High-sensitivity acceleration sensor detecting micro-mechanomyogram and deep learning approach for parkinson's disease classification.

High-sensitivity acceleration sensors have been independently developed by our research group to detect vibrations that are > 10 dB smaller than those detected by conventional commercial sensors. This study is the first to measure high-frequency micro-vibrations in muscle fibers, termed micro-mechanomyogram (MMG) in patients with Parkinson's disease (PwPD) using a high-sensitivity acceleration sensor. We specifically measured the extensor pollicis brevis muscle at the base of the thumb in PwPD and healthy controls (HC) and detected not only low-frequency MMG (< 15 Hz) but also micro-MMG (≥ 15 Hz), which was preciously undetectable using commercial acceleration sensors. Analysis revealed remarkable differences in the frequency characteristics of micro-MMG between PwPD and HC. Specifically, during muscle power output, the low-frequency MMG energy was greater in PwPD than in HC, while the micro-MMG energy was smaller in PwPD compared to HC. These results suggest that micro-MMG detected by the high-sensitivity acceleration sensor provides crucial information for distinguishing between PwPD and HC. Moreover, a deep learning model trained on both low-frequency MMG and micro-MMG achieved a high accuracy (92.19%) in classifying PwPD and HC, demonstrating the potential for a diagnostic system for PwPD using micro-MMG.

A Curiosity Estimation in Storytelling with Picture Books for Children Using Wearable Sensors.

Storytelling is one of the most important learning activities for children since reading aloud from a picture book stimulates children's curiosity, emotional development, and imagination. For effective education, the procedures for storytelling activities need to be improved according to the children's level of curiosity. However, young children are not able to complete questionnaires, making it difficult to analyze their level of interest. This paper proposes a method to estimate children's curiosity in picture book reading activities at five levels by recognizing children's behavior using acceleration and angular velocity sensors placed on their heads. We investigated the relationship between children's behaviors and their levels of curiosity, listed all observed behaviors, and clarified the behavior for estimating curiosity. Furthermore, we conducted experiments using motion sensors to estimate these behaviors and confirmed that the accuracy of estimating curiosity from sensor data is approximately 72%.

Image-Acceleration Multimodal Danger Detection Model on Mobile Phone for Phone Addicts.

With the popularity of smartphones, a large number of "phubbers" have emerged who are engrossed in their phones regardless of the situation. In response to the potential dangers that phubbers face while traveling, this paper proposes a multimodal danger perception network model and early warning system for phubbers, designed for mobile devices. This proposed model consists of surrounding environment feature extraction, user behavior feature extraction, and multimodal feature fusion and recognition modules. The environmental feature module utilizes MobileNet as the backbone network to extract environmental description features from the rear-view image of the mobile phone. The behavior feature module uses acceleration time series as observation data, maps the acceleration observation data to a two-dimensional image space through GADFs (Gramian Angular Difference Fields), and extracts behavior description features through MobileNet, while utilizing statistical feature vectors to enhance the representation capability of behavioral features. Finally, in the recognition module, the environmental and behavioral characteristics are fused to output the type of hazardous state. Experiments indicate that the accuracy of the proposed model surpasses existing methods, and it possesses the advantages of compact model size (28.36 Mb) and fast execution speed (0.08 s), making it more suitable for deployment on mobile devices. Moreover, the developed image-acceleration multimodal phubber hazard recognition network combines the behavior of mobile phone users with surrounding environmental information, effectively identifying potential hazards for phubbers.

Self-Powered Acceleration Sensor for Distance Prediction via Triboelectrification.

Accurately predicting the distance an object will travel to its destination is very important in various sports. Acceleration sensors as a means of real-time monitoring are gaining increasing attention in sports. Due to the low energy output and power density of Triboelectric Nanogenerators (TENGs), recent efforts have focused on developing various acceleration sensors. However, these sensors suffer from significant drawbacks, including large size, high complexity, high power input requirements, and high cost. Here, we described a portable and cost-effective real-time refreshable strategy design comprising a series of individually addressable and controllable units based on TENGs embedded in a flexible substrate. This results in a highly sensitive, low-cost, and self-powered acceleration sensor. Putting, which accounts for nearly half of all strokes played, is obviously an important component of the golf game. The developed acceleration sensor has an accuracy controlled within 5%. The initial velocity and acceleration of the forward movement of a rolling golf ball after it is hit by a putter can be displayed, and the stopping distance is quickly calculated and predicted in about 7 s. This research demonstrates the application of the portable TENG-based acceleration sensor while paving the way for designing portable, cost-effective, scalable, and harmless ubiquitous self-powered acceleration sensors.

A Low-Frequency Fiber Bragg Grating Acceleration Sensor Based on Spring Support and Symmetric Compensation Structure with Flexible Hinges.

To measure vibration signals, a low-frequency fiber Bragg grating (FBG) acceleration sensor featuring a flexible hinge with a spring support and symmetric compensation structure has been designed. Based on the mechanical model of the sensor's structure, the expressions for sensitivity and resonant frequency of the sensor are derived. The structural parameters of the sensor are optimized, and a simulation analysis is conducted using ANSYS 19.2 software. According to the results of simulation analysis and size optimization, the sensor prototype is constructed. Subsequently, its amplitude-frequency response, sensitivity, and temperature characteristics are investigated through vibration experiments. The experimental results show that the resonant frequency of the sensor is 73 Hz, the operating frequency range is 0~60 Hz, and the sensitivity measures 24.24 pm/g. This design meets the requirements for measuring vibration signals at low frequencies.

Fabrication and Characterization of Monolithic Integrated Three-Axis Acceleration/Pressure/Magnetic Field Sensors.

In order to realize the measurement of three-axis acceleration, pressure, and magnetic field, monolithic integrated three-axis acceleration/pressure/magnetic field sensors are proposed in this paper. The proposed sensors were constructed with an acceleration sensor consisting of four L-shaped double beams, two masses, middle double-beams, and twelve piezoresistors, a pressure sensor made of a square silicon membrane, and four piezoresistors, as well as a magnetic field sensor composed of five Hall elements. COMSOL software and TCAD-Atlas software were used to simulate characteristics of integrated sensors, and analyze the working principles of the sensors in measuring acceleration, pressure, and magnetic field. The integrated sensors were fabricated by using micro-electro-mechanical systems (MEMS) technology and packaged by using inner lead bonding technology. When applying a working voltage of 5 V at room temperature, it is possible for the proposed sensors to achieve the acceleration sensitivities of 3.58 mV/g, 2.68 mV/g, and 9.45 mV/g along the x-axis, y-axis, and z-axis (through an amplifying circuit), and the sensitivities towards pressure and magnetic field are 0.28 mV/kPa and 22.44 mV/T, respectively. It is shown that the proposed sensors can measure three-axis acceleration, pressure, and magnetic field.

Development of a Dog Health Score Using an Artificial Intelligence Disease Prediction Algorithm Based on Multifaceted Data.

Detecting aberrant behaviors in dogs or observing emotional interactions between a dog and its owner may serve as indicators of potential canine diseases. However, dog owners typically struggle to assess or predict the health status of their pets independently. Consequently, there is a demand for a methodology enabling owners to evaluate their dogs' health based on everyday behavioral data. To address this need, we gathered individual canine data, including three months of standard daily activities (such as scratching, licking, swallowing, and sleeping), to train an AI model. This model identifies abnormal behaviors and quantifies each behavior as a numerical score, termed the "Health Score". This score is categorized into ten levels, where a higher score indicates a healthier state. Scores below 5 warrant medical consultation, while those above 5 are deemed healthy. We validated the baseline value of the Health Score against veterinarian diagnoses, achieving an 87.5% concordance rate. This validation confirms the reliability of the Health Score, which assesses canine health through daily activity monitoring, and is expected to significantly benefit dog owners who face challenges in determining the health status of their pets.

A gait stability evaluation method based on wearable acceleration sensors.

In this study, an accurate tool is provided for the evaluation of the effect of joint motion effect on gait stability. This quantitative gait evaluation method relies exclusively on the analysis of data acquired using acceleration sensors. First, the acceleration signal of lower limb motion is collected dynamically in real-time through the acceleration sensor. Second, an algorithm based on improved dynamic time warping (DTW) is proposed and used to calculate the gait stability index of the lower limbs. Finally, the effects of different joint braces on gait stability are analyzed. The experimental results show that the joint brace at the ankle and the knee reduces the range of motions of both ankle and knee joints, and a certain impact is exerted on the gait stability. In comparison to the ankle joint brace, the knee joint brace inflicts increased disturbance on the gait stability. Compared to the joint motion of the braced side, which showed a large deviation, the joint motion of the unbraced side was more similar to that of the normal walking process. In this paper, the quantitative evaluation algorithm based on DTW makes the results more intuitive and has potential application value in the evaluation of lower limb dysfunction, clinical training and rehabilitation.

Development of an Intelligent Wind Erosion Monitoring System.

Wind erosion monitoring is an important method for measuring soil erosion and desertification. However, the current wind erosion monitoring equipment has the disadvantages of low automation and low measurement accuracy. In this work, an intelligent wind erosion monitoring system is developed, which can automatically collect and upload information on sand and the environment. The structure of the mechanical parts is designed and optimized to reduce the measurement error caused by the windblown sand impact on the sample collection to improve the stability of the system. A specific scheme for the precision calibration of the load cell is developed and implemented. The jitter parameters of the load cell were determined using the JY61 six-axis acceleration sensor, and then the optimal scheme to eliminate the jitter error was determined by comparing two neural network models in MATLAB 2021a software, and the precision calibration of the load cell was completed. As a result, the system has a reliable mechanical structure and hardware system and a perfect error compensation processing scheme. In a certain period, the system can be fully automatic with stable operation. The field operation test of this system can meet the design requirements and improve the measurement accuracy of windblown sand wells.

Experimental Study of Wheel-to-Rail Interaction Using Acceleration Sensors for Continuous Rail Transport Comfort Evaluation.

Rail transport comfort is ensured by predictive maintenance and continuous supervision of rail quality. Besides the specialized equipment, the authors are proposing a simple system that can be implemented on operational wagons while in service, aiming to detect irregularities in the rail and report them using the train's online communication lines. The sensor itself is an acceleration sensor connected to an electronic microcontroller able to filter the inrush acceleration and send it to the diagnosis system of the wagon. This paper presents a study of real data recorded of the transversal and vertical vibrations of a standard tank wagon, measured on 2 axles and the car body, followed by the interpretation of the recorded data.

Advancements in Buoy Wave Data Processing through the Application of the Sage-Husa Adaptive Kalman Filtering Algorithm.

In this paper, we propose a combined filtering method rooted in the application of the Sage-Husa Adaptive Kalman filtering, designed specifically to process wave sensor data. This methodology aims to boost the measurement precision and real-time performance of wave parameters. (1) This study delineates the basic principles of the Kalman filter. (2) We discuss in detail the methodology for analyzing wave parameters from the collected wave acceleration data, and deeply study the key issues that may arise during this process. (3) To evaluate the efficacy of the Kalman filter, we have designed a simulation comparison encompassing various filtering algorithms. The results show that the Sage-Husa Adaptive Kalman Composite filter demonstrates superior performance in processing wave sensor data. (4) Additionally, in Chapter 5, we designed a turntable experiment capable of simulating the sinusoidal motion of waves and carried out a detailed errors analysis associated with the Kalman filter, to facilitate a deep understanding of potential problems that may be encountered in practical application, and their solutions. (5) Finally, the results reveal that the Sage-Husa Adaptive Kalman Composite filter improved the accuracy of effective wave height by 48.72% and the precision of effective wave period by 23.33% compared to traditional bandpass filter results.

Structural Design of MEMS Acceleration Sensor Based on PZT Plate Capacitance Detection.

The problem that the fuze overload signal sticks and is not easily identified by the counting layer during the high-speed intrusion of the projectile is an important factor affecting the explosion of the projectile in the specified layer. A three-pole plate dual-capacitance acceleration sensor based on the capacitive sensor principle is constructed in this paper. The modal simulation of the sensor structure is carried out using COMSOL 6.1 simulation software, the structural parameters of the sensor are derived from the mechanical properties of the model, and finally the physical sensor is processed and fabricated using the derived structural parameters. The mechanical impact characteristics of the model under different overloads were investigated using ANSYS/LS-DYNA, and the numerical simulation of the projectile intrusion into the three-layer concrete slab was carried out using LS-DYNA. Under different overload conditions, the sensor was tested using the Machette's hammer test and the output signal of the sensor was obtained. The output signal was analyzed. Finally, a sensor with self-powered output, high output voltage amplitude, and low spurious interference was obtained. The results show that the ceramic capacitive sensor has a reasonable structure, can reliably receive vibration signals, and has certain engineering applications in the intrusion meter layer.

Factors modulating home range and resource use: a case study with Canarian houbara bustards.

BACKGROUND: The home range of an animal is determined by its ecological requirements, and these may vary depending on many intrinsic and extrinsic factors, which are ultimately driven by food resources. Investigating the effects of these factors, and specifically how individuals use food resources within their home ranges is essential to understand the ecology and dynamics of animal populations, and to establish conservation measures in the case of endangered species. Here, we investigate these questions in the Canarian houbara bustard, an endangered subspecies of African houbara endemic to the Canary Islands. METHODS: We analysed GPS locations of 43 houbaras in 2018-2021, using solar GSM/GPRS loggers provided with accelerometers. We assessed (1) the variation in their home range and core area with kernel density estimators in relation to several intrinsic and extrinsic factors and (2) their foraging habitat selection. RESULTS: Home ranges were smallest during the breeding season (November-April), when rains triggered a rapid growth of herbaceous vegetation. Displaying males and nesting females had smaller home ranges than individuals not involved in reproduction. Both sexes used almost exclusively non-cultivated land, selecting low density Launaea arborescens shrublands, pastures and green fallows as foraging habitats. Heavier males used smaller home ranges because they spent more time displaying at a fixed display site, while heavier females moved over larger areas during the mating period, probably visiting more candidate mates. During the non-breeding season (May-October), both sexes showed larger home ranges, shifting to high density shrubland, but also partly to cultivated land. They selected sweet potato fields, green fallows, alfalfas, orchards and irrigated fields, which offered highly valuable food resources during the driest months of the year. CONCLUSIONS: Our study shows how Canarian houbara, originally a desert-dwelling species that uses mostly shrublands and pastures, has developed the necessary adaptations to benefit from resources provided by current low intensity farming practices in the study area. Maintaining appropriate habitat conditions in the eastern Canary islands should constitute a key conservation measure to prevent the extinction of this endangered houbara subspecies.

Feature Selection Model Based on IWOA for Behavior Identification of Chicken.

In order to reduce the influence of redundant features on the performance of the model in the process of accelerometer behavior recognition, and to improve the recognition accuracy of the model, this paper proposes an improved Whale Optimization algorithm with mixed strategy (IWOA) combined with the extreme gradient boosting algorithm (XGBoost) as a preferred method for chicken behavior identification features. A nine-axis inertial sensor was used to obtain the chicken behavior data. After noise reduction, the sliding window was used to extract 44 dimensional features in the time domain and frequency domain. To improve the search ability of the Whale Optimization algorithm for optimal solutions, the introduction of the good point set improves population diversity and expands the search range; the introduction of adaptive weight balances the search ability of the optimal solution in the early and late stages; the introduction of dimension-by-dimension lens imaging learning based on the adaptive weight factor perturbs the optimal solution and enhances the ability to jump out of the local optimal solution. This method's effectiveness was verified by recognizing cage breeders' feeding and drinking behaviors. The results show that the number of feature dimensions is reduced by 72.73%. At the same time, the behavior recognition accuracy is increased by 2.41% compared with the original behavior feature dataset, which is 95.58%. Compared with other dimensionality reduction methods, the IWOA-XGBoost model proposed in this paper has the highest recognition accuracy. The dimension reduction results have a certain degree of universality for different classification algorithms. This provides a method for behavior recognition based on acceleration sensor data.

A Wearable Flexible Acceleration Sensor for Monitoring Human Motion.

Skin-inspired flexible wearable acceleration sensors attract much attention due to their advantages of portability, personalized and comfortable experience, and potential application in healthcare monitoring, human-machine interfaces, artificial intelligence, and physical sports performance evaluation. This paper presents a flexible wearable acceleration sensor for monitoring human motion by introducing the island-bridge configuration and serpentine interconnects. Compared with traditional wearable accelerometers, the flexible accelerometer proposed in this paper improves the wearing comfort while reducing the cost of the device. Simulation and experiments under bending, stretching, and torsion conditions demonstrate that the flexible performance of the flexible acceleration sensor can meet the needs of monitoring the daily movement of the human body, and it can work normally under various conditions. The measurement accuracy of the flexible acceleration sensor is verified by comparing it with the data of the commercial acceleration sensor. The flexible acceleration sensor can measure the acceleration and the angular velocity of the human body with six degrees of freedom and recognize the gesture and motion features according to the acceleration characteristics. The presented flexible accelerometers provide great potential in recognizing the motion features that are critical for healthcare monitoring and physical sports performance evaluation.

Measuring Setup for Experimental Research of Two-Coordinate Fiber-Optic Acceleration Sensors with Cylindrical Lenses.

The article presents the possibilities of using fiber-optic acceleration (FOC) sensors on products of rocket-space and aviation technology as part of information-measuring systems. A special measuring device has been developed for experimental confirmation of the main characteristics of the technical characteristics of the developed, two-coordinate fiber-optic acceleration sensors. The developed measuring setup for the experimental research of a two-coordinate fiber-optic acceleration sensor with two, cylindrical lenses fixed on two H-shaped elastic elements deflected under the influence of acceleration in two mutually perpendicular directions X and Y, intended for operation in harsh conditions of rocket and space technology. The experimental equipment consists of the developed setup for setting micromovements and an information conversion unit, including modules for signal conversion, transmission, power supply, signal amplification, and indication. Experimental dependences of the output voltage from the information conversion unit's output on the micro-displacement in the range corresponding to the micro-displacements of the inertial mass with a cylindrical lens under acceleration in the range of ±100 m/s2 were obtained on the micro-displacement setting unit. The maximum value of the linearity error of the prototype acceleration sensor together with the information conversion unit was 0.07%. The conversion sensitivity of a two-coordinate fiber-optic acceleration sensor per the experimental dependences obtained on the Data Physics LE-612 MST/DSA 10-40 k vibration stand when exposed to sinusoidal vibration with an acceleration amplitude from 2 to 10 g in the frequency range from 5 to 2560 Hz was, on average, 3 mV/m/s2. The conducted experimental research confirms the performance of experimental samples of fiber-optic acceleration sensors together with an information conversion unit, as well as the achievement of high metrological characteristics.

A Novel Portable Real-Time Low-Cost Sleep Apnea Monitoring System based on the Global System for Mobile Communications (GSM) Network.

BACKGROUND AND OBJECTIVE: Continuous monitoring of breathing activity plays a vital role in the detection of respiratory-based diseases (SA, COPD, etc.). Sleep Apnea (SA) is characterized by recurrent upper airway obstruction during sleep associated with arterial blood desaturation, sympathetic nervous system activation, and cardiovascular impairment. Untreated patients with SA have increased mortality rates compared to the general population. This study aims to design a remote monitoring system for sleep apnea to ensure patient safety and ease the workload of doctors in the Covid-19 era. METHODS: This study aims to design a remote monitoring system for sleep apnea to ensure patient safety and ease the workload of doctors. Our study focuses on a novel portable real-time low-cost sleep apnea monitoring system utilizing the GSM network (GSM Shield Sim900a). Proposed system is a remote monitoring and patient tracking system to detect the apnea event in real time, and to provide information of the sleep position, pulse, and respiratory and oxygen saturation to the medical specialists (SpO2) by establishing a direct contact. As soon as an abnormal condition is detected in the light of these parameters, the condition is reported (instant or in the form of short reports after sleep) to the patient relatives, the doctor's mobile telephone or to the emergency medical centers (EMCs) through a GSM network to handle the case depending on the patient's emergency condition. RESULTS: A study group was formed of six patients for monitoring apnea events (three males and three females) between the ages of 20 and 60. The patients in the study group have sleep apnea (SA) in different grades. All the apnea events were detected, and all the patients were successfully alerted. Also, the patient parameters were successfully sent to all patient relatives. Patients who could not get out of apnea were called through the CALL feature, and they were informed about their ongoing apnea event and told that intervention was necessary. The proposed system is tested on six patients. The beginning moment of apnea was successfully detected and the SMS/CALL feature was successfully activated without delay. During the testing, it has been observed that while some of the patients start breathing after the first SMS, some others needed the second or the third SMS. According to the measurement result, the maximum breathless time is 46 s among the patients, and a SMS is sent every 15 s. In addition, in cases where the patient was breathless for a long time, the CALL feature was actively sought from the relatives of the patient and enabled him to intervene. The proposed monitoring system could be used in both clinical and home settings. CONCLUSIONS: The monitoring of a patient in real time allows to intervene in any unexpected circumstances about the patient. The proposed work uses an acceleration sensor as a reliable method of the sleep apnea for monitoring and prevention. The developed device is more economical, comfortable, and convenient than existing systems not only for the patients but also for the doctors. The patients can easily use this device in their home environment, so which could yield a more comfortable, easy to use, cost-effective, and long-term breathing monitoring system for healthcare applications.

Effects of Touchscreen Media Use on Toddlers' Sleep: Insights from Longtime ECG Monitoring.

Wearable biomedical sensor technology enables reliable monitoring of physiological data, even in very young children. The purpose of the present study was to develop algorithms for gaining valid physiological indicators of sleep quality in toddlers, using data from an undisturbing and easy-to-use wearable device. The study further reports the application of this technique to the investigation of potential impacts of early touchscreen media use. Toddlers' touchscreen media use is of strong interest for parents, educators, and researchers. Mostly, negative effects of media use are assumed, among them, disturbances of sleep and impairments of learning and development. In 55 toddlers (32 girls, 23 boys; 27.4 ± 4.9 months; range: 16-37 months), ECG monitoring was conducted for a period of 30 (±3) h. Parents were asked about their children's touchscreen media use and they rated their children's sleep quality. The use of touchscreen media predicted the physiologically determined quality of sleep but not parent-reported sleep quality (such as sleep onset latency). Greater heart rate differences between restless sleep phases and restful sleep indicated poorer nighttime recovery in children with more frequent use of touchscreen media. The study demonstrates that the expert analysis of the ECG during sleep is a potent tool for the estimation of sleep quality in toddlers.

Theoretical, Numerical, and Experimental Study on the Identification of Subway Tunnel Structural Damage Based on the Moving Train Dynamic Response.

As an important part of urban rail transit, subway tunnels play an important role in alleviating traffic pressure in mega-cities. Identifying and locating damage to the tunnel structure as early as possible has important practical significance for maintaining the long-term safe operation of subway tunnels. Summarizing the current status and shortcomings of the structural health monitoring of subway tunnels, a very economical and effective monitoring program is proposed, which is to use the train vibration response to identify and locate the damage of the tunnel structure. Firstly, the control equation of vehicle-tunnel coupling vibration is established and its analytical solution is given as the theoretical basis of this paper. Then, a damage index based on the cumulative sum of wavelet packet energy change rate (TDISC) is proposed, and its process algorithm is given. Through the joint simulation of VI-Rail and ANSYS, a refined 3D train-tunnel coupled vibration model is established. In this model, different combined conditions of single damage and double damage verify the validity of the damage index. The effectiveness of this damage index was further verified through model tests, and the influence of vehicle speed and load on the algorithm was discussed. Numerical simulation and experimental results show that the TDISC can effectively locate the damage of the tunnel structure and has good robustness.

Self-Powered Acceleration Sensor Based on Multilayer Suspension Structure and TPU-RTV Film for Vibration Monitoring.

In this paper, we designed a triboelectric acceleration sensor with excellent multiple parameters. To more easily detect weak vibrations, the sensor was founded on a multilayer suspension structure. To effectively improve the electrical properties of the sensor, a surface roughening and internal doping friction film, which was refined with a room temperature vulcanized silicone rubber (RTV) and some thermoplastic polyurethanes (TPU) powder in a certain proportion, was integrated into the structure. It was found that the optimization of the RTV film increases the open circuit voltage and short circuit current of the triboelectric nanogenerator (TENG) by 223% and 227%, respectively. When the external vibration acceleration is less than 4 m/s2, the sensitivity and linearity are 1.996 V/(m/s2) and 0.999, respectively. Additionally, when it is in the range between 4 m/s2 and 15 m/s2, those are 23.082 V/(m/s2) and 0.975, respectively. Furthermore, the sensor was placed in a simulated truck vibration environment, and its self-powered monitoring ability validated by experiments in real time. The results show that the designed sensor has strong practical value in the field of monitoring mechanical vibration acceleration.