Accelerometer Research Articles

Experimental determination of damping coefficient of a support with elastic ring under harmonic loading

The article describes the possibility of determining the damping coefficient of a support with an elastic ring based on dry friction forces using a special test bench. The forced excitation force is set by the oscillations of a compact speaker installed on top of the oscillating mass through a U-shaped crossbar. The object of the study is a damper with an elastic ring, that is a thin-walled ring with evenly distributed bulges inside and outside, arranged in a checkerboard pattern. The damping coefficient is estimated using a device for simulating a rotor support, an acceleration sensor, an exciter speaker, a controller and a processing station. The amount of damping is estimated according to the width of the peak at the resonant frequency. A comparison is given of the magnitude of the damping coefficient obtained from the analysis of the resonance peak and from the decay rate during the impact experiment.

Model test study on the dynamic failure process of tunnel surrounding rocks in jointed rock mass under explosive load

The presence of joints can significantly reduce the integrity and stability of an engineering rock mass. Under dynamic loads, such as from blasting excavation, the key blocks divided by joints may be destabilized and prone to sliding, potentially leading to engineering geological disasters like rockbursts. To study the dynamic instability process, similar materials for the rock mass and joints were developed based on the similarity theory, and a tunnel model in the jointed rock mass was constructed. Subsequently, a detonating fuse was used to generate a dynamic load, and the dynamic instability process of the tunnel surrounding rock in the jointed rock mass under explosive load was studied using the geotechnical multifunctional testing device. The deformation characteristics and dynamic instability process of the tunnel surrounding rock were analyzed using acceleration sensors, resistance strain gages, linear variable displacement transducers and motion camera. The study shows that the acceleration at the tunnel vault is significantly greater than at the straight wall and floor under blast loads, with differences reaching an order of magnitude. Acceleration waveforms were classified into three categories based on peak characteristics, explained through the propagation of explosive stress waves. Additionally, strain and displacement at the tunnel arch were also significantly greater than in other areas, indicating more severe stress concentration and dynamic damage at the arch, necessitating reinforced support in tunnel excavation. The entire dynamic instability process of the tunnel surrounding rock was successfully recorded using a motion camera. The dynamic failure process was divided into several phases, the appearance of cracks on the joint surface, particle ejection accompanied by the dropping of jointed blocks, a significant drop of the jointed blocks, and return to calm. The dynamic failure modes include the dropping and rotation of jointed blocks, local particle ejection, and shear cracks on jointed block.

Identification of Disc Cutter Wear via Operation Parameters Combined With Vibration Data: A Case Study

ABSTRACTThis paper proposed an approach to estimate disc cutter wear utilizing a combination of multiple operational parameters and vibration data collected during shield tunneling operations. The incorporation of vibration signals, notably those originating from acceleration sensors mounted on the back plate of the soil chamber, has markedly enhanced the accuracy of the model. Time‐frequency domain features were extracted through analysis methods such as Fast Fourier Transform (FFT), Short‐Time Fourier Transform (STFT), and Continuous Wavelet Transform (CWT). A predictive model utilizing vibration and shield operation parameters was developed using the XGBoost algorithm, and a deep GoogLeNet Convolutional Neural Network (CNN) was trained on time‐frequency graphs from the CWT. In addition, this study also investigated the impact of signal duration on wavelet image information and model accuracy. In the Huang‐Shang Intercity Railway Project, the approach effectively assessed disc cutter wear during tunneling operations and dynamically optimized the operational parameters of the shield tunnel machine through predictive analysis.

Design of monitoring system for material vibration screening equipment

In order to effectively reduce the failure rate of vibrating screens, a vibration monitoring system design was proposed based on the structure and working principle. The system should be able to monitor the operating state of the vibrating screen in real time, including vibration amplitude, frequency, axial displacement, etc., and be able to detect abnormal conditions in time and give warning prompts. The key hardware of the monitoring system was designed, including acceleration sensor, power module, signal filtering circuit, etc. The sensitivity of the acceleration sensor was measured using comparative method and accurately completed parameter calibration. The eccentric block was used to simulate different deviation of excitation force for system vibration signal test and calibration. The results show that under normal operating conditions, the amplitude of each frequency of the vibrating screen is less than 0.04 g. Under the condition of excitation force deviation, the amplitude of 16 Hz is obviously lower than that of other frequencies, which means that the vibrating screen is currently experiencing lateral oscillation.

Development of Laminated Egg-Shaped Tsunami Shelter Structure Made of Steel-Cushioning-Steel

When a tsunami is caused by an earthquake or other event, spherical shelters are developed to protect people from the tsunami. This study proposes a new egg-shaped laminated tsunami shelter with a buffer layer to improve the functionality of traditional spherical shelters. The inner and outer shells of this shelter are made from thin-walled stainless steel, using the integral hydro bulge forming (IHBF) process. The space between these two layers was filled with urethane foam, providing an elastic buffer. This resulted in a laminated egg-shaped structure designed for tsunami protection. To verify the proposed laminated egg-shaped tsunami shelter and its processing method, an egg-shaped shell with an external shape (length 660 mm, width 493 mm) was fabricated using a 1.0 mm thick stainless plate, and a laminated egg-shaped tsunami shelter with a 25 mm thick intermediate layer made of urethane foam was fabricated. The shape accuracy of the processed egg-shaped laminated tsunami shelter structure was measured, and the maximum error between the surface shape of the molded egg-shaped shell and the true egg shape was -4.13 mm, and the relative error to the maximum radius of the egg shape of 246.5 mm was -1.68%. In addition, to assess the buffering effect under external impact loads, acceleration sensors were attached to both the inner and outer layers of the fabricated egg-shaped laminated tsunami shelters. A hammer was used to apply an impact load to the outer layer, and the response acceleration values recorded by the sensors on both layers were compared. It was found that the response acceleration of the inner layer was 15.81% lower than that of the outer layer.

Resonance Testing Data Evaluation Approaches for Scaling Onset Detection in Pipelines

Most industries dealing with pipelines face problems resulting from the buildup of deposits therein, such as reduced efficiency, downtime and increased maintenance costs. Although solutions to this issue have been sought for decades, no widely employed technique for monitoring growth of inorganic deposits (or ‘scaling’) in pipelines exists. In this research, a means of detecting the onset of scaling growth, by processing resonance testing data, was sought. For the resonance testing measurements the pipeline segment of interest is equipped with acceleration sensors which record signals generated by impacting the pipeline with a steel tip. The signals are Fourier transformed and analysed in the frequency domain, where a clear shift in frequency peak positions can be observed as the scaling thickness changes. How best to extract quantitative information from the generated frequency data is an open question. In this research, two data analysis approaches for scaling thickness prediction are compared: a supervised (binary classification) machine learning approach as well as a comparison-based change detection approach using cross-correlation. The supervised machine learning approach yields generalizable results for different acceleration sensors and impactor diameters whilst the change detection approach is sensitive from a scaling thickness of 0.5 mm. Whilst this research is specific to the pipe–scaling geometry—and type used in the experiments conducted, resonance testing can be applied to any pipe–scaling combination. The robustness of the data processing approaches presented in this work, when applied to other pipe–scaling materials and geometries, is the next point of research.

Electric vehicle ramp recognition based on fusion of vehicle mass estimation

Road slope is the necessary environmental information for intelligent electric vehicles. The accuracy of slope recognition directly determines the control quality of vehicles on the hill. Aiming at the problems that the existing ramp recognition algorithms have poor adaptability to the conditions and are unable to To satisfy the application requirements of mass production vehicles, this paper proposes an electric vehicle slope recognition method based on vehicle mass estimation. Firstly, the vehicle longitudinal dynamics model is established, and the signal characteristics of the acceleration sensor under the actual vehicle conditions are analyzed. The least square vehicle mass estimation strategy with forgetting factor is constructed to obtain the vehicle mass directly under the starting condition. Ramp recognition algorithms are designed for static parking and dynamic driving scenarios. In the static scenario, the filter latch strategy is used to deal with the interference factors such as activities in the vehicle. In the dynamic scenario, the Kalman filter algorithm based on measurement noise adaptation is designed to realize the fusion estimation of dynamic observation and kinematic observation for the slope. The effectiveness of the method is verified by Simulink -CarSim joint simulation. Finally, the real vehicle test is completed on Chery new energy's mass production electric vehicle platform and domain controller. road test results show that the mass estimation error is less than ±10 kg; the static estimation error of the slope is less than 0.001 rad, and the dynamic error is within 0.005 rad. The estimation accuracy and stability are greatly improved, which ensures the environmental adaptability of the intelligent electric vehicles.

Study of the Mechanism of Perceived Rotational Acceleration of a Bionic Semicircular Canal on the Basis of the "Circular Geometry Hypothesis".

Academia often uses the "circular geometry hypothesis" to explain the sensing principle of the human semicircular canal (SCC) system for angular acceleration, which is widely accepted as an important angular acceleration sensor in the human balance system. On the basis of this hypothesis and the anatomical structure of human SCCs, a series of physical SCC models with different geometries at 4× magnification were prepared via three-dimensional printing and modification of hydrogels. Theoretical models of the SCC perception mechanism were established. Then, impulse angular acceleration, sinusoidal rotation, and sinusoidal linear stimulation were applied to the models, and their responses were visually observed and analyzed in detail. As a result, the circular SCC model had a larger system gain and a smaller phase difference for angular acceleration stimulation but a smaller system gain and a larger phase difference for linear acceleration stimulation. These results verified that the circular semicircular canal was more sensitive to angular acceleration. Our bionic model is hoped to be used for demonstrating the human SCC working process, facilitating researchers in better understanding of the working mechanism of the human SCC, or as a manual model for medical staff to simulate the diagnosis and treatment of human SCC.

Effects of epileptic seizures on the quality of biosignals recorded from wearables.

Wearable nonelectroencephalographic biosignal recordings captured from the wrist offer enormous potential for seizure monitoring. However, signal quality remains a challenging factor affecting data reliability. Models trained for seizure detection depend on the quality of recordings in peri-ictal periods in performing a feature-based separation of ictal periods from interictal periods. Thus, this study aims to investigate the effect of epileptic seizures on signal quality, ensuring accurate and reliable monitoring. This study assesses the signal quality of wearable data during peri-ictal phases of generalized tonic-clonic and focal to bilateral tonic-clonic seizures (TCS), focal motor seizures (FMS), and focal nonmotor seizures (FNMS). We evaluated accelerometer (ACC) activity and the signal quality of electrodermal activity (EDA) and blood volume pulse (BVP) data. Additionally, we analyzed the influence of peri-ictal movements as assessed by ACC (ACC activity) on signal quality and examined intraictal subphases of focal to bilateral TCS. We analyzed 386 seizures from 111 individuals in three international epilepsy monitoring units. BVP signal quality and ACC activity levels differed between all seizure types. We found the largest decrease in BVP signal quality and increase in ACC activity when comparing the ictal phase to the pre- and postictal phases for TCS. Additionally, ACC activity was strongly negatively correlated with BVP signal quality for TCS and FMS, and weakly for FNMS. Intraictal analysis revealed that tonic and clonic subphases have the lowest BVP signal quality and the highest ACC activity. Motor elements of seizures significantly impair BVP signal quality, but do not have significant effect on EDA signal quality, as assessed by wrist-worn wearables. The results underscore the importance of signal quality assessment methods and careful selection of robust modalities to ensure reliable seizure detection. Future research is needed to explain whether seizure detection models' decisions are based on signal responses induced by physiological processes as opposed to artifacts.

Synthetic image data generation via rendering techniques for training AI-based Instance Segmentation

Within the scope of the development of the project, the acceleration and gyro signal, VR implementation, and User Control for VR were realized. The starting point was a list of MPU9250 data, which are acceleration and gyro data. The goal is to do the signal processing of the signal that is got from MPU9250 and implement the data with a specific format so that the LINAK LA36 actuator can be controlled in such a way that it performs as desired position. For this purpose, a multi-axis acceleration and position sensor is mounted on a surfing board which then collects the acceleration and Gyro data. A MATLAB Program had been written to convert the desired position into a CAN message. Before that, the raw acceleration data and gyro must be converted and filtered into actual data. First, the acceleration data was collected from another group. A signal processing method has been implemented. This includes gravitational force filter, noise filter, integration, and complementary filter method was used in order to get the accurate actual data that is able to be implemented into the system. For those methods, it was necessary to deal extensively with the characteristics of signal processing. VR application was implemented in this project to give the user a sensational surfing experience in the virtual world. User control has been implemented as well to allow users to control the video. Unity with Arduino work together so that both Arduino and Unity can communicate with each other, and signal messages will be sent to the Arduino from Unity and vice versa to achieve the synchronization of the video signal and Unity VR application.

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.

Machine learning-driven intelligent tire wear detection system

Traditional methods detect wear by interpreting mathematical models and tire characteristics; however, these methods struggle to accurately reflect the actual rolling condition of the tire. In this study, we propose a machine learning-based tire wear detection module that can provide accurate results under tire test rig conditions. To develop this module, we designed three key components: integrated acceleration and PVDF sensors within the tire to capture vibration and deformation data; signal preprocessing algorithms to highlight multi-source signal differences under varying wear conditions; and deep learning algorithms to achieve precise tire wear grade identification. Experimental results demonstrate that, under different tire pressures, loads, speeds, and wear levels, the system can accurately identify tire wear grades with 99.99% accuracy by combining data from both sensors.

Kinematic Analysis of the Vibration Harvesting Process of Lycium barbarum L. Fruit

The traditional shrub fruits harvesting method is manual picking, while the efficiency is low, which seriously restricts the development of Lycium barbarum L. industry. In order to mechanize the harvesting process of Lycium barbarum L. and improve the correct picking rate while reducing the damage rate of Lycium barbarum L. harvesting, it is very important to analyze the kinematic model of the fruit-bearing branch during vibration harvesting. Through the measurement and analysis of the natural characteristics and physical parameters of the branches, a simplified model of Lycium barbarum L. shrub fruit-bearing branch was built by Solidworks 2023 software, and the appropriate material properties were selected. Through modal analysis and harmonious response analysis, the response characteristics data of fruit-bearing branches of Lycium barbarum L. shrub were obtained. In Qinghai Nuomuhong Farm, the field vibration harvesting kinematic model feature analysis test was carried out, and the acceleration data of the vibration harvesting process were collected by using the acceleration sensor, and through the analysis of the frequency spectrum characteristics of the data, it was concluded that when the excitation frequency was maintained between 8 and 14 Hz, the Lycium barbarum L. fell off well and the picking rate can reach 97.56%, the efficiency can reach 6.88 pieces of fruit per second, and the branch damage was acceptable, which theoretically met the needs of harvesting.

A simulation study on visual preview control of vehicle magnetorheological suspension

Abstract At present, the boom in intelligent vehicles, especially automotives, has brought magnetorheological (MR hereinafter) suspensions into further focus. Generally, traditional MR suspensions work with acceleration sensors to respond with a non-negligible time delay issue due to the inductive property of MR damper (MRD hereinafter), which hinders their full performance especially at high-speed driving with a powerless “blind zone”. To address this issue, this study proposes a simulation study on visual preview control for vehicle MR suspension based on MPC (Model Predictive Control), since the road preview is able to compensate the time delay due to the future road information perceived in advance. First, a camera with a computer vision algorithm is employed to preview road information of target type, target size and distance to wheels. Besides, the error correction of radial and tangential distortions of camera lens are investigated, and the trajectory of wheels are predicted by a cycloid model because MR suspension only needs to respond to the road targets that the wheels will pass over. Further, the area in front of the vehicle is divided into a recognition zone and action zone, and the road targets within action zone (&lt;1.5 m) are calculated more precisely for a prepared immediate response from the MR suspension. Then, the performance of a simulating vehicle MR suspension is investigated through a joint simulation by the automotive simulation software CarSim and the model-solving Simulink. The evident results show that the MR suspension with visual preview control performs the best in ride comfort (mean square values of sprung mass acceleration is 82% and 52% lower compared to a passive suspension and a MR suspension with skyhook control, respectively). Therefore, the visual preview control strategy of MR suspension is able to enhance the performance on the discrete road conditions.

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.