Search Results (86)

Rotator cuff tears are common in individuals over 40, and physical therapy is often prescribed post-surgery. However, access can be limited by cost, convenience, and insurance coverage. CuffLink is a telehealth rehabilitation system that integrates the Strengthening and Stabilization System mechanical exerciser with the interACTION mobile health platform. The system includes a triple-axis accelerometer (LSM6DSOX + LIS3MDL FeatherWing), a rotary encoder, a VL530X time-of-flight sensor, and two wearable BioMech Health IMUs to capture upper-limb motion. CuffLink is designed to facilitate controlled, home-based exercise while enabling clinicians to remotely monitor joint function. Concurrent validity and test–retest reliability were used to assess device accuracy and repeatability. The results showed moderate to good validity for shoulder rotation (ICC = 0.81), device rotation (ICC = 0.94), and linear tracking (from zero: ICC = 0.75 and RMSE = 2.41; from start: ICC = 0.88 and RMSE = 2.02) and good reliability (e.g., RMSEs as low as 1.66 cm), with greater consistency in linear tracking compared to angular measures. Shoulder rotation and abduction exhibited higher variability in both validity and reliability measures. Future improvements will focus on manufacturability, signal stability, and force sensing. CuffLink supports accessible, data-driven rehabilitation and holds promise for advancing digital health in orthopedic recovery. Full article

In order to meet the application requirements for high-precision and low-noise accelerometers in micro-vibration measurement and navigation fields, this paper presents the design and testing of an ultra-high-capacitance resolution capacitive readout circuit with attofarad-level precision. First, a differential charge amplifier circuit is employed for the first stage of capacitance detection. To suppress noise interference in the circuit, a frequency-domain modulation technique is utilized to mitigate low-frequency noise. Subsequently, a differential subtraction circuit is implemented to reduce common-mode noise. Additionally, an improved filtering circuit is designed to suppress noise interference in the final stage. The test results indicate that the designed circuit operates at a carrier frequency of 1 MHz, achieving a capacitance resolution of up to 0.103 aF/H${\mathrm{z}}^{1/2}$ and a noise floor of 25.6 $\mathsf{\mu }$g/H${\mathrm{z}}^{1/2}$, thereby meeting the requirements for high-precision and low-noise capacitance detection in MEMS accelerometers. Full article

The aims of this study were to analyze (1) the external match demands during a congested period (CP) (i.e., three games in eight days) and (2) the differences among games with two- or three-day intervals in professional futsal players. Eleven elite male futsal players were monitored during 15 official matches. Wearable accelerometers were used to record player load (PL), accelerations (ACC), decelerations (DEC), and changes of direction (COD) at different intensities (e.g., high, medium, and low) using two approaches (e.g., absolute and relative per minute). A linear mixed model and effect sizes (ESs) were used to analyze differences between matches and days of interval. Considering the external match load during CP, non-significant differences were found for all the variables (p = 0.108–0.995; ES: 0.01–0.40). Comparing the interval days between games, players had significantly higher DEC_HI (p = 0.030; ES: 0.48), COD_TOTAL (p = 0.028; ES: 0.33), COD_MED (p = 0.024; ES: 0.40), and COD_LOW (p = 0.038; ES: 0.31) following 3 days of interval between the games when compared with 2 days. However, when analyzed relative to effective time, non-significant differences were found. In summary, CPs seem to not affect the match external load, but players performed better in terms of DEC and COD following 3 days of interval when compared to 2 days when analyzed with absolute values. Full article

High-precision, low-power MEMS accelerometers are extensively utilized across civilian applications. Closed-loop accelerometers employing switched-capacitor (SC) circuit topologies offer notable advantages, including low power consumption, high signal-to-noise ratio (SNR), and excellent linearity. Addressing the critical demand for high-precision, low-power MEMS accelerometers in modern geophones, this work focuses on the design and implementation of closed-loop interface ASICs (Application-Specific Integrated Circuits). The proposed interface circuit, based on switched-capacitor modulation technology, incorporates a low-noise charge amplifier, sample-and-hold circuit, integrator, and clock divider circuit. To minimize average power consumption, a switched operational amplifier (op-amp) technique is adopted, which temporarily disconnects idle op-amps from the power supply. Additionally, a class-AB output stage is employed to enhance the dynamic range of the circuit. The design was realized using a standard 0.35 μm CMOS process, culminating in the completion of layout design and small-scale engineering fabrication. The performance of the MEMS accelerometers was evaluated under a 3.3 V power supply, achieving a power consumption of 3.3 mW, an accelerometer noise density below 1 μg/√Hz, a sensitivity of 1.65 V/g, a measurement range of ±1 g, a nonlinearity of 0.15%, a bandwidth of 300 Hz, and a bias stability of approximately 36 μg. These results demonstrate the efficacy of the proposed design in meeting the stringent requirements of high-precision MEMS accelerometer applications. Full article

According to South Korea’s Ministry of Employment and Labor, approximately 25,000 construction workers suffered from various injuries between 2015 and 2019. Additionally, about 500 fatalities occur annually, and multiple studies are being conducted to prevent these accidents and quickly identify their occurrence to secure the golden time for the injured. Recently, AI-based video analysis systems for detecting safety accidents have been introduced. However, these systems are limited to areas where CCTV is installed, and in locations like construction sites, numerous blind spots exist due to the limitations of CCTV coverage. To address this issue, there is active research on the use of MEMS (micro-electromechanical systems) sensors to detect abnormal conditions in workers. In particular, methods such as using accelerometers and gyroscopes within MEMS sensors to acquire data based on workers’ angles, utilizing three-axis accelerometers and barometric pressure sensors to improve the accuracy of fall detection systems, and measuring the wearer’s gait using the x-, y-, and z-axis data from accelerometers and gyroscopes are being studied. However, most methods involve use of MEMS sensors embedded in smartphones, typically attaching the sensors to one or two specific body parts. Therefore, in this study, we developed a novel miniaturized IMU (inertial measurement unit) sensor that can be simultaneously attached to multiple body parts of construction workers (head, body, hands, and legs). The sensor integrates accelerometers, gyroscopes, and barometric pressure sensors to measure various worker movements in real time (e.g., walking, jumping, standing, and working at heights). Additionally, incorporating PPG (photoplethysmography), body temperature, and acoustic sensors, enables the comprehensive observation of both physiological signals and environmental changes. The collected sensor data are preprocessed using Kalman and extended Kalman filters, among others, and an algorithm was proposed to evaluate workers’ safety status and update health-related data in real time. Experimental results demonstrated that the proposed IMU sensor can classify work activities with over 90% accuracy even at a low sampling rate of 15 Hz. Furthermore, by integrating internal filtering, communication modules, and server connectivity within an application, we established a cyber–physical system (CPS), enabling real-time monitoring and immediate alert transmission to safety managers. Through this approach, we verified improved performance in terms of miniaturization, measurement accuracy, and server integration compared to existing commercial sensors. Full article

Background: Hyperkinetic movement disorders involve excessive, involuntary movements such as ataxia, chorea, dystonia, myoclonus, tics, and tremor. Recent advances in artificial intelligence (AI) allow investigators to integrate multimodal instrumented movement measurements and imaging techniques and to analyze these data together at scale. In this systematic review, we aim to characterize AI’s performance in diagnosing and quantitatively phenotyping these disorders. Methods: We searched PubMed and Embase using a semi-automated article-screening pipeline. Results: Fifty-five studies met the inclusion criteria (n = 11,946 subjects). Thirty-five studies used machine learning, sixteen used deep learning, and four used both. Thirty-eight studies reported disease diagnosis, twenty-three reported quantitative phenotyping, and six reported both. Diagnostic accuracy was reported in 36 of 38 and correlation coefficients in 10 of 23 studies. Kinematics (e.g., accelerometers and inertial measurement units) were the most used dataset. Diagnostic accuracy was reported in 36 studies and ranged from 56 to 100% compared to clinical diagnoses to differentiate them from healthy controls. The correlation coefficient was reported in 10 studies and ranged from 0.54 to 0.99 compared to clinical ratings for quantitative phenotyping. Five studies had an overall judgment of “low risk of bias” and three had external validation. Conclusion: There is a need to adopt AI-based research guidelines to minimize reporting heterogeneity and bolster clinical interpretability. Full article

Shock and vibration hazards to civil structures are common and come not only from earthquakes but most often from mining operations or foundation work involving the installation of piles using hammer-driving and vibrating technology. The purpose of this study is to present test methods for low-cost MEMS accelerometers in terms of their selection for low-amplitude acceleration vibration-prone object-monitoring systems. Tests of 24 commercially available digital accelerometers were carried out on a custom-built test bench, selecting four models for detailed tests conducted on a specially built precision vibration table capable of inflicting accelerations at frequencies of 1–2 Hz, using displacements as small as a few micrometers. The analysis of the results was based, among other things, on a modified method of determining the signal-to-noise ratio (SNR) and also on the idea of the effective number of bits (ENOB). The results of the analysis showed that among low-cost MEMS accelerometers, there are some that are successfully suitable for the monitoring and warning of excessive vibration hazards in situations where objects are extremely sensitive to such impacts (e.g., treatment rooms in hospitals). Examples of accelerometers capable of detecting harmonic vibrations with amplitudes as small as 10 mm/s² or impulsive shocks with amplitudes of at least 70 mm/s² are indicated. Full article

Flooding and consequential scouring are the primary causes of bridge failures, making the detection of such events crucial for structural safety. This study investigates the characteristics of accelerometer data from bridge pier vibrations and proposes a flood detection method with deep learning-based models based on ResNet18 and 1D Convolution architectures. These models were comprehensively evaluated for (1) detecting vehicles passing on bridges and (2) detecting flood events based on axis-specific accelerometer data under various traffic conditions. Continuous Wavelet Transform (CWT) was employed to convert the accelerometer data into richer time-frequency representations, enhancing the detection of passing vehicles. Notably, when vehicles are passing over bridges, the vertical direction exhibits a magnified and more sustained energy distribution across a wider frequency range. Additionally, under flooding conditions, time-frequency representations from the bridge direction reveal a significant increase in energy intensity and continuity compared with non-flooding conditions. For detection of vehicles passing, ResNet18 outperformed the 1D Convolution model, achieving an accuracy of 97.2% compared with 91.4%. For flood detection without vehicles passing, the two models performed similarly well, with accuracies of 97.3% and 98.3%, respectively. However, in scenarios with vehicles passing, the 1D Convolution model excelled, achieving an accuracy of 98.6%, significantly higher than that of ResNet18 (81.6%). This suggests that high-frequency signals, such as vertical vibrations induced by passing vehicles, are better captured by more complex representations (CWT) and models (e.g., ResNet18), while relatively low-frequency signals, such as longitudinal vibrations caused by flooding, can be effectively captured by simpler 1D Convolution over the original signals. Consequentially, the two model types are deployed in a pipeline where the ResNet18 model is used for classifying whether vehicles are passing the bridge, followed by two 1D Convolution models: one trained for detecting flood events under vehicles-passing conditions and the other trained for detecting flood events under no-vehicles-passing conditions. This hierarchical approach provides a robust framework for real-time monitoring of bridge response to vehicle passing and timely warning of flood events, enhancing the potential to reduce bridge collapses and improve public safety. Full article

This article proposes a low cross-axis sensitivity resonant MEMS(Micro-Electro-Mechanical Systems) accelerometer that is optimized based on the BP and NSGA-II algorithms. When resonant accelerometers are used in seismic monitoring, automotive safety systems, and navigation applications, high immunity and low cross-axis sensitivity are required. To improve the high immunity of the accelerometer, a coupling structure is introduced. This structure effectively separates the symmetric and antisymmetric mode frequencies of the DETF resonator and prevents mode coupling. To obtain higher detection accuracy and low cross-axis sensitivity, a decoupling structure is introduced. To find the optimal dimensional parameters of the decoupled structure, the BP and NSGA-II algorithms are used to optimize the dimensional parameters of the decoupled structure. The optimized decoupled structure has an axial stiffness of 6032.21 N/m and a transverse stiffness of 6.29 N/m. The finite element analysis results show that the sensitivity of the accelerometer is 59.1 Hz/g (Y-axis) and 59 Hz/g (X-axis). Cross-axis sensitivity is 0.508% (Y-axis) and 0.339% (X-axis), which is significantly lower than most resonant accelerometers. The coupling structure and optimization method proposed in this paper provide a new solution for designing resonant accelerometers with high interference immunity and low cross-axis sensitivity. Full article

Climbing gyms aim to continuously improve their offerings and make the best use of their infrastructure to provide a unique experience for their clients, the climbers. One approach to achieve this goal is to track and analyze climbing sessions from the beginning of the ascent until the climber’s descent. Detecting the climber’s descent is crucial because it indicates when the ascent has ended. This paper discusses an approach that preserves climber privacy (e.g., not using cameras) while considering the convenience of climbers and the costs to the gyms. To this aim, a hardware prototype has been developed to collect data using accelerometer sensors attached to a piece of climbing equipment mounted on the wall, called a quickdraw, which connects the climbing rope to the bolt anchors. The sensors are configured to be energy-efficient, making them practical in terms of expenses and time required for replacement when used in large quantities in a climbing gym. This paper describes the hardware specifications, studies data measured by the sensors in ultra-low power mode, detects sensors’ orientation patterns during descent on different routes, and develops a supervised approach to identify lowering. Additionally, the study emphasizes the benefits of multidisciplinary feature engineering, combining domain-specific knowledge with machine learning to enhance performance and simplify implementation. Full article

This study examines how the 8-week Hoosier Sport program impacts cardiovascular disease (CVD) risks by promoting physical activity (PA) among rural, low-income children. Using a human-centered participatory co-design approach, the program aimed to increase PA levels (e.g., total PA, daily steps) in at-risk children. The present study explored the feasibility of the intervention as well as physiological and psychological changes across the intervention using a hybrid type 2 design (a model that evaluates both the effectiveness of an intervention and its implementation in real-world settings). Favorable feasibility indicators like attendance, acceptability, and compliance, with a 23.3% recruitment rate and 94.3% retention rate, were observed. Moreover, participants attended over 80% of sessions across the 8 weeks. Accelerometers (AX3) tracked daily steps and total PA for 7 days before and after the intervention, revealing increased PA levels throughout. At post-intervention, notable improvements were observed in psychological factors such as autonomy, social competence, and global self-worth. This study highlights the importance of tailored PA interventions in schools, emphasizing their potential to improve PA levels among rural, low-income children. Full article

This paper presents a micromachined thermal convective accelerometer with low power and high reliability. This accelerometer comprises a heater and two thermistors. The central heater elevates the temperature of the chip above ambient levels while the symmetrically arranged thermistors monitor the temperature differentials induced by acceleration. The heater and thermistors are fabricated on a glass substrate using a standard micro-electromechanical systems (MEMS) process flow, and the fabricated sensor is installed on a rotation platform and a shaking table experimental setup to perform the experiment. The results indicate that the sensor has the capability to measure accelerations surpassing 80 m/s², with an approximate linear sensitivity of 110.69 mV/g. This proposed thermal convective accelerometer offers promising potential for various applications requiring precise acceleration measurements in environments where low power consumption and high reliability are paramount. Full article

Different PMI foam materials of 52, 110, and 200 kg/m³ were used to design stepwise gradient cores to improve the impact resistance of the sandwich beam. The stepwise gradient core consists of three layers arranged in positive gradient, negative gradient, and sandwich-core (e.g., 200/52/200). These sandwich beams were subjected to the impact of a steel projectile under impact momentum of 10 to 20 kg·m/s, corresponding to impact energy in the range of 12.5 to 50 J. During the test, the impact force was recorded by an accelerometer, and the different failure modes were also obtained. Subsequently, the influence of the layer arrangement on the energy absorption and load transfer mechanism between the different layers was analyzed. The results showed that the top layer with a large density can improve the impact force, but the middle/bottom layer with a low density promoted specific energy absorption. Thus, based on these two points, the negative gradient core (200/110/52) had an excellent specific energy absorption because it can transfer and expand the area to bear the load layer by layer, which improved the energy absorption in each layer. Combined with the failure modes, the load transfer and deformation mechanisms between the layers were also discussed. The present work provided a valuable method to design an efficient lightweight sandwich structure in the protection field. Full article

While the availability of low-cost micro electro-mechanical systems (MEMS) accelerometers, gyroscopes, and magnetometers initially seemed to promise the possibility of using them to easily track the position and orientation of virtually any object that they could be attached to, this promise has not yet been fulfilled. Navigation-grade accelerometers and gyroscopes have long been the basis for tracking ships and aircraft, but the signals from low-cost MEMS accelerometers and gyroscopes are still orders of magnitude poorer in quality (e.g., bias stability). Therefore, the applications of MEMS inertial measurement units (IMUs), containing tri-axial accelerometers and gyroscopes, are currently not as extensive as they were expected to be. Even the addition of MEMS tri-axial magnetometers, to conform magnetic, angular rate, and gravity (MARG) sensor modules, has not fully overcome the challenges involved in using these modules for long-term orientation estimation, which would be of great benefit for the tracking of human–computer hand-held controllers or tracking of Internet-Of-Things (IoT) devices. Here, we present an algorithm, GMVDμK (or simply GMVDK), that aims at taking full advantage of all the signals available from a MARG module to robustly estimate its orientation, while preventing damaging overcorrections, within the context of a human–computer interaction application. Through experimental comparison, we show that GMVDK is more robust to magnetic disturbances than three other MARG orientation estimation algorithms in representative trials. Full article

Flight parameters are crucial criteria for UAV control, playing a significant role in ensuring the safe and efficient completion of missions. Launch force and airspeed information are key parameters in the early and middle stages of flight, serving as important data for monitoring the UAV’s flight status. In response to challenges such as weak launch force, low identification rates, small airspeed, and low recognition accuracy in UAVs, a method for identifying UAV flight parameters based on launch force and airspeed is proposed. From the aspect of launch force identification, a recognition method based on a low-g value accelerometer information source is proposed, utilizing a ‘multi-level time window + threshold’ approach. For airspeed identification, an optimization method for airspeed measurement under the Kalman filter architecture is introduced. A device for airspeed measurement based on pressure sensors is designed, and the recommended installation position is determined through simulation. Furthermore, the feasibility and robustness of the proposed launch force identification and airspeed measurement optimization methods are validated through simulation. Finally, the effectiveness of the design is verified through centrifuge and wind tunnel experiments. This research provides technical support for the identification of the launch force and airspeed measurement in UAVs. Full article