Raw Acceleration Data Research Articles

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.

Synchronous LoRa sensor nodes for modal identification in footbridge vibration monitoring

AbstractThis article aims at presenting a preliminary investigation of using long-range and low-cost LoRa (Long Range) technology and two synchronous LoRa sensor nodes for cost-effective footbridge structural health monitoring (SHM). Two sensor nodes with LoRa modules and accelerometers were employed for vibration monitoring and a new attempt was made to use synchronous LoRa nodes for modal identification. In this article, a modal identification method based on a lightweight synchronization concept was proposed. This method is able to identify the fundamental mode of vibrating beam structures. Meanwhile, maximum accelerations can also be tracked periodically from the simultaneously recorded acceleration data by the synchronous LoRa nodes. Specifically, synchronization was achieved through the wireless peer-to-peer (P2P) communication between the two LoRa nodes, with the aim of initializing simultaneous acceleration recordings. The fundamental frequency and the phase information derived from the on-board calculation of the synchronized nodes can then be used to effectively identify the vertical bending mode and torsion mode. A series of laboratory tests were also conducted on a beam structure for the purpose of validation. The test results showed that the fundamental mode of the vibrating beam can be obtained rapidly and accurately using the synchronized LoRa nodes, with an average synchronization accuracy of 4.45 ms. The maximum acceleration data recorded by the LoRa nodes also showed high accuracy when compared with the raw acceleration data collected from a commercial Bluetooth accelerometer node. The fundamental frequencies obtained from both types of nodes also compare reasonably. The proposed modal identification method using two synchronous LoRa sensor nodes provides a basis for the development of a low-cost footbridge SHM system with the integration of IoT techniques. An attempt has been made to perform a preliminary field test on a cable-stayed footbridge, where the fundamental frequency and the mode shape type of the footbridge were successfully identified and its serviceability condition was also found to satisfy the code requirements.

Classification of movements of collegiate female soccer players using inertial measurement units

This study aimed to classify the movements of female soccer players during matches using raw data measured by inertial measurement units (IMUs). Twelve collegiate female soccer players were equipped with IMUs (100 Hz), and raw triaxial acceleration data from eight official matches were analyzed. The measurement data were separated every 3 s, and a Fast Fourier Transform (FFT) was performed. After FFT, the Euclidean distances between the data when the players were in the stationary state and other states were calculated to classify the movements of the players using the k-means method. The data of the clustering numbers classified as the stationary state were eliminated after analyzing the movements of players by video filming the matches. After classification, the average Euclidean distances between the stationary state and other movements were calculated. Consequently, the results showed that the upward and downward directions of the raw data affected the classification. Using the methods of this study, it was also shown that the distribution of Euclidean distances differed from player to player. Our findings indicate that the method used in this study can be used to classify and characterize the movements of female collegiate soccer players.

Algorithm Validation for Quantifying ActiGraph™ Physical Activity Metrics in Individuals with Chronic Low Back Pain and Healthy Controls.

Assessing physical activity is important in the treatment of chronic conditions, including chronic low back pain (cLBP). ActiGraph™, a widely used physical activity monitor, collects raw acceleration data, and processes these data through proprietary algorithms to produce physical activity measures. The purpose of this study was to replicate ActiGraph™ algorithms in MATLAB and test the validity of this method with both healthy controls and participants with cLBP. MATLAB code was developed to replicate ActiGraph™'s activity counts and step counts algorithms, to sum the activity counts into counts per minute (CPM), and categorize each minute into activity intensity cut points. A free-living validation was performed where 24 individuals, 12 cLBP and 12 healthy, wore an ActiGraph™ GT9X on their non-dominant hip for up to seven days. The raw acceleration data were processed in both ActiLife™ (v6), ActiGraph™'s data analysis software platform, and through MATLAB (2022a). Percent errors between methods for all 24 participants, as well as separated by cLBP and healthy, were all less than 2%. ActiGraph™ algorithms were replicated and validated for both populations, based on minimal error differences between ActiLife™ and MATLAB, allowing researchers to analyze data from any accelerometer in a manner comparable to ActiLife™.

A data-driven approach to detect upper limb functional use during daily life in breast cancer survivors using wrist-worn sensors

To gain insights into the impact of upper limb (UL) dysfunctions after breast cancer treatment, this study aimed to develop a temporal convolutional neural network (TCN) to detect functional daily UL use in breast cancer survivors using data from a wrist-worn accelerometer. A pre-existing dataset of 10 breast cancer survivors was used that contained raw 3-axis acceleration data and simultaneously recorded video data, captured during four daily life activities. The input of our TCN consists of a 3-axis acceleration sequence with a receptive field of 243 samples. The 4 ResNet TCN blocks perform dilated temporal convolutions with a kernel of size 3 and a dilation rate that increases by a factor of 3 after each iteration. Outcomes of interest were functional UL use (minutes) and percentage UL use. We found strong agreement between the video and predicted data for functional UL use (ICC = 0.975) and moderately strong agreement for %UL use (ICC = 0.794). The TCN model overestimated the functional UL use by 0.71 min and 3.06%. Model performance showed good accuracy, f1, and AUPRC scores (0.875, 0.909, 0.954, respectively). In conclusion, using wrist-worn accelerometer data, the TCN model effectively identified functional UL use in daily life among breast cancer survivors.

Multi-Task AI-driven undetermined blind source identification for modal identification of large scale structures

The effective management of aging infrastructure represents a critical concern globally, exerting profound socio-economic influences and occupying a prominent position on the political agenda. The escalating occurrences of catastrophic collapses in recent years underscore the pressing requirement for efficient Structural Health Monitoring (SHM) strategies. These strategies play a pivotal role in aiding decision-making processes related to prioritizing interventions and rehabilitation efforts. Among SHM systems, those utilizing Operational Modal Analysis (OMA) through vibration-based techniques have gained popularity due to their non-destructive nature, comprehensive damage assessment capabilities, and relative ease of automation. Despite their advantages, current OMA methods encounter significant scalability challenges, primarily attributable to extensive computational demands and the necessity for substantial expert involvement. In this context, recent advancements in the field of artificial intelligence (AI) present a promising solution to address these scalability issues, opening the door for the development of next-generation SHM systems. This study introduces an innovative Multitask Learning Deep Neural Network (MTL-DNN) model designed for rapid and automated blind source modal identification of structures. Integrating the concepts of undetermined second-order blind source identification (SOBI) into the architecture of the network, the proposed model can reveal the independent modal components present in the raw response acceleration data with minimum computational burden. The presented results demonstrate the quasi-instantaneous modal identification capabilities of the proposed architecture, enabling the estimation of more components than the number of sensors.

Adapting the Intensity Gradient for Use with Count-Based Accelerometry Data in Children and Adolescents.

The intensity gradient is a new cutpoint-free metric that was developed to quantify physical activity (PA) measured using accelerometers. This metric was developed for use with the ENMO (Euclidean norm minus one) metric, derived from raw acceleration data, and has not been validated for use with count-based accelerometer data. In this study, we determined whether the intensity gradient could be reproduced using count-based accelerometer data. Twenty participants (aged 7-22 years) wore a GT1M, an ActiGraph (count-based), and a GT9X, ActiGraph (raw accelerations) accelerometer during both in-lab and at-home protocols. We found strong agreement between GT1M and GT9X counts during the combined in-lab activities (mean bias = 2 counts) and between minutes per day with different intensities of activity (e.g., sedentary, light, moderate, and vigorous) classified using cutpoints (mean bias < 5 min/d at all intensities). We generated bin sizes that could be used to generate IGs from the count data (mean bias = -0.15; 95% LOA [-0.65, 0.34]) compared with the original IG. Therefore, the intensity gradient could be used to analyze count data. The count-based intensity gradient metric will be valuable for re-analyzing historical datasets collected using older accelerometer models, such as the GT1M.

Improving the identification of bone-specific physical activity using wrist-worn accelerometry: A cross-sectional study in 11-12-year-old Australian children.

Physical activity (PA) during childhood and adolescence is important for the accrual of maximal peak bone mass. The precise dose that benefits bone remains unclear as methods commonly used to analyze PA data are unsuitable for measuring bone-relevant PA. Using improved accelerometry methods, this study identified the amount and intensity of PA most strongly associated with bone outcomes in 11-12-year-olds. Participants (n=770; 382 boys) underwent tibial peripheral quantitative computed tomography to assess trabecular and cortical density, endosteal and periosteal circumference and polar stress-strain index. Seven-day wrist-worn raw acceleration data averaged over 1-s epochs was used to estimate time accumulated above incremental PA intensities (50 milli-gravitational unit (mg) increments from 200 to 3000mg). Associations between time spent above each 50mg increment and bone outcomes were assessed using multiple linear regression, adjusted for age, sex, height, weight, maturity, socioeconomic position, muscle cross-sectional area and PA below the intensity of interest. There was a gradual increase in mean R2 change across all bone-related outcomes as the intensity increased in 50mg increments from >200 to >700mg. All outcomes became significant at >700mg (R2 change=0.6%-1.3% and p=0.001-0.02). Any further increases in intensity led to a reduction in mean R2 change and associations became non-significant for all outcomes >1500mg. Using more appropriate accelerometry methods (1-s epochs; no a priori application of traditional cut-points) enabled us to identify that ∼10min/day of PA >700mg (equivalent to running ∼10km/h) was positively associated with pQCT-derived measures of bone density, geometry and strength in 11-12-year-olds.

Development of a multi-wear-site, deep learning-based physical activity intensity classification algorithm using raw acceleration data.

Accelerometers are widely adopted in research and consumer devices as a tool to measure physical activity. However, existing algorithms used to estimate activity intensity are wear-site-specific. Non-compliance to wear instructions may lead to misspecifications. In this study, we developed deep neural network models to classify device placement and activity intensity based on raw acceleration data. Performances of these models were evaluated by making comparisons to the ground truth and results derived from existing count-based algorithms. 54 participants (26 adults 26.9±8.7 years; 28 children, 12.1±2.3 years) completed a series of activity tasks in a laboratory with accelerometers attached to each of their hip, wrist, and chest. Their metabolic rates at rest and during activity periods were measured using the portable COSMED K5; data were then converted to metabolic equivalents, and used as the ground truth for activity intensity. Deep neutral networks using the Long Short-Term Memory approach were trained and evaluated based on raw acceleration data collected from accelerometers. Models to classify wear-site and activity intensity, respectively, were evaluated. The trained models correctly classified wear-sites and activity intensities over 90% of the time, which outperformed count-based algorithms (wear-site correctly specified: 83% to 85%; wear-site misspecified: 64% to 75%). When additional parameters of age, height and weight of participants were specified, the accuracy of some prediction models surpassed 95%. Results of the study suggest that accelerometer placement could be determined prospectively, and non-wear-site-specific algorithms had satisfactory accuracies. The performances, in terms of intensity classification, of these models also exceeded typical count-based algorithms. Without being restricted to one specific wear-site, research protocols for accelerometers wear could allow more autonomy to participants, which may in turn improve their acceptance and compliance to wear protocols, and in turn more accurate results.

Exploring deep learning techniques for wild animal behaviour classification using animal‐borne accelerometers

Abstract Machine learning‐based behaviour classification using acceleration data is a powerful tool in bio‐logging research. Deep learning architectures such as convolutional neural networks (CNN), long short‐term memory (LSTM) and self‐attention mechanism as well as related training techniques have been extensively studied in human activity recognition. However, they have rarely been used in wild animal studies. The main challenges of acceleration‐based wild animal behaviour classification include data shortages, class imbalance problems, various types of noise in data due to differences in individual behaviour and where the loggers were attached and complexity in data due to complex animal‐specific behaviours, which may have limited the application of deep learning techniques in this area. To overcome these challenges, we explored the effectiveness of techniques for efficient model training: data augmentation, manifold mixup and pre‐training of deep learning models with unlabelled data, using datasets from two species of wild seabirds and state‐of‐the‐art deep learning model architectures. Data augmentation improved the overall model performance when one of the various techniques (none, scaling, jittering, permutation, time‐warping and rotation) was randomly applied to each data during mini‐batch training. Manifold mixup also improved model performance, but not as much as random data augmentation. Pre‐training with unlabelled data did not improve model performance. The state‐of‐the‐art deep learning models, including a model consisting of four CNN layers, an LSTM layer and a multi‐head attention layer, as well as its modified version with shortcut connection, showed better performance among other comparative models. Using only raw acceleration data as inputs, these models outperformed classic machine learning approaches that used 119 handcrafted features. Our experiments showed that deep learning techniques are promising for acceleration‐based behaviour classification of wild animals and highlighted some challenges (e.g. effective use of unlabelled data). There is scope for greater exploration of deep learning techniques in wild animal studies (e.g. advanced data augmentation, multimodal sensor data use, transfer learning and self‐supervised learning). We hope that this study will stimulate the development of deep learning techniques for wild animal behaviour classification using time‐series sensor data.

Comparative Analysis and Conversion Between Actiwatch and ActiGraph Open-Source Counts

Body-worn sensors have contributed to a rich and growing body of literature in public health and clinical research in the last decades. A major challenge in sensor research is the lack of consistency and standardization of the collection and reporting of the sensor data. The algorithms used to derive these activity counts can be vastly different between manufactures and not always transparent to the researchers. With Philips, one of the major research-grade wearable device manufacturers, discontinuing this product line, many researchers are left in need of alternative solutions and at the risk of not being able to relate their historical data using the Philips Actiwatch 2 devices to future findings with other devices. We herein provide a comparison analysis and conversion method that can be used to convert activity counts from Philips to those from ActiGraph, another major manufacturer who provide both raw acceleration data and count data based on their open-source algorithm to the research community. This work provides an approach to maximize the scientific value of historical actigraphy data collected by the Actiwatch devices to support research continuity in this community. The conversion, however, is not perfect and only offers an approximation, due to the intrinsic difference in the count algorithms between the two accelerometers, and the permanent information loss during data reduction. We encourage future research using body-worn sensors to retain the raw sensor data to ensure data consistency, comparability, and the ability to leverage future algorithm improvement.

Agcounts: An R Package to Calculate ActiGraph Activity Counts From Portable Accelerometers

Portable accelerometers are used to capture physical activity in free-living individuals with the ActiGraph being one of the most widely used device brands in physical activity and health research. Recently, in February 2022, ActiGraph published their activity count algorithm and released a Python package for generating activity counts from raw acceleration data for five generations of ActiGraph devices. The nonproprietary derivation of the ActiGraph count improved the transparency and interpretation of accelerometer device-measured physical activity, but the Python release of the count algorithm does not integrate with packages developed by the physical activity research community using the R Statistical Programming Language. In this technical note, we describe our efforts to create an R-based translation of ActiGraph’s Python package with additional extensions to make data processing easier and faster for end users. We call the resulting R package agcounts and provide an inside look at its key functionalities and extensions while discussing its prospective impacts on collaborative open-source software development in physical behavior research. We recommend that device manufacturers follow ActiGraph’s lead by providing open-source access to their data processing algorithms and encourage physical activity researchers to contribute to the further development and refinement of agcounts and other open-source software.

SOCIAL ISOLATION IN OLDER ADULTS POST HIP SURGERY IS CORRELATED WITH MOBILITY AND PHYSIOLOGICAL INDICATORS

Abstract Hip fracture affect approximately 30,000 people in Canada and more than 300,000 people in U.S., annually. Older adults post hip-fracture surgery experience social isolation along with reduced physical mobility, changes in sleeping patterns, reduced indoor and outdoor activities, and other physiological changes once they are discharged form inpatient rehabilitation. Our team has developed MAISON (Multimodal AI-based Sensor platform for Older iNdividuals), a cloud-based multimodal sensor system that supports the collection of physiological, ambient and contextual data from various smart devices. Currently, MAISON consists of a smart watch, a smart phone, a motion sensor and a sleep mattress. Using MAISON, we have collected 24 weeks of raw acceleration data, heartrate, step count, frequency of indoor motion, GPS and sleep metrics from three older adults post-hip surgery living in the community. Statistical and domain-specific features were extracted from the collected data with a time window of one day, including average heartrate, maximum acceleration, total sleep time, total steps taken, and average number of exits. Additionally, clinical data is collected from the participants on a biweekly basis consisting of three questionnaires (i.e., Social Isolation Scale (SIS)) and two physical tests. The correlation between features from sensor data and clinical data was performed using Spearman coefficient, which showed a strong positive correlation (&amp;gt;0.5) between the SIS and number of exits, variance of acceleration, total sleep time, and heartrate variance. Various correlation values were found between features from all the sensors and SIS, indicating the usefulness of multimodal sensors for this application.

A 2D-CNN-Based Two-Stage Structural Damage Localization and Quantification Technique Using Time Domain Vibration Data

The conventional approaches for detecting structural degradation are time-consuming, labor-intensive, and costly. The physical monitoring of the structure also poses risks to the health and safety of supervisors. Therefore, damage estimation of any structure using artificial intelligence (AI), more specifically deep learning (DL), is becoming more significant in civil infrastructure. In the presented research article, an efficient two-stage damage detection method is proposed for structural damage detection (SDD) from time domain vibration signals. The proposed method utilizes two-dimensional convolutional neural network (2D-CNN) architecture as a DL algorithm for damage detection. Here, a computer-aided damage detection method for steel beam and frame-type structures is developed using 2D-CNN algorithm in the Google Colab platform. The effectiveness of the proposed method is first verified, and it provides more than 90% accuracy for identifying the damage location and severity of a cantilever beam for single- and multi-damage scenarios from numerically simulated noisy displacement data. The algorithm is also experimentally validated through the raw acceleration data of damaged steel frame joints collected from the Qatar University Grandstand simulator (QUGS). The proposed 2D-CNN algorithm performs better than other DL algorithms by achieving 100% accuracy within 10 epochs for damage detection of steel frames using QUGS data. It demonstrates significant potential for detecting damage location and quantifying damages for single- and multi-damage scenarios using noise-free and noisy datasets. The primary contribution of this study resides in the implementation of two-stage damage detection algorithm utilizing 2D-CNN with time domain vibration response for multiclass damage identification and quantification.

Deep-Learning-Based Approach for Automated Detection of Irregular Walking Surfaces for Walkability Assessment with Wearable Sensor

A neighborhood’s walkability is associated with public health, economic and environmental benefits. The state of the walking surface on sidewalks is a key factor in assessing walkability, as it promotes pedestrian movement and exercise. Yet, conventional practices for assessing sidewalks are labor-intensive and rely on subject-matter experts, rendering them subjective, inefficient and ineffective. Wearable sensors can be utilized to address these limitations. This study proposes a novel classification method that employs a long short-term memory (LSTM) network to analyze gait data gathered from a single wearable accelerometer to automatically identify irregular walking surfaces. Three different input modalities—raw acceleration data, single-stride and multi-stride hand-crafted accelerometer-based gait features—were explored and their effects on the classification performance of the proposed method were compared and analyzed. To verify the effectiveness of the proposed approach, we compared the performance of the LSTM models to the traditional baseline support vector machine (SVM) machine learning method presented in our previous study. The results from the experiment demonstrated the effectiveness of the proposed framework, thereby validating its feasibility. Both LSTM networks trained with single-stride and multi-stride gait feature modalities outperformed the baseline SVM model. The LSTM network trained with multi-stride gait features achieved the highest average AUC of 83%. The classification performance of the LSTM model trained with single-stride gait features further improved to an AUC of 88% with post-processing, making it the most effective model. The proposed classification framework serves as an unbiased, user-oriented tool for conducting sidewalk surface condition assessments.

LOADING OF PHYSICAL ACTIVITY REDUCES RISK OF SARCOPENIA IN MIDDLE-AGED ADULTS

AbstractObjectivesThe aim of this study was to investigate whether mechanical loading induced by physical activity can reduce risk of sarcopenia in middle-aged adults.MethodsThis was a longitudinal study based on a subset of UK Biobank data consisting of 1,918 participants (902 men and 1,016 women, mean age 56 years) who had no sarcopenia at baseline (assessed between 2006 and 2010). The participants were assessed again after 6 years at follow-up, and were categorized into no sarcopenia, probable sarcopenia, or sarcopenia according to the definition and algorithm developed in 2018 by European Working Group on Sarcopenia in Older People (EWGSOP). Physical activity was assessed at a time between baseline and follow-up using 7-day acceleration data obtained from wrist worn accelerometers. Raw acceleration data were then analysed to study the mechanical loading of physical activity at different intensities (i.e. very light, light, moderate-to-vigorous). Multinominal logistic regression was employed to examine the association between the incidence of sarcopenia and physical activity loading, between baseline and follow up, controlled for other factors at baseline including age, gender, BMI, smoking status, intake of alcohol, vitamin D and calcium, history of rheumatoid arthritis, osteoarthritis, secondary osteoporosis, and type 2 diabetes.ResultsAmong the 1918 participants with no sarcopenia at baseline, 230 (69 men and 161 women) developed probable sarcopenia and 37 (14 men and 23 women) developed sarcopenia at follow-up. Physical activity loading at moderate-to-vigorous intensity was higher in men (p&lt;0.05), while women had higher physical activity loading at very light intensity (p&lt;0.05). No significant difference was found in physical activity loading at light intensity between men and women (p&gt;0.05). Logistic regression models showed that increase in physical activity loading at moderate-to-vigorous intensity significantly reduced the risk of sarcopenia (odds ratio = 0.368, p&lt;0.05), but not probable sarcopenia (odds ratio = 0.974, p&gt;0.05), while loading at light or very light activity intensity were not associated with the risk of sarcopenia or probable sarcopenia (p&gt;0.05).ConclusionLoading of physical activity at moderate-to-vigorous intensity could reduce risk of sarcopenia in middle-aged adults.Declaration of Interest(b) declare that there is no conflict of interest that could be perceived as prejudicing the impartiality of the research reported:I declare that there is no conflict of interest that could be perceived as prejudicing the impartiality of the research project.

Bridge Acceleration Data Cleaning Based on Two-Stage Classification Model with Multiple Feature Fusion

Over the past few decades, rapid economic development has led to the establishment of numerous monitoring systems, resulting in the accumulation of vast amounts of monitoring data. Among these data, dynamic acceleration data stand out prominently. However, the quality of collected acceleration data is often compromised due to factors such as challenging operational environments and sensor malfunctions. This severely hampers the value extracted from the data. Although manual identification and classification of data anomalies are more reliable, they are time consuming and labor intensive. To address the challenge of identifying and classifying anomalies in massive acceleration data, this paper proposes a two-stage model for intelligent data cleaning. Firstly, raw acceleration data are transformed into IPDF and PSD features, and a one-dimensional convolutional neural network is trained to preliminarily identify and classify acceleration data anomalies. Subsequently, the RPV indicator is extracted from the original data of the normal and outlier categories to achieve precise classification based on threshold values. The proposed method is successfully validated using acceleration monitoring data from a large-span arch bridge, achieving an accuracy of over 99%. Furthermore, compared to directly employing a one-dimensional CNN classification model, the approach significantly enhances the model’s perception of local significant disturbances.

O.4.3-3 Optimising time-use data to determine the optimal balance between 24-hour movement behaviours for the prevention of childhood obesity

Abstract Purpose This cross-sectional study aimed to investigate the associations between 24-hour movement behaviours and adiposity and to determine optimal durations for sleep, sedentary behaviour (SB), light physical activity (LPA), and moderate-to-vigorous physical activity (MPVA) for preventing childhood obesity. Methods The study involved 659 Czech children and adolescents aged 8 to 18 years, whose 24-hour movement behaviours were measured using wrist-worn accelerometers. Data on sleep duration and time spent in SB, LPA, and MPVA were derived from raw acceleration data recorded for seven consecutive days. Healthy adiposity status was defined as any value of the adiposity indicator below the 85th percentile. The study employed compositional regression analysis to investigate the association between movement behaviour composition and adiposity. An 'optimal day' was defined as the compositional mean of all compositions that were associated with a healthy adiposity status. Results We found a significant association between the composition of movement behaviours and visceral adipose tissue (F = 3.672, p = 0.013) in children and the percentage of fat mass (F = 2.733, p = 0.044) in adolescents. The optimal durations of movement behaviours differed across various age groups. For children, the optimal balance of movement behaviours consisted of 8.5 hours of sleep, 10.8 hours of SB, 3.9 hours of LPA, and 0.8 hours of MVPA. For adolescents, the 'optimal day' consisted of 7.5 hours of sleep, 12.4 hours of SB, 3.5 hours of LPA and 0.5 hours of MVPA. Conclusions It seems crucial to optimise the duration of sleep, SB, and physical activity of varying intensities to prevent excess adiposity. Additionally, age-specific variations should be considered while suggesting the most appropriate durations for 24-hour movement behaviours to prevent childhood obesity. Funding source This research was funded by research grants from the Czech Science Foundation (18-09188S and 22-02392S).

IDENTIFICATION OF CHATTER VIBRATIONS AND ACTIVE VIBRATION CONTROL BY USING THE SLIDING MODE CONTROLLER ON DRY TURNING OF TITANIUM ALLOY (TI6AL4V)

In recent years, with the development of sensor technologies, communication platforms, cyber-physical systems, storage technologies, internet applications and controller infrastructures, the way has been opened to produce competitive products with high quality and low cost. In turning, which is one of the important processes of machining, chatter vibrations are among the biggest problems affecting product quality, productivity and cost. There are many techniques proposed to reduce chatter vibrations for which the exact cause cannot be determined. In this study, an active vibration control based on the Sliding Mode Control (SMC) has been implemented in order to reduce and eliminate chatter vibration, which is undesirable for the turning process. In this context, three-axis acceleration data were collected from the cutting tool during the turning of Ti6Al4V. Finite Impulse Response (FIR) filtering, Fast Fourier Transform (FFT) analysis and integral process were carried out in order to use the raw acceleration data collected over the system in control. The system is modeled mathematically and an active control block diagram is created. It is observed that chattering decreased significantly after the application of active vibration control. The surface quality formed by the amplitude of the graph obtained after active control has been compared and verified with the data obtained from the actual manufacturing result.

Associations of Monitor-Independent Movement Summary and Health-Related Fitness With Gross Motor Skills in Young Children

The purpose of this study was to determine the associations of device-based assessments of physical activity (PA) and health-related fitness (HRF) with gross motor skills (GMS) in preschool-aged children. Participants were 3- to 5-year-old children (N = 316; 49.6% female) who participated in the 2012 National Youth Fitness Survey. GMS was assessed using the gross motor quotient calculated from the Test for Gross Motor Development—Second Edition. PA was assessed using wrist-worn ActiGraph GT3X accelerometers with raw triaxial acceleration data summarized using monitor-independent movement summary units (MIMS). Analyzed metrics included average MIMS per day and peak 30-min MIMS. HRF assessment consisted of a plank score and a sum of skinfold assessment. Weighted hierarchical regressions tested the associations between PA, HRF, and GMS variables with a secondary weighted mediation analysis that examined whether HRF mediated the association between PA and GMS. Peak 30-min MIMS significantly correlated with GMS (b = 0.17, p = .005). Plank scores had the strongest correlation with GMS (b = 0.23, p = .004), and weighted mediation analyses revealed that plank scores partially mediated the association between peak 30-min MIMS and GMS (indirect effect = 0.03, p = .01, 23.1% mediation). Peak 30-min MIMS significantly associated with GMS in preschool children, an association partially mediated by core muscular endurance.