Accelerometer Research Articles

Identifying Gravity-Related Artifacts on Ballistocardiography Signals by Comparing Weightlessness and Normal Gravity Recordings (ARTIFACTS): Protocol for an Observational Study.

Modern ballistocardiography (BCG) and seismocardiography (SCG) use acceleration sensors to measure oscillating recoil movements of the body caused by the heartbeat and blood flow, which are transmitted to the body surface. Acceleration artifacts occur through intrinsic sensor roll, pitch, and yaw movements, assessed by the angular velocities of the respective sensor, during measurements that bias the signal interpretation. This observational study aims to generate hypotheses on the detection and elimination of acceleration artifacts due to the intrinsic rotation of accelerometers and their differentiation from heart-induced sensor accelerations. Multimodal data from 4 healthy participants (3 male and 1 female) using BCG-SCG and an electrocardiogram will be collected and serve as a basis for signal characterization, model modulation, and location vector derivation under parabolic flight conditions from µg to 1.8g. The data will be obtained during a parabolic flight campaign (3 times 30 parabolas) between September 24 and July 25 (depending on the flight schedule). To detect the described acceleration artifacts, accelerometers and gyroscopes (6-degree-of-freedom sensors) will be used for measuring acceleration and angular velocities attributed to intrinsic sensor rotation. Changes in acceleration and angular velocities will be explored by conducting descriptive data analysis of resting participants sitting upright in varying gravitational states. A multimodal data set will serve as a basis for research into a noninvasive and gentle method of BCG-SCG with the aid of low-noise and synchronous 3D gyroscopes and 3D acceleration sensors. Hypotheses will be generated related to detecting and eliminating acceleration artifacts due to the intrinsic rotation of accelerometers and gyroscopes (6-degree-of-freedom sensors) and their differentiation from heart-induced sensor accelerations. Data will be collected entirely and exclusively during the parabolic flights, taking place between September 2024 and July 2025. Thus, as of June 2024, no data have been collected yet. The data will be analyzed until December 2025. The results are expected to be published by June 2026. The study will contribute to understanding artificial acceleration bias to signal readings. It will be a first approach for a detection and elimination method. Deutsches Register Klinische Studien DRKS00034402; https://drks.de/search/en/trial/DRKS00034402. PRR1-10.2196/63306.

Vibration disturbance compensation in in situ confocal microscopy.

In situ microscopic measurement, conducted within the natural environment of a material or device, offers precise observations directly at the sample location, mitigating potential damage or deformation during transport. However, the inherent vibration of microscopic measurement equipment can introduce blurring and distortion to images, compromising measurement accuracy. This study proposes employing an acceleration sensor to detect microprobe vibrations and subsequently calculates three-dimensional coordinate displacements to compensate for measurement discrepancies. This approach can diminish the adverse effects of vibration on measurement outcomes within the order of hundreds of nanometers. Experimental results demonstrated the efficacy of this method in mitigating vibration artifact stripes or irregularities with a displacement amplitude I = sinc2[a(z - b)] ranging from ∼0.2 to 5.2 μm and a frequency spanning ∼7.9-18.8 Hz. Moreover, the lateral resolution of the probe attained 212 nm. Notably, the measurement error associated with the standard step height was decreased from 2.32 to 0.03 μm.

Picking up railway track vibration energy using a novel doughnut-shaped piezoelectric energy harvester

Railway transportation is essential for modern infrastructure, and ensuring safety through real-time monitoring of track conditions is critical. However, providing cost-effective and stable power for sensors in remote areas remains a challenge. This paper presents a novel doughnut-shaped piezoelectric energy harvester designed to address this issue. Under a sinusoidal excitation of 50N and 110Hz, the harvester achieves an output power of 0.047 mW, which increases linearly with load. At optimal resistance (230 kΩ) and a load of 3 kN at 7Hz, the output voltage reaches 37.61V, resulting in an output power of 7.23 mW. In track vibration conditions, the harvester produces an output power of 207.67 mW, making it suitable for Internet of Things (IoT) applications, such as powering acceleration and speed sensors to monitor train operations and ensure track safety.

A spatial six-dimensional force measurement method based on acceleration sensors

Accurate measurement of disturbance forces is an essential prerequisite for the simulation and analysis of micro-vibrations of space optical devices. Most of the current force measurement methods are based on force sensors, which have problems such as serious coupling between sensors, and the measurement accuracy is easily affected by the preload force. In order to solve these problems, a six-dimensional disturbance force measurement method based on acceleration sensors is proposed in this paper. First, the basic structure of the force measurement platform is introduced, and then the dynamics model of the platform is established and its force measurement principle is analyzed. Then the simulation analysis of the measurement accuracy of the platform is carried out by the Monte Carlo method. The analysis results show that the measurement accuracy of the disturbance force is proportional to the measurement accuracy of the acceleration sensor and the mass matrix recognition accuracy. When the errors of the acceleration sensor and mass matrix are within the normal range, the measurement error of the force measurement platform is less than 15%. Finally, a single-degree-of-freedom disturbance force measurement verification platform is built, and the test results show that the disturbance force is basically proportional to the acceleration of the platform when it is far from the resonance frequency, which verifies the correctness of the method to a certain extent. The measurement method is simple in structure and easy to implement, and can perform real-time measurement. Moreover, the measurement frequency is wide and the resolution is high, which is suitable for the measurement of disturbance force in the form of sinusoidal vibration or multi-frequency line spectrum.

Research on failure mechanism of landslide with retaining-wall-like locked segment and instability prediction by inverse velocity method

The locked segment is critical for determining the stability of locked segment-type landslides. Research indicates that the volume expansion point marks the transition from the secondary creep stage to the tertiary creep stage in a landslide’s evolution, and also separates the stable crack growth stage from the unstable crack growth stage in the locked segment. Identifying the volume expansion point is essential for early warning and predicting locked segment-type landslides. A series of instruments (resistance strain gauges, acoustic emission system, piezoelectric acceleration sensors, etc.) were used to conduct physical model tests of the landslide with retaining-wall-like locked segment under external load on the landslide’s trailing edge. The evolution process of this landslide was analyzed through changes in slope shape and stress response characteristics. The experimental results reveal the failure mechanism of the landslide with retaining-wall-like locked segment: the upper part of the landslide thrusts and slides, the middle part squeezes and uplifts, the retaining-wall-like locked segment produces a locking effect, and compression-shear fracture of the retaining-wall-like locked segment leads to landslide failure. Based on the deformation and acoustic emission characteristics of the locked segment, a method for identifying the volume expansion point was established. This point was used as the onset of acceleration point in the inverse velocity method to predict the failure time of the locked segment-type landslides, incorporating the three-stage creep model and Fukumoto’s theory.

Temporal localization of upper extremity bilateral synergistic coordination using wearable accelerometers.

The human upper extremity is characterized by inherent motor abundance, allowing a diverse array of tasks with agility and adaptability. Upper extremity functional limitations are a common sequela to Stroke, resulting in pronounced motor and sensory impairments in the contralesional arm. While many therapeutic interventions focus on rehabilitating the weaker arm, it is increasingly evident that it is necessary to consider bimanual coordination and motor control. Participants were recruited to two groups differing in age (Group 1 (n=10): 23.4 ±2.9 years, Group 2 (n=10): 55.9 ±10.6 years) for an exploratory study on the use of accelerometry to quantify bilateral coordination. Three tasks featuring coordinated reaching were selected to investigate the acceleration of the upper arm, forearm, and hand during activities of daily living (ADLs). Subjects were equipped with acceleration and inclination sensors on each upper arm, each forearm, and each hand. Data was segmented in MATLAB to assess inter-limb and intra-limb coordination. Inter-limb coordination was indicated through dissimilarity indices and temporal locations of congruous movement between upper arm, forearm, or hand segments of the right and left limbs. Intra-limb coordination was likewise assessed between upper arm-forearm, upper arm-hand, and forearm-hand segment pairs of the dominant limb. Acceleration data revealed task-specific movement features during the three distinct tasks. Groups demonstrated diminished similarity as task complexity increased. Groups differed significantly in the hand segments during the buttoning task, with Group 1 showing no coordination in the hand segments during buttoning, and strong coordination in reaching each button with the upper arm and forearm guiding extension. Group 2's dissimilarity scores and percentages of similarity indicated longer periods of inter-limb coordination, particularly towards movement completion. Group 1's dissimilarity scores and percentages of similarity indicated longer periods of intra-limb coordination, particularly in the coordination of the upper arm and forearm segments. The Expanding Procrustes methodology can be applied to compute objective coordination scores using accessible and highly accurate wearable acceleration sensors. The findings of task duration, angular velocity, and peak roll angle are supported by previous studies finding older individuals to present with slower movements, reduced movement stability, and a reduction of laterality between the limbs. The theory of a shift towards ambidexterity with age is supported by the finding of greater inter-limb coordination in the group of subjects above the age of thirty-five. The group below the age of thirty was found to demonstrate longer periods of intra-limb coordination, with upper arm and forearm coordination emerging as a possible explanation for the demonstrated greater stability.

Numerical study of steady-state dynamic characteristic of non-pneumatic tire with local structural damage

Non-pneumatic tires (NPTs) fundamentally avoid the risk of tire blowout of traditional pneumatic tires, but the overall and key component performance inevitably degrades due to factors such as high temperature, variable load, variable working conditions and impact in its service process. Once this leads to failure, it will significantly impact on vehicle safety. To this end, this paper studied the influence of local structural damage on the dynamic response of NPTs, which lays the foundation for realizing health monitoring of intelligent non-pneumatic tires (INPTs). Firstly, a three-dimensional nonlinear finite element model of NPTs was established, and the failure locations of NPTs were determined by fracture mechanics and maximum strain energy density; Secondly, the influence of structural damage on the static and dynamic performance of NPTs was analyzed; Finally, the sensitivity of the acceleration signal and sensor position of the tire inner liner to the local structural damage was studied. The research results show that structural damage will cause the stress of the spokes and shear layer to increase, and as the number of broken spokes increases, the shear layer will bear a larger load. Compared with the circumferential and lateral acceleration, the radial acceleration has the highest sensitivity to the damage of NPTs. The sensor closest to the damage location is the most sensitive to the damage. The research results provide a reference for the structural optimization design and health monitoring of INPTs.

Improving the technological process of balancing electric machine rotors on a balancing machine

The object of research is the process of dynamic balancing of rotors on a balancing machine in the process of restoring electric machines. The work is aimed at improving the quality of balancing rotors in the process of major repairs of traction electric motors for electric trains. The problem addressed was the quality of balancing the rotors of electric machines on stationary balancing machines. According to the conventional balancing technology, the rotor to be balanced is mounted on the supports of the balancing machine with support surfaces that usually have mechanical defects. These defects cannot be eliminated by machining due to the peculiarities of rotor repair technology. Theoretical and experimental studies of the effect of damage to the rotor support surfaces on the balancing parameters have been carried out. It has been proven that the properties of the bearing surfaces of the rotor during its balancing on a balancing machine significantly affect the results of determining the imbalance. At the same time, the difference in the mass values of balancing loads can reach 25 %. This is because damage to the bearing surfaces of the rotor generates false signals unrelated to the imbalance. In order to increase the accuracy of determining the mass of balancing loads during rotor balancing, it is proposed to improve the technological process of balancing. The improvement involves the inclusion of a frequency filter in the signal conversion chain of acceleration sensors. The filter is designed to separate signals with a frequency greater than the rotational frequency of the rotor. A condition for the practical application of the research results is the expediency of introducing a frequency filter of signals of acceleration sensors with a threshold frequency of filtering signals that exceeds the rotational frequency of the rotor into the schematic diagram of the balancing machine.

Measuring Vibrations of Subway Tunnel Structures with Cracks

In a study conducted in a metro tunnel, acceleration and displacement sensors were strategically placed along steel rails, track beds, and tunnel walls to capture real-time dynamic responses during train operations. Data were analyzed in the time and frequency domains, focusing on vibration levels and one-third octave bands. The results indicated that peak vibration acceleration significantly decreases from steel rails to tunnel walls, with different vibration frequencies observed at various locations: steel rails (200 Hz–1400 Hz), track beds, and tunnel walls (70 Hz–400 Hz). Cracks notably increase peak acceleration, vibration levels, peak frequency, and steel rail displacement but do not alter the overall vibration trends. Tunnel wall responses show the highest sensitivity to cracks, with a 300% increase in peak frequency, followed by track beds (100%) and steel rails (70%). Vibration levels under one-third octave band processing increased by 12.4% for tunnel walls, 8.8% for track beds, and 2.2% for steel rails. Cracks also caused steel rails’ vertical and lateral displacement to rise by 112% and 53%, respectively. These findings provide valuable insights for vibration reduction and crack repair in long-term subway operations.

Development of Simplified Methods for Levitation Force Distribution in Maglev Vehicles Using Frequency Ratio Tests.

Maglev vehicles apply the entire vehicle load uniformly onto bridges through levitation forces. In assessing the dynamic characteristics of the maglev train-bridge coupling system, it is reasonable to simplify the distributed levitation force as a concentrated force. This article theoretically derives the analytical response of bridge dynamics under the action of a single constant force and conducts numerical simulations for a moving single constant force and a series of equally spaced constant forces passing over simply supported beams and two-span continuous beams, respectively. The topic of discussion is the response of bridge dynamics when different degrees of force concentration are involved. High-precision displacement and acceleration sensors were utilized to conduct tests on the Shanghai maglev line to verify the accuracy of the simulation results. The results indicate that when simplifying the distributed levitation force into a concentrated force model, a frequency ratio can be used to analyze the conditions for resonance between the train and the bridge and to calculate the critical speed of the train; the levitation distribution force of a high-speed maglev vehicle can be simplified into four groups of concentrated forces based on the number of levitation frames to achieve sufficient accuracy, with the dynamic response of the bridge being close to that under distributed loads.

Online Handwriting Recognition Method with a Non-Inertial Reference Frame Based on the Measurement of Linear Accelerations and Differential Geometry: An Alternative to Quaternions.

This work describes a mathematical model for handwriting devices without a specific reference surface (SRS). The research was carried out on two hypotheses: the first considers possible circular segments that could be made during execution for the reconstruction of the trace, and the second is the combination of lines and circles. The proposed system has no flat reference surface, since the sensor is inside the pencil that describes the trace, not on the surface as in tablets or cell phones. An inertial sensor was used for the measurements, in this case, a commercial Micro-Electro Mechanical sensor of linear acceleration. The tracking device is an IMU sensor and a processing card that allows inertial measurements of the pen during on-the-fly tracing. It is essential to highlight that the system has a non-inertial reference frame. Comparing the two proposed models shows that it is possible to construct shapes from curved lines and that the patterns obtained are similar to what is recognized; this method provides an alternative to quaternion calculus for poorly specified orientation problems.

Integrated package and calibration of high-g MEMS ASIC acceleration sensor

To achieve the intelligentization and miniaturization goals of high-g acceleration sensors, this paper proposes a super high-g accelerometer using ASIC (Application-Specific Integrated Circuit) and MEMS (Micro-Electro-Mechanical Systems) integrated packaging. The sensor structure is a membrane island structure and the ASIC interface is manufactured using 0.35 µm CMOS (Complementary Metal-Oxide-Semiconductor) technology. Two prototypes were calibrated and the sensitivities are 0.206 µV/g and 0.337 µV/g. The package frequency of sensors is 74.5 kHz. The sensor range is 200,000 g, with an overload resistance of up to 250,000 g, the enhancement of overload resistance of sensors with integrated ASIC chips has been achieved. The resolution of the prototype is 371.10 g, which is 71.95 % higher compared to the sensor without integrated ASIC. The signal-to-noise ratio (SNR) is 72.363 dB, which has been improved by 24.73 %, verifying the effective improvement of resolution and SNR of the sensors with the addition of ASIC chips.

Recognizing salat activity using deep learning models via smartwatch sensors

In this study, we focus on human activity recognition, particularly aiming to distinguish the activity of praying (salat) from other daily activities. To achieve this goal, we have created a new dataset named HAR-P (Human activity recognition for Praying), which includes eight different activities: walking, running, sitting, standing, walking upstairs, walking downstairs, typing with a keyboard, and praying (salat). The HAR-P dataset was collected from 50 male individuals, who wore smartwatches on their dominant wrists. We compare the activity classification performance using three state-of-the-art algorithms from the literature: Long Short-Term Memory, Convolutional Long Short-Term Memory, and Convolutional Neural Network—Long Short-Term Memory. To assess the influence of sensors, data from accelerometer, gyroscope, linear acceleration sensor, and magnetic field sensor were utilized. The impact of individual sensor data as well as combinations thereof was investigated. The highest classification accuracy within single sensor groups, reaching 95.7%, was achieved using the accelerometer data with the Convolutional Long Short-Term Memory method. Combining two sensor groups resulted in an increase in accuracy of up to 9%. The highest accuracy of 96.4% was obtained by utilizing three sensor groups together with the Convolutional Neural Network—Long Short-Term Memory method. Furthermore, the evaluation of sensor and model performance was conducted using the stratified k-fold cross-validation method with 5-folds. These findings contribute significantly to evaluating the performance of sensor combinations and different algorithms in activity classification. This study may provide an effective foundation for the automatic recognition and tracking of human activities and offer an applicable model, particularly for the recognition of religious practices such as praying.

Multiple Self-Powered Sensor-Integrated Mobile Manipulator for Intelligent Environment Detection.

A multiple self-powered sensor-integrated mobile manipulator (MSIMM) system was proposed to address challenges in existing exploration devices, such as the need for a constant energy supply, limited variety of sensed information, and difficult human-computer interfaces. The MSIMM system integrates triboelectric nanogenerator (TENG)-based self-powered sensors, a bionic manipulator, and wireless gesture control, enhancing sensor data usability through machine learning. Specifically, the system includes a tracked vehicle platform carrying the manipulator and electronics, including a storage battery and a microcontroller unit (MCU). An integrated sensor glove and terminal application (APP) enable intuitive manipulator control, improving human-computer interaction. The system responds to and analyzes various environmental stimuli, including the droplet and fall height, temperature, pressure, material type, angles, angular velocity direction, and acceleration amplitude and direction. The manipulator, fabricated using 3D printing technology, integrates multiple sensors that generate electrical signals through the triboelectric effect of mechanical motion. These signals are classified using convolutional neural networks for accurate environmental monitoring. Our database shows signal recognition and classification accuracy exceeding 94%, with specific accuracies of 100% for pressure sensors, 99.55% for angle sensors, and 98.66, 95.91, 96.27, and 94.13% for material, droplet, temperature, and acceleration sensors, respectively.

Fault Diagnosis of Rolling Bearings in Agricultural Machines Using SVD-EDS-GST and ResViT

In the complex and harsh environment of agriculture, rolling bearings, as the key transmission components in agricultural machinery, are very prone to failure, so research on the intelligent fault diagnosis of agricultural machinery components is critical. Therefore, this paper proposes a new method based on SVD-EDS-GST and ResNet-Vision Transformer (ResViT) for the fault diagnosis of rolling bearings in agricultural machines. Firstly, an experimental platform for rolling bearing failure in agricultural machinery is built, and one-dimensional vibration signals are obtained using acceleration sensors. Next, the signal is preprocessed for noise reduction using singular value decomposition (SVD) combined with the energy difference spectrum (EDS) to solve for the interference of complex noise and redundant components in the vibration signal. Secondly, generalized S-transform (GST) is used to process vibration signals into images. Then, the ResViT model is proposed, where the ResNet34 network is used to replace the image chunking mechanism in the original Vision Transformer model for feature extraction. Finally, an improved Vision Transformer (ViT) is utilized to synthesize global and local information for fault classification. The experimental results show that the proposed method’s average accuracy in rolling bearing fault classification for agricultural machinery reaches 99.08%. In addition, compared with SVD-EDS-GST-CNN, SVD-EDS-GST-LSTM, STFT-ViT, GST-ViT, and SVD-EDS-GST-ViT, the accuracy rate was improved by 3.5%, 3.84%, 4.8%, 8.02%, and 0.56%, and the standard deviation was also minimized.

Application of digital transformation in real-time bridge monitoring systems: a case study with GNSS, accelerometer and IoT solution

Abstract Digital transformation is a term that has recently become more popular in all industries such as economics, financial fields, and engineering. The development of the Internet of Things, smart sensor systems, and cloud computing is the basis for forming effective solutions in construction monitoring from cyclic monitoring and post-processing to real-time monitoring and processing; from physical storage systems to virtual systems, and connecting every component in the project. This article presents a study and application of advanced devices including GNSS, acceleration sensors and IoT solutions in bridge construction monitoring. The research aims to apply smart devices and IoT to build real-time monitoring modules for bridge projects following the digital transformation trend. The study uses a GNSS N3 Comnav receiver with a sampling interval of 1Hz, an MPU 6050 acceleration sensor with a sampling frequency of 250 Hz, and an ESP Wi-Fi module. The result of the research is a real-time wireless monitoring system that operates effectively and economically and initially processes data with GNSS and high-frequency acceleration sensors.

Developing a multi-method approach to identifying e-scooter hazard hotspots

In recent years, e-scooters have been introduced in many European cities. In several places we have witnessed a rapid uptake of this new mode of transport mainly as a result of public sharing schemes. A number of incidents, injuries and even fatalities have given rise to questions regarding the safety of these vehicles. These questions are being researched mainly using official crash data and data specifying injuries and hospital treatment. Until now, the research has focused on investigating typical injury patterns and estimating risk levels. Very little is known about exactly where conflicts and crashes occur. Knowledge of hazard hotspots is crucial when investigating risk levels and improving safety for all road users.Hence, this paper develops an approach to investigating locations with potentially dangerous interactions within the active mobility system in the city of Berlin. The approach consists of explorative expert interviews, an online poll, and quantitative analyses. For the latter we combine three datasets. First, we research crash hotspots using official data. Second, we use data based on acceleration sensors from cyclists' smartphones to find locations of sudden movements. Third, we use trip data from the operators of e-scooter sharing systems. The information gathered is used in a conclusive expert workshop to identify hazard hotspots.Results show that many of the conflicts with pedestrians are caused by parked e-scooters. Second, e-scooter trips are concentrated in the inner city and along specific routes. In moving traffic, various data sources are used to identify hotspots at intersections and in areas between intersections.The present research lays the foundation for important further studies to investigate interactions at hotspots in detail by determining nine specific locations in the city of Berlin.

Determination of riding comfort on cycleways using a smartphone application

Well maintained cycleways will encourage more people to cycle, as the condition of cycleways is important for the safety, accessibility and riding comfort of cyclists. Despite that, only a few models used to describe the quality of service for cyclists take the surface condition into account. Objective measuring methods are needed to enable reliable and effective assessment of surface conditions, and measurable performance criteria related to the needs of cyclists should be developed. The purpose of this study has been to test the reliability and validity of using accelerometers in smartphones to assess the riding comfort on cycleways. A smartphone application converting three-dimensional accelerometer measurements into a single indicator for cycleways has been used to assess road surfaces in two field studies, in Sweden and Norway, respectively. Both studies assessed test sections of varying quality. To relate the measurements to subjective riding comfort assessments by cyclists, recruited cyclists collected quantitative data using the app, whilst also rating their perceived riding comfort by completing a survey. Measurements were also related to standard road surface condition indicators, generated from a road surface tester equipped with 19 laser sensors: international roughness index (IRI), mega- and macrotexture. The results show that it is possible to describe the unevenness of a cycleway using the technology present in smartphones. A software application can be used to collect and analyse data from the acceleration sensors in the phone, which can then be used to describe the riding comfort of cyclists. It is mainly the unevenness in the 50–1000 mm size-range that create the greatest discomfort for cyclists, and intermittent vibrations are perceived as more uncomfortable than more evenly distributed vibrations. Therefore, IRI is not a relevant measurement for describing the riding comfort of cyclists.

A novel fiber Bragg grating acceleration sensor based on the double-hinge structure

A novel fiber Bragg grating acceleration sensor based on the double-hinge structure

Detection of Total Hip Replacement Loosening Based on Structure-Borne Sound: Influence of the Position of the Sensor on the Hip Stem.

Accurate detection of implant loosening is crucial for early intervention in total hip replacements, but current imaging methods lack sensitivity and specificity. Vibration methods, already successful in dentistry, represent a promising approach. In order to detect loosening of the total hip replacement, excitation and measurement should be performed intracorporeally to minimize the influence of soft tissue on damping of the signals. However, only implants with a single sensor intracorporeally integrated into the implant for detecting vibrations have been presented in the literature. Considering different mode shapes, the sensor's position on the implant is assumed to influence the signals. In the work at hand, the influence of the position of the sensor on the recording of the vibrations on the implant was investigated. For this purpose, a simplified test setup was created with a titanium rod implanted in a cylinder of artificial cancellous bone. Mechanical stimulation via an exciter attached to the rod was recorded by three accelerometers at varying positions along the titanium rod. Three states of peri-implant loosening within the bone stock were simulated by extracting the bone material around the titanium rod, and different markers were analyzed to distinguish between these states of loosening. In addition, a modal analysis was performed using the finite element method to analyze the mode shapes. Distinct differences in the signals recorded by the acceleration sensors within defects highlight the influence of sensor position on mode detection and natural frequencies. Thus, using multiple sensors could be advantageous in accurately detecting all modes and determining the implant loosening state more precisely.