Use of accelerometers is now widespread within animal biologging as they provide a means of measuring an animal's activity in a meaningful and quantitative way where direct observation is not possible. In sequential acceleration data, there is a natural dependence between observations of behaviour, a fact that has been largely ignored in most analyses.

Analyses of acceleration data where serial dependence has been explicitly modelled have largely relied on hidden Markov models (HMMs). Depending on the aim of an analysis, an HMM can be used for state prediction or to make inferences about drivers of behaviour. For state prediction, a supervised learning approach can be applied. That is, an HMM is trained to classify unlabelled acceleration data into a finite set of pre-specified categories. An unsupervised learning approach can be used to infer new aspects of animal behaviour when biologically meaningful response variables are used, with the caveat that the states may not map to specific behaviours.

We provide the details necessary to implement and assess an HMM in both the supervised and unsupervised learning context and discuss the data requirements of each case. We outline two applications to marine and aerial systems (shark and eagle) taking the unsupervised learning approach, which is more readily applicable to animal activity measured in the field. HMMs were used to infer the effects of temporal, atmospheric and tidal inputs on animal behaviour.

Animal accelerometer data allow ecologists to identify important correlates and drivers of animal activity (and hence behaviour). The HMM framework is well suited to deal with the main features commonly observed in accelerometer data and can easily be extended to suit a wide range of types of animal activity data. The ability to combine direct observations of animal activity with statistical models, which account for the features of accelerometer data, offers a new way to quantify animal behaviour and energetic expenditure and to deepen our insights into individual behaviour as a constituent of populations and ecosystems.

AcTBeCalf Dataset Description

The AcTBeCalf dataset is a comprehensive dataset designed to support the classification of pre-weaned calf behaviors from accelerometer data. It contains detailed accelerometer readings aligned with annotated behaviors, providing a valuable resource for research in multivariate time-series classification and animal behavior analysis. The dataset includes accelerometer data collected from 30 pre-weaned Holstein Friesian and Jersey calves, housed in group pens at the Teagasc Moorepark Research Farm, Ireland. Each calf was equipped with a 3D accelerometer sensor (AX3, Axivity Ltd, Newcastle, UK) sampling at 25 Hz and attached to a neck collar from one week of birth over 13 weeks.

This dataset encompasses 27.4 hours of accelerometer data aligned with calf behaviors, including both prominent behaviors like lying, standing, and running, as well as less frequent behaviors such as grooming, social interaction, and abnormal behaviors.

The dataset consists of a single CSV file with the following columns:

• dateTime: Timestamp of the accelerometer reading, sampled at 25 Hz.
• calfid: Identification number of the calf (1-30).
• accX: Accelerometer reading for the X axis (top-bottom direction)*.
• accY: Accelerometer reading for the Y axis (backward-forward direction)*.
• accZ: Accelerometer reading for the Z axis (left-right direction)*.
• behavior: Annotated behavior based on an ethogram of 23 behaviors.
• segId: Segment identification number associated with each accelerometer reading/row, representing all readings of the same behavior segment.

* the directions are mentioned in relation to the position of the accelerometer sensor on the calf.

Code Files Description

The dataset is accompanied by several code files to facilitate the preprocessing and analysis of the accelerometer data and to support the development and evaluation of machine learning models. The main code files included in the dataset repository are:

1. accelerometer_time_correction.ipynb: This script corrects the accelerometer time drift, ensuring the alignment of the accelerometer data with the reference time.
2. shake_pattern_detector.py: This script includes an algorithm to detect shake patterns in the accelerometer signal for aligning the accelerometer time series with reference times.
3. aligning_accelerometer_data_with_annotations.ipynb: This notebook aligns the accelerometer time series with the annotated behaviors based on timestamps.
4. manual_inspection_ts_validation.ipynb: This notebook provides a manual inspection process for ensuring the accurate alignment of the accelerometer data with the annotated behaviors.
5. additional_ts_generation.ipynb: This notebook generates additional time-series data from the original X, Y, and Z accelerometer readings, including Magnitude, ODBA (Overall Dynamic Body Acceleration), VeDBA (Vectorial Dynamic Body Acceleration), pitch, and roll.
6. genSplit.py: This script provides the logic used for the generalized subject separation for machine learning model training, validation and testing.
7. active_inactive_classification.ipynb: This notebook details the process of classifying behaviors into active and inactive categories using a RandomForest model, achieving a balanced accuracy of 92%.
8. four_behv_classification.ipynb: This notebook employs the mini-ROCKET feature derivation mechanism and a RidgeClassifierCV to classify behaviors into four categories: drinking milk, lying, running, and other, achieving a balanced accuracy of 84%.

Kindly cite one of the following papers when using this data:

Dissanayake, O., McPherson, S. E., Allyndrée, J., Kennedy, E., Cunningham, P., & Riaboff, L. (2024). Evaluating ROCKET and Catch22 features for calf behaviour classification from accelerometer data using Machine Learning models. arXiv preprint arXiv:2404.18159.

Dissanayake, O., McPherson, S. E., Allyndrée, J., Kennedy, E., Cunningham, P., & Riaboff, L. (2024). Development of a digital tool for monitoring the behaviour of pre-weaned calves using accelerometer neck-collars. arXiv preprint arXiv:2406.17352

Snapshot img

Accelerometers Market Size 2024-2028

The accelerometers market size is forecast to increase by USD 814 million, at a CAGR of 4.5% between 2023 and 2028.

The market is experiencing significant growth, driven primarily by the increasing demand from various end-user industries, including screen rotation in portable devices and industrial automation in manufacturing hubs. This trend is particularly noticeable in the Asia Pacific region, where the market is witnessing robust expansion. However, the market's growth trajectory is not without challenges. One such challenge is the relatively low accuracy of accelerometers in certain industries, such as the semiconductor industry, which may hinder their widespread adoption. Despite this obstacle, companies can capitalize on the market's growth potential by focusing on innovation and improving the accuracy of their accelerometer offerings. Strategic collaborations and partnerships can also help overcome this challenge and expand market reach.
In summary, the market presents a compelling growth opportunity for companies, with increasing demand from end-users, particularly in the Asia Pacific region, offset by the challenge of maintaining high accuracy levels. Companies that can effectively navigate these dynamics and deliver innovative, high-performing accelerometer solutions will be well-positioned to capitalize on this market's potential.

What will be the Size of the Accelerometers Market during the forecast period?

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The market continues to evolve, driven by advancements in technology and increasing applications across various sectors. Capacitive accelerometers, with their high sensitivity and low power consumption, are gaining popularity in automotive and consumer electronics. Mounting configurations for these sensors are becoming more diverse, with silicon microstructures enabling compact designs and improved performance. Inertial measurement units (IMUs) are another key market trend, integrating acceleration data logging, environmental testing, and signal processing algorithms to measure both linear and angular motion. Impact force measurement and tilt sensing are essential applications for IMUs in industries like construction and aerospace. Three-axis accelerometers, based on MEMS technology, are increasingly used for vibration measurement and motion tracking.

Sensor fusion techniques enable the combination of data from multiple sensors, enhancing accuracy and reliability. Linear acceleration sensors and angular rate sensors are crucial components in this context. Industry growth in the market is expected to reach double-digit percentages, fueled by the increasing demand for advanced sensing technologies in various applications. For instance, a leading automotive manufacturer reported a 15% increase in sales due to the integration of advanced accelerometer systems in their latest vehicle models. Dynamic range specifications, interface protocols, packaging techniques, reliability testing, and calibration procedures are essential considerations for accelerometer manufacturers. Bandwidth specifications, power consumption, noise reduction filters, and shock detection systems are other critical factors influencing market dynamics.

In conclusion, the market is characterized by continuous innovation and evolving patterns, with applications ranging from automotive to aerospace and consumer electronics. The integration of advanced technologies like MEMS, sensor fusion, and digital output is driving growth and enhancing performance.

How is this Accelerometers Industry segmented?

The accelerometers industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

End-user

  Industrial
  Automotive
  Consumer electronics
  Aerospace and defense
  Others


Geography

  North America

    US


  Europe

    Germany


  APAC

    China
    India
    Japan


  Rest of World (ROW)

By End-user Insights

The industrial segment is estimated to witness significant growth during the forecast period.

The global accelerometer market is witnessing significant growth due to the increasing adoption of automation in various industries. Industrial applications accounted for the largest market share in 2021, driven by the use of robots and industrial automation systems. Companies offer rugged industrial accelerometers with features such as stainless steel casing, low-frequency response, and waterproofing. For instance, Dytran's model 3185D accelerometer is a rugged IEPE accelerometer with a built-in Faraday shield for electrostatic noise immunity, a sensitivity of 100 mV/g, and a respon

This dataset contains accelerometer and gyroscope readings of 76 undergraduate students (from different ages, different genders) during writing random sentences for 1 minute period with a mobile phone (iPhone X). We provide two datasets. One is for binary classification (one-vs-all) and the other one contains the whole data. These datasets can be used for writing behavior analysis.

The columns of datasets are: ID,Gender,Age,AccX,AccY,AccZ,GyroX,GyroY,GyroZ

These datasets contain movement data and coat colour data from accelerometer-collared hares collected during the autumns of 2015-2017 and springs of 2015-2018. We analyzed how coat colour mismatch affects snowshoe hare foraging time across these two seasons. For more details on how foraging behaviour was inferred from accelerometer data, see Studd et al. (2019). Other variables that influence snowshoe hare foraging time are also included in these datasets. Data were collected in the Kluane Lake region of the Yukon.

The global market for 3-axis accelerometers is experiencing robust growth, driven by increasing demand across diverse sectors. The automotive industry, a significant consumer, is pushing adoption due to the proliferation of advanced driver-assistance systems (ADAS) and the rise of electric vehicles (EVs). These systems rely heavily on precise motion sensing for functionalities like collision avoidance, stability control, and airbag deployment. Furthermore, the burgeoning consumer electronics sector, particularly smartphones, wearables, and gaming devices, fuels market expansion. The integration of accelerometers in these devices for motion tracking, gesture recognition, and health monitoring applications is a key growth catalyst. Industrial applications, such as robotics, industrial automation, and building monitoring, are also contributing to the market's expansion. Precision agriculture and IoT device deployments further add to the overall growth trajectory. A conservative estimate places the 2025 market size around $2 billion, with a Compound Annual Growth Rate (CAGR) of approximately 8% projected through 2033. This growth is tempered by factors like increasing component costs and the potential for technological disruption from emerging sensor technologies. Despite the challenges, several significant trends are shaping the future of the 3-axis accelerometer market. Miniaturization and power efficiency are consistently being improved, allowing integration into even smaller and more energy-constrained devices. The increasing demand for high-precision, low-noise sensors is driving advancements in sensor technology and manufacturing processes. Furthermore, the integration of 3-axis accelerometers with other sensors, such as gyroscopes and magnetometers, to create Inertial Measurement Units (IMUs), is a prevalent trend. This integration provides more comprehensive motion data and expands application possibilities. The rise of artificial intelligence (AI) and machine learning (ML) is also impacting the market, as these technologies enable more sophisticated data analysis and interpretation from accelerometer data, leading to improved accuracy and functionality. Key players like Bosch, NXP, STMicroelectronics, and others are actively innovating to cater to these market demands and maintain their competitive edge.

The analog output accelerometer market is experiencing robust growth, driven by increasing demand across diverse sectors. The automotive industry, a major contributor, utilizes these sensors for advanced driver-assistance systems (ADAS) like electronic stability control and airbag deployment. The aerospace industry relies on them for flight control and navigation systems, emphasizing high accuracy and reliability. Other applications, including industrial automation, robotics, and consumer electronics (wearable devices, smartphones), further fuel market expansion. The market is segmented by type (single-axis, biaxial, triaxial), with triaxial accelerometers gaining traction due to their ability to detect movement in three dimensions, enhancing precision in various applications. The preference for analog output over digital stems from its lower cost and simpler integration in legacy systems, although the shift towards digital is gradual. North America and Europe currently hold significant market shares, owing to established automotive and aerospace industries, however, Asia-Pacific is projected to witness the fastest growth rate due to increasing industrialization and technological advancements in regions like China and India. Competition is intense among established players like Bosch Sensortec, Analog Devices, and STMicroelectronics, alongside emerging companies focusing on niche applications and innovative designs. Challenges include maintaining high accuracy across varying environmental conditions and addressing the miniaturization requirements for compact applications. Over the forecast period (2025-2033), the market is expected to maintain a healthy CAGR (let's assume a conservative estimate of 7%, reflecting a balance between strong growth and potential market saturation). This growth will be influenced by technological advancements resulting in smaller, more power-efficient sensors with enhanced sensitivity and increased functionality. The integration of analog output accelerometers with other sensor technologies (e.g., gyroscopes) to create inertial measurement units (IMUs) will also drive market expansion. Continued development of sophisticated algorithms for data processing and interpretation will further improve the accuracy and reliability of applications reliant on accelerometer data. The increasing adoption of autonomous vehicles and the growing demand for improved safety features in automobiles are poised to be significant growth drivers in the coming years.

The size of the Accelerometer Industry market was valued at USD XX Million in 2023 and is projected to reach USD XXX Million by 2032, with an expected CAGR of 2.97% during the forecast period. Accelerometer industry growth accelerates with increased demand in motion-sensing devices designed for various applications such as consumer electronics, automotive, healthcare, industrial automation, and aerospace. An accelerometer is a sensor that measures the forces imposed on an object because of acceleration. Accurate data on motion, orientation, and vibration is obtained as a result of the acceleration. These devices come in handy in so many technologies, be it detecting phone orients to enabling advanced safety features in vehicles. In the auto industry, for instance, accelerometers are integral to the safety systems such as airbags, stability control, and collision detection that enhance vehicle safety and performance. In consumer electronics, accelerometer is responsible for such features as smartphones that ensure the orientation of screens, wearables that track fitness, and in gaming devices that decide motion sensing. The healthcare sector likewise reassesses accelerometer technology, including its use in wearable devices that can monitor patient activity, detect falls, and manage chronic conditions. Technological factors- miniaturization, increased accuracy, and improved energy efficiency- spur the use of accelerometers in new applications. Exponentially growing more connected devices and associated with the Internet of Things (IoT) have been acting as a growth driver of the market. Accelerometers are used in all and sundry be it smart homes to industrial IoT systems. Accelerometer North America and Asia-Pacific held major market shares, mainly because of high demand from the automotive and electronics industries. Key drivers for this market are: , Emergence of the MEMS Technology; Increasing Demand from Consumer Electronics; Developing Aerospace and Defense Sector (High-end Accelerometers). Potential restraints include: , Costs and Complexity Concerns. Notable trends are: Aerospace and Defense Industry to Account for a Significant Share in the Market.

Background: Adherence to home practice rehabilitation programs is important for efficacy; however, adherence is challenging for many individuals post-stroke. Accelerometers have emerged as a potential means to support home practice. This secondary data analysis explored the use of a commercially available accelerometer with custom software to collect and analyze data to corroborate self-reported practice collected during a home program.Methods: The initial study was a single subject design trial that investigated the effect of preferred music listening on adherence to an upper extremity home practice program (Trial Number NCT02906956. ClinicalTrials.gov). The participants (n = 7) were post-stroke adults with aphasia and hemiparesis of the upper extremity. Participants completed home program exercises while wearing accelerometers and recorded practice times in a logbook. Data were collected, cleaned, processed, and analyzed to facilitate descriptive comparisons and clinical interpretations of accelerometer output data.Results: Across all participants, an average of 47% of data were captured and usable for analysis. Five out of seven participants self-reported longer practice times compared to accelerometer duration output by a mean of 66.5 s. Individual exercise set mean total angular velocity and standard deviation of acceleration demonstrated potential for use across time to monitor change.Conclusions: One challenge of integrating accelerometers into clinical practice is the amount of data loss and the steps for data processing. The comparisons of available accelerometer data to the self-reported logs, however, were generally representative. Future investigations should explore ways to increase data capture and accessibility of the data for feedback to the client and practitioner.

Snapshot img

Commercial Aircraft Piezoelectric Accelerometer Market Size 2024-2028

The commercial aircraft piezoelectric accelerometer market size is forecast to increase by USD 51.26 million at a CAGR of 4.07% between 2023 and 2028.

The market is experiencing significant growth, driven primarily by the increasing demand for newer-generation aircraft. These modern aircraft rely heavily on advanced sensing technologies for improved safety, efficiency, and performance. Piezoelectric acceleromers, with their ability to convert mechanical energy into electrical energy, are a crucial component in these systems. Another key trend in the market is the emergence of nanoelectromechanical systems (NEMS). NEMS piezoelectric accelerometers offer enhanced sensitivity and miniaturization, making them ideal for use in aerospace applications. However, the market also faces challenges, particularly the adverse effect of temperature dissimilarity on piezoelectric accelerometers. Temperature variations between different components in an aircraft can lead to errors in sensor readings, affecting the overall system's accuracy and reliability.
To mitigate this challenge, manufacturers are investing in advanced materials and design techniques to improve temperature stability and ensure consistent performance. Companies seeking to capitalize on market opportunities should focus on developing innovative solutions that address these challenges while leveraging the growing demand for advanced sensing technologies in the aerospace industry.

What will be the Size of the Commercial Aircraft Piezoelectric Accelerometer Market during the forecast period?

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The market continues to evolve, driven by the increasing demand for advanced sensor technologies in the aerospace industry. These sensors play a crucial role in data acquisition for aircraft design, damage detection, aircraft noise reduction, and aircraft certification. The integration of cloud-based data management systems enables real-time monitoring and analysis of vibration signatures, which is essential for aircraft maintenance schedules and component life assessment. Data analytics and vibration analysis are key applications of piezoelectric accelerometers in the aviation sector. These technologies help aircraft manufacturers and operators optimize aircraft performance and ensure compliance with environmental regulations.
Additionally, they facilitate fault diagnosis and aircraft certification standards, enhancing safety and fuel efficiency. The ongoing development of smart aircraft and the implementation of stress measurement and structural health monitoring systems further expand the market's potential. These technologies enable remote monitoring and real-time analysis, providing valuable insights into aircraft performance and operation. The aerospace industry's relentless pursuit of innovation continues to drive the evolution of piezoelectric accelerometer technology, ensuring its continued relevance in the commercial aviation sector.

How is this Commercial Aircraft Piezoelectric Accelerometer Industry segmented?

The commercial aircraft piezoelectric accelerometer industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Type

  Narrow-body aircraft
  Wide-body aircraft
  Regional aircraft


Application

  Monitoring
  Safety and control
  Navigation


Technology Specificity

  Analog
  Digital


Aircraft Type

  Commercial Jets
  Business Jets
  Helicopters


End-User

  OEMs
  Aftermarket


Geography

  North America

    US
    Mexico


  Europe

    France
    Germany
    Italy
    Spain
    UK


  Middle East and Africa

    UAE


  APAC

    Australia
    China
    India
    Japan
    South Korea


  South America

    Brazil


  Rest of World (ROW)

By Type Insights

The narrow-body aircraft segment is estimated to witness significant growth during the forecast period.

In the dynamic world of commercial aviation, the integration of advanced technologies has become a necessity to enhance aircraft performance, safety, and efficiency. Piezoelectric sensors, a key technology in this domain, are increasingly being adopted for various applications. These sensors, which utilize the piezoelectric effect of certain materials to measure mechanical variables such as acceleration, vibration, and mechanical shock, are particularly valuable in the aerospace industry. Aircraft design and manufacturing companies are integrating piezoelectric sensors for damage detection, aircraft noise reduction, and structural health monitoring. Real-time monitoring of aircraft components is essential for optimizing aircraft performance, adhering to flight safety regulations, and ensuring aircraft maintenance schedul

Accelerometer Market is Segmented by Type (MEMS Accelerometers, Piezoelectric Accelerometers, and More), Dimension (1-Axis, 2-Axis, and More), End User (Consumer Electronics, Automotive, and More), Performance Grade (Consumer Grade, Industrial Grade, and More), and Geography (North America, South America, and More). The Market Forecasts are Provided in Terms of Value (USD).

This dataset examines the complexity of network structures in professional and collegiate women’s soccer teams using directed network analysis based on tri-axial acceleration data. The study involved one professional team and one university-level team, with data collected from matches during their respective seasons. Directed network analysis identified dyads and triads, representing cooperative interactions among players, while movement entropy quantified the influence of individual movements within the team. Network diversity, defined as the variability in activation probabilities of dyads and triads, was calculated to evaluate the tactical dynamics and cooperative behaviors of the teams. Data were collected using GNSS devices equipped with tri-axial accelerometers, ensuring precise measurement of movement intensity. The findings provide insights into the structural and functional differences in team coordination between professional and collegiate levels. The dataset is anonymized an..., Participants Prior to participant recruitment, we calculated the minimum required number of matches using G*Power 3.1.9.4 (Heinrich Heine Universität Düsseldorf, Germany). This study employs a two-way analysis of variance (ANOVA) to primarily examine the interaction effects between the period of the match (the first half and second half of the match) and three team groups (professional teams during the first half of the season, professional teams during the second half of the season, and collegiate teams). Thus, the calculation for the F-test with ANOVA was conducted a priori, given an effect size of 0.40, an α error probability of 0.05, a power of 0.80, and a numerator df of 2 with six groups. The effect size (0.40) for this analysis was set based on findings from a previous study that examined changes in team coordination states during matches and reported a large effect size (η² = 0.240 to 0.263) for differences influenced by the level of the opposing team. The total required sample ..., , # Accelerometer-based network analysis in female soccer: performance levels and injuries

https://doi.org/10.5061/dryad.sf7m0cgh6

Description of the data and file structure

Dataset Overview

This dataset investigates the complexity of network structures in professional and collegiate women’s soccer teams, focusing on cooperative interactions and tactical dynamics. Data were collected during matches using GNSS devices equipped with tri-axial accelerometers, providing precise measurements of player movements and interactions.

Data Content

The dataset includes:

1. Player Movement Data: Tri-axial acceleration data recorded at 10 Hz for all players during each match. Data capture spans from match entry to exit, with segmentation into 45-minute halves. Stoppage time is excluded, and substitutions are handled by segmenting data accordingly.
2. Network Analysis Results: Quantitative results of directed network analysis, includ...

ABSTRACT

The issue of diagnosing psychotic diseases, including schizophrenia and bipolar disorder, in particular, the objectification of symptom severity assessment, is still a problem requiring the attention of researchers. Two measures that can be helpful in patient diagnosis are heart rate variability calculated based on electrocardiographic signal and accelerometer mobility data. The following dataset contains data from 30 psychiatric ward patients having schizophrenia or bipolar disorder and 30 healthy persons. The duration of the measurements for individuals was usually between 1.5 and 2 hours. R-R intervals necessary for heart rate variability calculation were collected simultaneously with accelerometer data using a wearable Polar H10 device. The Positive and Negative Syndrome Scale (PANSS) test was performed for each patient participating in the experiment, and its results were attached to the dataset. Furthermore, the code for loading and preprocessing data, as well as for statistical analysis, was included on the corresponding GitHub repository.

BACKGROUND

Heart rate variability (HRV), calculated based on electrocardiographic (ECG) recordings of R-R intervals stemming from the heart's electrical activity, may be used as a biomarker of mental illnesses, including schizophrenia and bipolar disorder (BD) [Benjamin et al]. The variations of R-R interval values correspond to the heart's autonomic regulation changes [Berntson et al, Stogios et al]. Moreover, the HRV measure reflects the activity of the sympathetic and parasympathetic parts of the autonomous nervous system (ANS) [Task Force of the European Society of Cardiology the North American Society of Pacing Electrophysiology, Matusik et al]. Patients with psychotic mental disorders show a tendency for a change in the centrally regulated ANS balance in the direction of less dynamic changes in the ANS activity in response to different environmental conditions [Stogios et al]. Larger sympathetic activity relative to the parasympathetic one leads to lower HRV, while, on the other hand, higher parasympathetic activity translates to higher HRV. This loss of dynamic response may be an indicator of mental health. Additional benefits may come from measuring the daily activity of patients using accelerometry. This may be used to register periods of physical activity and inactivity or withdrawal for further correlation with HRV values recorded at the same time.

EXPERIMENTS

In our experiment, the participants were 30 psychiatric ward patients with schizophrenia or BD and 30 healthy people. All measurements were performed using a Polar H10 wearable device. The sensor collects ECG recordings and accelerometer data and, additionally, prepares a detection of R wave peaks. Participants of the experiment had to wear the sensor for a given time. Basically, it was between 1.5 and 2 hours, but the shortest recording was 70 minutes. During this time, evaluated persons could perform any activity a few minutes after starting the measurement. Participants were encouraged to undertake physical activity and, more specifically, to take a walk. Due to patients being in the medical ward, they received instruction to take a walk in the corridors at the beginning of the experiment. They were to repeat the walk 30 minutes and 1 hour after the first walk. The subsequent walks were to be slightly longer (about 3, 5 and 7 minutes, respectively). We did not remind or supervise the command during the experiment, both in the treatment and the control group. Seven persons from the control group did not receive this order and their measurements correspond to freely selected activities with rest periods but at least three of them performed physical activities during this time. Nevertheless, at the start of the experiment, all participants were requested to rest in a sitting position for 5 minutes. Moreover, for each patient, the disease severity was assessed using the PANSS test and its scores are attached to the dataset.

The data from sensors were collected using Polar Sensor Logger application [Happonen]. Such extracted measurements were then preprocessed and analyzed using the code prepared by the authors of the experiment. It is publicly available on the GitHub repository [Książek et al].

Firstly, we performed a manual artifact detection to remove abnormal heartbeats due to non-sinus beats and technical issues of the device (e.g. temporary disconnections and inappropriate electrode readings). We also performed anomaly detection using Daubechies wavelet transform. Nevertheless, the dataset includes raw data, while a full code necessary to reproduce our anomaly detection approach is available in the repository. Optionally, it is also possible to perform cubic spline data interpolation. After that step, rolling windows of a particular size and time intervals between them are created. Then, a statistical analysis is prepared, e.g. mean HRV calculation using the RMSSD (Root Mean Square of Successive Differences) approach, measuring a relationship between mean HRV and PANSS scores, mobility coefficient calculation based on accelerometer data and verification of dependencies between HRV and mobility scores.

DATA DESCRIPTION

The structure of the dataset is as follows. One folder, called HRV_anonymized_data contains values of R-R intervals together with timestamps for each experiment participant. The data was properly anonymized, i.e. the day of the measurement was removed to prevent person identification. Files concerned with patients have the name treatment_X.csv, where X is the number of the person, while files related to the healthy controls are named control_Y.csv, where Y is the identification number of the person. Furthermore, for visualization purposes, an image of the raw RR intervals for each participant is presented. Its name is raw_RR_{control,treatment}_N.png, where N is the number of the person from the control/treatment group. The collected data are raw, i.e. before the anomaly removal. The code enabling reproducing the anomaly detection stage and removing suspicious heartbeats is publicly available in the repository [Książek et al]. The structure of consecutive files collecting R-R intervals is following:

    Phone timestamp
    RR-interval [ms]


    12:43:26.538000
    651


    12:43:27.189000
    632


    12:43:27.821000
    618


    12:43:28.439000
    621


    12:43:29.060000
    661


    ...
    ...

The first column contains the timestamp for which the distance between two consecutive R peaks was registered. The corresponding R-R interval is presented in the second column of the file and is expressed in milliseconds.
The second folder, called accelerometer_anonymized_data contains values of accelerometer data collected at the same time as R-R intervals. The naming convention is similar to that of the R-R interval data: treatment_X.csv and control_X.csv represent the data coming from the persons from the treatment and control group, respectively, while X is the identification number of the selected participant. The numbers are exactly the same as for R-R intervals. The structure of the files with accelerometer recordings is as follows:

    Phone timestamp
    X [mg]
    Y [mg]
    Z [mg]


    13:00:17.196000
    -961
    -23
    182


    13:00:17.205000
    -965
    -21
    181


    13:00:17.215000
    -966
    -22
    187


    13:00:17.225000
    -967
    -26
    193


    13:00:17.235000
    -965
    -27
    191


    ...
    ...
    ...
    ...

The first column contains a timestamp, while the next three columns correspond to the currently registered acceleration in three axes: X, Y and Z, in milli-g unit.

We also attached a file with the PANSS test scores (PANSS.csv) for all patients participating in the measurement. The structure of this file is as follows:

    no_of_person
    PANSS_P
    PANSS_N
    PANSS_G
    PANSS_total


    1
    8
    13
    22
    43


    2
    11
    7
    18
    36


    3
    14
    30
    44
    88


    4
    18
    13
    27
    58


    ...
    ...
    ...
    ...
    ..

The first column contains the identification number of the patient, while the three following columns refer to the PANSS scores related to positive, negative and general symptoms, respectively.

USAGE NOTES

All the files necessary to run the HRV and/or accelerometer data analysis are available on the GitHub repository [Książek et al]. HRV data loading, preprocessing (i.e. anomaly detection and removal), as well as the calculation of mean HRV values in terms of the RMSSD, is performed in the main.py file. Also, Pearson's correlation coefficients between HRV values and PANSS scores and the statistical tests (Levene's and Mann-Whitney U tests) comparing the treatment and control groups are computed. By default, a sensitivity analysis is made, i.e. running the full pipeline for different settings of the window size for which the HRV is calculated and various time intervals between consecutive windows. Preparing the heatmaps of correlation coefficients and corresponding p-values can be done by running the utils_advanced_plots.py file after performing the sensitivity analysis. Furthermore, a detailed analysis for the one selected set of hyperparameters may be prepared (by setting sensitivity_analysis = False), i.e. for 15-minute window sizes, 1-minute time intervals between consecutive windows and without data interpolation method. Also, patients taking quetiapine may be excluded from further calculations by setting exclude_quetiapine = True because this medicine can have a strong impact on HRV [Hattori et al].

The accelerometer data processing may be performed using the utils_accelerometer.py file. In this case, accelerometer recordings are downsampled to ensure the same timestamps as for R-R intervals and, for each participant, the mobility coefficient is calculated. Then, a correlation

The global sound accelerometer sensor market is poised to witness substantial growth in the coming years, driven by the increasing demand for consumer electronics and healthcare devices. The market size, valued at million in 2025, is projected to expand at a CAGR of % during the forecast period, reaching million by 2033. The rising adoption of TWS earphones, smartwatches, and other wearable devices is fueling the demand for sound accelerometer sensors, which are essential for motion tracking and gesture recognition. Key trends shaping the market include the miniaturization of sensors, the integration of advanced signal processing algorithms, and the growing adoption of wireless connectivity. These advancements are enabling the development of more sophisticated and compact sensors that can accurately measure sound and vibration data. Additionally, the growing emphasis on healthcare and fitness is driving the demand for medical equipment that incorporates sound accelerometer sensors for monitoring vital signs and assessing posture. The key players in the market include STMicroelectronics, Sonion, Vesper Technologies, Memsensing Microsys, Goertek, Bosch Sensortec, and Knowles, among others. These companies are investing in research and development to enhance the performance and functionality of their sensors, while also expanding their global reach through strategic partnerships and acquisitions.

Replication Data for: Application of Bayesian Additive Regression Tree to quantify the uncertainty of machine-learning derived variables: a case study in human activity patterns learned from accelerometer data. There are two datasets provided: accel_data_no_id.csv Features_reSampled_5sec.csv The files represent the raw data (accel_data_no_id.csv) and the analysis data resampled at 5 seconds (Features_reSampled_5sec.csv). Code for the analysis is available here (https://github.com/hiroshimamiya/BART_PhysicalActivity). Analysis was done on a cluster of the Digital Research Alliance of Canada (https://alliancecan.ca/en).

This multi-sensor gait dataset comprises inertial and optical motion data from 25 subjects free of lower-limb injury, aged between 18 and 47 years. A smartphone and an IMU were attached to one of the subject's legs to capture accelerometer data, and 42 reflexive markers were taped over the whole body to record three-dimensional trajectories. The participants were instructed to walk on a straight-level walkway at their normal pace. Ten trials for each participant were recorded and preprocessed in each of two sessions, performed on different days. This amounts to 500 trials of three-dimensional trajectories, 500 trials of accelerations from the IMU, and 500 trials of accelerations from the smartphone.

IntroductionAdolescents’ physical activity (PA) behavior can be driven by several psychosocial determinants at the same time. Most analyses use a variable-based approach that examines relations between PA-related determinants and PA behavior on the between-person level. Using this approach, possible coexistences of different psychosocial determinants within one person cannot be examined. Therefore, by applying a person-oriented approach, this study examined (a) which profiles regarding PA-related psychosocial variables typically occur in female sixth-graders, (b) if these profiles deliver a self-consistent picture according to theoretical assumptions, and (c) if the profiles contribute to the explanation of PA.Materials and MethodsThe sample comprised 475 female sixth-graders. Seventeen PA-related variables were assessed: support for autonomy, competence and relatedness in PE as well as their satisfaction in PE and leisure-time; behavioral regulation of exercise (five subscales); self-efficacy and social support from friends and family (two subscales). Moderate-to-vigorous PA was measured using accelerometers. Data were analyzed using the self-organizing maps (SOM) analysis, a cluster analysis including an unsupervised algorithm for non-linear models.ResultsAccording to the respective level of psychosocial resources, a positive, a medium and a negative cluster were identified. This superordinate cluster solution represented a self-consistent picture that was in line with theoretical assumptions. The three-cluster solution contributed to the explanation of PA behavior, with the positive cluster accumulating an average of 6 min more moderate-to-vigorous PA per day than the medium cluster and 10 min more than the negative cluster. Additionally, SOM detected a subgroup within the positive cluster that benefited from a specific combination of intrinsic and external regulations with regard to PA.DiscussionThe results underline the relevance of the assessed psychosocial determinants of PA behavior in female sixth-graders. The results further indicate that the different psychosocial resources within a given person do not develop independently of one another, which supports the use of a person-oriented approach. In addition, the SOM analysis identified subgroups with specific characteristics, which would have remained undetected using variable-based approaches. Thus, this approach offers the possibility to reduce data complexity without overlooking subgroups with special demands that go beyond the superordinate cluster solution.

This dataset consists of triaxial acceleration data linked to 143,081 observations. Acceleration data and observations were collected from 48 sheep split into 5 flocks. Each flock experienced a different feeding environment.

The feed and breed details of each flock:

Muresk Dry Pasture: Dry pasture at Muresk Institute, Western Australia (WA) (Hogget Merino ewes) Murdoch Green Pasture: Short-green pasture at Murdoch University Farm (Adult Suffolk ewes) Muresk Stubble: Stubble at Muresk Institute (Hogget Merino ewes) Katanning Green Pasture: Late winter pasture at a farm in Katanning, WA (Adult Merino ewes during lambing) Muresk Barley: Grazing cereal crop at Muresk Institute, WA (Adult Merino wethers)

The data file names and number of sheep of each flock:

Muresk Dry Pasture: 10 sheep, file names: 'Muresk Dry Pasture_X.csv' where X: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 Murdoch Green Pasture: 10 sheep, file names: 'Murdoch Green Pasture_X.csv' where X: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 Muresk Stubble: 10 sheep, file names: 'Muresk Stubble_X.csv' where X: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 Katanning Green Pasture: 9 sheep, file names: 'Katanning Green Pasture_X.csv' where X: 1, 2, 3, 4, 6, 7, 8, 9, 10 Muresk Barley: 9 sheep, file names: 'Muresk Barley_X.csv' where X: 2, 3, 4, 5, 6, 7, 8, 9, 10

The accelerometer data was recorded on Actigrapgh wGT3X-BT acceleration loggers that were sampling at 30Hz with a possible acceleration range of -8 to +8 G. The triaxial acceleration values recorded were in Gs. The Actigrapgh wGT3X-BT has a built-in anti-aliasing filter, so the resulting data does not need to be low pass-filtered to remove sampling artifacts.

The acceleration data was downloaded from the Actigrapgh devices as a gt3x file and software was developed to convert the gt3x file into a csv file where each row contained acceleration values for the x, y, z axes and a timestamp. As the sampling was at 30Hz, consecutive rows were ~33milli seconds apart.

All the sheep were videoed and then the videos were viewed and behaviour observations were recorded for 10 second periods. Software was written that linked the observations to their corresponding 10 seconds of acceleration data. The result is the set of files comprising the dataset. One file of observations and acceleration data for each of the 48 sheep.

Column Descriptions: study_name: the name of the flock sheep_number: identity of the sheep within a flock time_stamp: the time of the observation and the start of the 10 seconds of acceleration data sitting: whether the sheep is sitting or not standing: whether the sheep is standing or not walking: whether the sheep is walking or not grazing: whether the sheep is grazing or not ruminating: whether the sheep is ruminating or not steps: the number of steps taken by the sheep in the ten second epoch time_stood: the time that the sheep stood up from sitting time_sat: the time that the sheep transition from standing to sitting walk_time: the number of seconds within the 10 second epoch that the sheep was walking. Not 100% consistent or complete. x_0 -> x_299: 10 seconds of accelerometer data from the x axis y_0 -> y_299: 10 seconds of accelerometer data from the y axis z_0 -> z_299: 10 seconds of accelerometer data from the z axis

The accelerometers were attached to the right hand side of the sheep's jaw using a halter.

The global piezoelectric accelerometer sensor market, valued at $567.4 million in 2025, is projected to experience robust growth, driven by increasing demand across diverse sectors. The compound annual growth rate (CAGR) of 4.4% from 2025 to 2033 indicates a steadily expanding market, fueled by several key factors. The automotive industry's push for advanced driver-assistance systems (ADAS) and electric vehicles (EVs), requiring precise vibration and acceleration measurement, significantly contributes to market expansion. Similarly, the burgeoning industrial automation sector, encompassing robotics and smart manufacturing, relies heavily on these sensors for real-time monitoring and process optimization. Furthermore, growth in the aerospace and defense sectors, where accurate vibration analysis is crucial for flight safety and equipment performance, further boosts market demand. Technological advancements leading to smaller, more efficient, and cost-effective piezoelectric accelerometer sensors also contribute to market growth. Competitive landscape analysis reveals key players like Kistler, PCB Piezotronics, Brüel & Kjær, and others constantly innovating to improve sensor performance and expand their market share. However, certain restraints may influence the market's trajectory. The high initial investment costs associated with implementing advanced sensor technologies might deter some smaller companies. Moreover, the need for specialized expertise in sensor integration and data analysis can create a barrier to entry for some potential users. Despite these challenges, the long-term prospects for the piezoelectric accelerometer sensor market remain positive, driven by continued technological progress, expanding applications across multiple sectors, and the ongoing need for precise vibration and acceleration measurement. Market segmentation analysis, while not explicitly provided, likely reflects variations in sensor type, application, and end-user industry, offering further avenues for growth and market diversification.

The Accelerometer Sensor Board market has emerged as a vital component across various industries, driven by the increasing demand for precise motion detection and measurement solutions. These sensor boards, equipped with accelerometers, play a crucial role in applications ranging from consumer electronics to automot