch, the Samsung watch (SM-R800), and evaluate its effectiveness for the detection of generalized convulsive seizures (GCS) in persons with epilepsy. This multisite epilepsy monitoring unit (EMU) phase 2 study included 36 adult patients. Each patient wore a Samsung watch that contained accelerometer, gyroscope, and photoplethysmographic sensors. Sixty-eight time and frequency domain features were extracted from the sensor data and were used to train a random forest algorithm. A testing framework was developed that would better reflect the EMU setting, consisting of (1) leave-one-patient-out cross-validation (LOPO CV) on GCS patients, (2) false alarm rate (FAR) testing on nonseizure patients, and (3) "fixed-and-frozen" prospective testing on a prospective patient cohort. Balanced accuracy, precision, sensitivity, and FAR were used to quantify the performance of the algorithm. Seizure onsets and offsets were determined by using video-electroencephalographic (EEG) monitoring. Feature importance was calculated as the mean decrease in Gini impurity during the LOPO CV testing. LOPO CV results showed balanced accuracy of .93 (95% confidence interval [CI] = .8-.98), precision of .68 (95% CI = .46-.85), sensitivity of .87 (95% CI = .62-.96), and FAR of .21/24 h (interquartile range [IQR] = 0-.90). Testing the algorithm on patients without seizure resulted in an FAR of .28/24 h (IQR = 0-.61). During the "fixed-and-frozen" prospective testing, two patients had three GCS, which were detected by the algorithm, while generating an FAR of .25/24 h (IQR = 0-.89). Feature importance showed that heart rate-based features outperformed accelerometer/gyroscope-based features. Commercially available wearable digital watches that reliably detect GCS, with minimum false alarm rates, may overcome usage adoption and other limitations of custom-built devices. Contingent on the outcomes of a prospective phase 3 study, such devices have the potential to provide non-EEG-based seizure surveillance and forecasting in the clinical setting.

Detecting Parkinson’s disease from shoe-mounted accelerometer sensors using convolutional neural networks optimized with modified metaheuristics

Neurodegenerative conditions significantly impact patient quality of life. Many conditions do not have a cure, but with appropriate and timely treatment the advance of the disease could be diminished. However, many patients only seek a diagnosis once the condition progresses to a point at which the quality of life is significantly impacted. Effective non-invasive and readily accessible methods for early diagnosis can considerably enhance the quality of life of patients affected by neurodegenerative conditions. This work explores the potential of convolutional neural networks (CNNs) for patient gain freezing associated with Parkinson’s disease. Sensor data collected from wearable gyroscopes located at the sole of the patient’s shoe record walking patterns. These patterns are further analyzed using convolutional networks to accurately detect abnormal walking patterns. The suggested method is assessed on a public real-world dataset collected from parents affected by Parkinson’s as well as individuals from a control group. To improve the accuracy of the classification, an altered variant of the recent crayfish optimization algorithm is introduced and compared to contemporary optimization metaheuristics. Our findings reveal that the modified algorithm (MSCHO) significantly outperforms other methods in accuracy, demonstrated by low error rates and high Cohen’s Kappa, precision, sensitivity, and F1-measures across three datasets. These results suggest the potential of CNNs, combined with advanced optimization techniques, for early, non-invasive diagnosis of neurodegenerative conditions, offering a path to improve patient quality of life.

Design of a Wireless Monitoring System for Vibration Characteristics of the Wheeled Tractor at Idle Speeds

To enhance the precision and efficiency of the tractor-failure-rate and equipment-quality inspection, the present study introduces a wireless rapid detection method for assessing tractor quality. For this study, which was based on the symmetrical structural characteristics of tractors, we designed a magnetic suction accelerometer. The test system was composed of a wireless router, a magnetic suction accelerometer sensor, a data-acquisition terminal, and other components. This test system aimed to test the equipment quality of the tractor at idle speed before leaving the factory. The experiment found that the vibration characteristics of the tractor had a symmetrical pattern on the left and right sides of the front and rear axle at idle. When the idle speed of the tractor was 800 r/min and 1000 r/min, the predominant vibration direction of both sides of the front axle of the tractor was the Y direction, while the predominant vibration direction of the rear axle was the Z direction. The experimental results showed that the proposed wireless rapid detection method of tractor quality and the designed acceleration sensor had good testing accuracy. The present study could provide a novel rapid detection method for the failure detection of power machinery in the agricultural field and for inspection before leaving the factory. The implementation of the method could improve the detection efficiency, and reduce the detection cost and the incidence of failure during actual use.

Riding smooth: A cost-benefit assessment of surface quality on Copenhagen’s bicycle network

A growing body of research acknowledges the substantial societal benefits of improved bicycle infrastructure, with recent studies highlighting the significant impact of road surface quality on comfort, safety, and accessibility of cycling. In this paper, we present a cost-benefit assessment of improved surface quality for the City of Copenhagen. The study focuses solely on improved accessibility through higher travel speed, where two large-scale crowdsourced bicycle trajectory datasets from Copenhagen are used, along with link-specific data for the network. It is found that travel time savings that result from investments in better surface, render a benefit-cost ratio of 2 to 4 and correspond to an absolute yearly recurrent welfare gain of around 20 mill. DKK for Copenhagen. Hence, from a societal welfare perspective, careful monitoring and maintenance of surface quality is indeed a good investment. It is also demonstrated that such monitoring can be achieved in a cost-effective manner by equipping cyclists who travel in a specific area with affordable accelerometer sensors.

Characterizing behavioral responses of Pacific cod to bottom trawl vessels and gear using archival tag accelerometer data

Fish behavior during the capture process affects the overall efficiency of survey vessels and gear, influencing selectivity and catchability, which in turn influences stock assessments. The effects of behavior on capture are typically investigated through experiments that use video or acoustic technologies to observe behavioral responses of fish to vessels and gear directly. While these approaches provide valuable information about fish behavior, their use is limited to observing the movement of fish inside a small observed area. In this proof-of-concept study, we demonstrate a novel application of bio-logging Pop-up Satellite Archival Tags (PSATs) equipped with accelerometer sensors to observe behavioral responses of free-swimming demersal fish to approaching bottom trawl fishing vessels and gear. By combining PSAT and fishing vessel geolocation data from the Vessel Monitoring System, we characterized diving and herding responses of three individual Pacific cod (Gadus macrocephalus) at-liberty for 21–106 days to trawling fishing vessels. We found that fish descended as trawlers approached, presumably in response to noise generated by the fishing vessel and gear, which supports the previously untested hypothesis that Pacific cod dive in response to approaching fishing vessels and gear, similar to closely related gadids. Additionally, one 88 cm fish swam ahead of a trawl towed at 1.25 m·s−1 (i.e., 1.42 body lengths per second) for 55.6 minutes, covering a distance of 4.17 km before capture, which challenges previous assertions that Pacific cod are unlikely to outswim bottom trawl gear towed at typical towing speeds. This study demonstrates that opportunistically collected archival tag data can be used to improve knowledge of behavioral interactions between fish and fishing gear.

Using a frequency vibration approach, examine the impact of screw rotor clearance on the screw housing in an 11kW compressor

A screw compressor is one of the industrial machines needed for productivity and use in today's production. Industrial machines generally use pneumatic systems with the help of compressed air supplied by a compressor machine. The compressor working system must receive very special attention. Maintenance and prevention of damage to air screw compressor machines is very necessary, especially maintenance and prevention of damage to rotary screw air compressors. Problems that often occur with the shaft will result in reduced air supply flow or unstable air pressure resulting in disruption of the production line. The aim of this research was to discuss the vibration values produced against the rotor clearance from the ideal output on male and female screw compressor rotors using the frequency analysis method, taking the vibration values at the screw compressor housing point using an Accelerometer sensor and a Fast Fourier Transform (FFT) tool. The analyzer rotates at 1500 rpm in the X, Y and Z axes at each predetermined point. Research shows that the clearance is set at 0.03, 0.05 and 0.08. In this research, the high frequency sample values of 1x 25 Hz, 2x 50 Hz, and 3x 75 Hz often appear in each vibration data of 0.03 and 0.05, at the vibration frequency range value of 0.08 none appear. This shows the results that the ideal rotor clearance value is at a distance of 0.08 because there is no frequency graph of 1x, 2x, and 3x rpm at each point and axis on the end housing of the screw rotor compressor

Investigation of harvesting energy vibrations due to the feed process on milling machines

Milling machines are machines used in the production process. The time required during the machining process must be more efficient in order to obtain the desired production capacity. The aim of this paper is to utilize vibrations originating from milling machines in industry as a source of Harvesting Energy Vibration. The research was carried out by varying the feeding depth of the milling machine so that it could be seen how much stress was produced. The Lushan Model ZX32 milling machine was used in this research, where vibrations from the vibration source were measured using an FFT Analyzer connected to an Accelerometer Sensor and Harvesting energy vibration connected to an Oscilloscope. Meanwhile, the workpieces used in this research were Aluminum 5052 and PVC. The results of the research were that on PVC material with an ingestion depth of 1 mm, the vibration frequency at 210 rpm was 71 Hz, the amplitude was 1.38 mm/s2, resulting in a harvesting energy of 6.449 mV. And the results of research using aluminum at 210 rpm rotation with a feeding depth of 3 mm obtained a vibration frequency of 90 Hz with an amplitude of 1.508 mm/s2 producing a harvesting energy of 5.856 mV. The research results show that the PVC material produces higher harvesting energy vibration at a depth of 1 mm, and the aluminum material produces higher harvesting energy vibration at a depth of 3 mm

Landslide Update and Conflict Avoidance System for Vehicles in Deep Curves

The advancements of technology in artificial intelligence and in this project plays a major role for the purpose of natural disasters like landslides. Landslides are a catastrophic disaster worldwide that alter from terrain to terrain. This paper is an attempt with regard to the expansion of a landslide susceptible approach by using accelerometer sensor and rain sensor is used for detecting heavy rainfall. Landslide is one of the hazardous and critical geographical process, which damages to civil infrastructure and property as well as causes loss of life. Upon detecting the landslide condition or heavy rains it warns on display as a message and closes gates on either sides of ghat till road condition gets normal, in this situation the motor operated gates are fixed upon each sides for free passage of vehicles from one side to other side. And, a siren buzzer that alerts the civilians in hilly and proned regions, this might save thousands of lives. Also, this project aims to decrease the number of accidents that occur on curved roadways. To do this, a warning is displayed as a vehicle approaches from the other side of the bend serves as a message to the driver.

Development of a smart lumbar posture correction vest

Lack of proper lumbar posture is responsible for the occurrence of neck and shoulder pain, low back pain, and muscle and joint injuries in the abdominal region in more than 75% of individuals. This scenario is influenced by the excessive use of computers in the workplace, prolonged periods of incorrect sitting posture, inadequate posture during daily activities, and repetitive and localized movements, among other factors. Consequently, numerous studies are being carried out with the aim of reducing or eliminating the frequency of the problem. The most common solution proposed by researchers is the use of lumbar posture correction waistcoats. This article has automated a posture correction waistcoat that protects the wearer but also monitors their posture. When incorrect posture is identified, the waistcoat generates a warning signal. To do this, gyro sensors, accelerometer sensors, and vibration motors were installed and supervised by an Arduino microcontroller. A communication system based on Bluetooth technology was used, integrated into an Android app that interacts with the user, displaying monitoring data and providing graphical representations of inappropriate postures. In addition, a health and well-being module was implemented, which suggests a physical exercise routine to prevent back pain. In addition, a therapeutic massage module was included for relaxation and an interface for storing music that can be played while wearing the waistcoat. The results demonstrated that the implemented system generated a smart vest capable of monitoring and alerting the user when an incorrect posture was identified. Furthermore, the results indicated that the health and well-being module provided a positive user experience.

Design an Earthquake Early Warning System Based on Arduino Uno Microcontroller with Accelerometer-MPU6050 sensor and NodeMCU-ESP8266

Geographically, Indonesia is located in three rings of active plates and surrounded by various active mountains that make Indonesia prone to earthquakes. When the earthquake occurred, there were still many people who were late in getting information to carry out the early evacuation process. This research aims to provide solutions to people who are late in receiving earthquake information by developing a microcontroller-based earthquake early warning system alarm with MPU6050 accelerometer sensors and NodeMCU ESP8266 to minimize possible losses. This research uses the Research and Development method with the ADDIE development model, to develop an Arduino uno-based earthquake alarm from previous research. This research uses a literature study system for analysis of earthquake problems when an earthquake occurs. The results of this study are in the form of a tool equipped with an Arduino Uno microcontroller system, where the earthquake warning system is run according to the system design. The tools developed were validated by three expert lecturers using validation questionnaires. Data analysis was carried out quantitatively to determine the feasibility of Arduino-based earthquake early warning tools using the Likert scale. Based on the average results of validation tests by experts, 91% of earthquake early warning tools developed are in the very decent category. Based on the trials carried out, the tool can provide warnings to the public directly