Data Record Research Articles

An Evaluation of Sentinel-3 SYN VGT Products in Comparison to the SPOT/VEGETATION and PROBA-V Archives

Sentinel-3 synergy (SYN) VEGETATION (VGT) products were designed to provide continuity to the SPOT/VEGETATION (SPOT VGT) base products archive. Since the PROBA-V mission acted as a gap filler between SPOT VGT and Sentinel-3, and in principle, a continuous series of data products from the combined data archives of SPOT VGT (1998–2014), PROBA-V (2013–2020) and Sentinel-3 SYN VGT (from 2018 onwards) are available to users, the consistency of Sentinel-3 SYN VGT with both the latest SPOT VGT (VGT-C3) and PROBA-V (PV-C2) archives is highly relevant. In past years, important changes have been implemented in the SYN VGT processing baseline. The archive of SYN VGT products is therefore intrinsically inconsistent, leading to different consistency levels with SPOT VGT and PROBA-V throughout the years. A spatio-temporal intercomparison of the combined time series of VGT-C3, PV-C2 and Sentinel-3 SYN VGT 10-day NDVI composite products with an external reference from LSA-SAF, and an intercomparison of Sentinel-3 SYN V10 products with a climatology of VGT-C3 resp. PV-C2 for three distinct periods with different levels of product quality have shown that the subsequent processing baseline updates have indeed resulted in better-quality products. It is therefore essential to reprocess the entire Sentinel-3 SYN VGT archive; a uniform data record of standard SPOT VGT, PROBA-V and Sentinel-3 SYN VGT products, spanning over 25 years, would provide valuable input for a wide range of applications.

More transparent and explainable machine learning algorithms are required to provide enhanced and sustainable dataset understanding

For detailed dataset interrogation and auditing purposes the lack of dataset explainability/transparency of the majority of available machine-learning (ML) models poses limitations. There is a tendency for ML models to focus on prediction speed and accuracy at the expense of transparently revealing dataset relationships. A case is made here to broaden that focus and for ML models to offer alternative configurations tailored to provide more explanations about how individual predictions are derived. Indeed, those striving to achieve sustainable objectives should not rely on opaque ML models and seek transparency as a fundamental objective of good modelling practice (GMP). Doing so tends to boost trust and confidence in the outputs of models relating to complex socio-environmental systems (SES), particularly those being used to potentially justify controversial social, political and ethical decisions. Currently, the transparent open box algorithms (TOB) are the only ML algorithms available that are configured specifically to routinely provide detailed data record relationships for each of their predictions. This study describes the data mining benefits of the Python-coded optimized data-matching TOB algorithms generally, and when applied to environmental datasets characterized by complex non-linear relationships involving many variables.

Validation of a data logger for measuring standing time and seat angles for community-based standing devices

Context Little is understood about community-based standing device use and the impact of standing on health outcomes (e.g. pressure injury) in those living with spinal cord injury (SCI). This project reports on the accuracy of a commercially available data logger for measuring standing time and seat angle. Methods A standing frame and a standing manual wheelchair were each instrumented with a commercially available data logger and each was tested by an non-disabled participant. Standing time in the standing frame was calculated from the data logger and compared to a user-recorded standing log over a two-month period in a laboratory environment. The standing wheelchair’s seat angle was calculated using motion capture and compared to the calculated seat angle from the data logger. Average seat interface pressures were also captured during the testing of the standing wheelchair. Results The data logger demonstrated high accuracy (99.99999%) in classifying sitting and standing in the standing frame when compared to a user-recorded standing log. The wheelchair seat angle calculated from the data logger demonstrated a high level of agreement with the motion lab calculations of seat angle (ICC = 0.96 (0.95, 0.97)). The data logger seat angle results also demonstrated strong relationships to average seat pressure and rear dispersion index, measures relevant to pressure injuries. Conclusions The data logger appears to be an appropriate tool for determining standing time and seat angle in standing devices, which may aid clinicians and researchers to better understand the use and impact of standing technologies on health outcomes.

Studying the Flow Experience in Computer-Supported Collaborative Learning

Computer-Supported Collaborative Learning (CSCL) is recognized as an effective methodology for fostering social interaction mediated by technology in ways that potentially trigger learning. The successful implementation of CSCL hinges on factors such as the scripting mechanics for activity sequencing proposed by Collaborative Learning Flow Patterns (CLFP). Yet, research in CSCL scripts has not studied if CLFPs achieves the so-called notion of “flow experience,” defined as an optimal state in which individuals are engaged and absorbed in an activity. This study proposes an approach to measure flow in the case of the Pyramid CLFP and studies the factors that influence the flow experience in the PyramidApp tool. The study tests a model that uses analysis of the Flow Short Scale and data logs. The findings show that there is a relationship between factors such as the speed of individual contributions and active participation in groups with the flow experience. Notably, the quantity of participation does not exhibit a discernible impact on the flow. The study emphasizes the interest of the modelled factors and the proposed approach for learning analytics to understand the flow experience in CLFP implementations.

Nonlinear Analysis and Closed-Form Solution for Overhead Line Magnetic Energy Harvester Behavior

Recently, much attention has been given to the development of various energy harvesting technologies to power remote electronic sensors, data loggers, and communicators that can be installed on smart grid systems. Magnetic energy harvesting is, perhaps, the most straightforward way to capture a significant amount of power from a current-carrying overhead line. Since the harvester is expected to have a small size, the high currents of the distribution system easily saturate its magnetic core. As a result, the operation of the magnetic harvester is highly nonlinear and makes precise analytical modeling difficult. The operation of an overhead line magnetic energy harvester (OLMEH) generating significant DC power output into a constant voltage load was investigated in this paper. The analysis method was based on the Froelich equation to analytically model the nonlinearity of the core’s BH characteristic. The main findings of this piecewise nonlinear analysis include a closed-form solution that accounts for both the core and rectifiers’ nonlinearities and provides an accurate prediction of OLMEH transfer window length, output current, and harvested power. Continuous and discontinuous operational modes are identified and the mode transition boundary is obtained quantitatively. The theoretical investigation was concluded by comparison with a computer simulation and also verified by the experimental results of a laboratory prototype harvester. A good agreement was found.

An Approach for Estimating the Contributions of Various Real-World Usage Conditions towards the Attained Utility Factor of Plug-in Hybrid Electric Vehicles

Plug-in hybrid electric vehicles (PHEVs) are designed to enable the electrification of a large portion of the distance vehicles travel while utilizing relatively small batteries via taking advantage of the fact that long-distance travel days tend to be infrequent for many vehicle owners. PHEVs also relieve range anxiety through seamless switching to hybrid driving—an efficient mode of fuel-powered operation—whenever the battery reaches a low state of charge. Stemming from the perception that PHEVs are a well-rounded solution to reducing greenhouse gas (GHG) emissions, various metrics exist to infer the effectiveness of GHG reduction, with utility factor (UF) being prominent among such metrics. Recently, articles in the literature have called into question whether the theoretical values of UF agree with the real-world performance of PHEVs, while also suggesting that infrequent charging was the likely cause for observed deviations. However, it is understood that other reasons could also be responsible for UF mismatch. This work proposes an approach that combines theoretical modeling of UF under progressively relaxed assumptions (including the statistical distribution of daily traveled distance, charging behavior, and attainable electric range), along with vehicle data logs, to quantitatively infer the contributions of various real-world factors towards the observed mismatch between theoretical and real-world UF. A demonstration of the proposed approach using data from three real-world vehicles shows that all contributing factors could be significant. Although the presented results (via the small sample of vehicles) are not representative of the population, the proposed approach can be scaled to larger datasets.

A photonic sensor system for real‐time monitoring of turbidity changes in aquaculture

AbstractObjectiveThe objective of this study was to test the compatibility and performance of a developed photonic sensor system, which can serve as a dependable and practical device for continuous monitoring of turbidity changes in aquaculture tanks.MethodsThe fabricated photonic sensor system consisted of an integrated data logger and sensor probe. The sensor probe exhibited a precise emission of infrared light at a wavelength of 850 nm. Moreover, the sensor evaluates the ambient light across the red‐green‐blue spectrum. To ensure accuracy and reliability, the entire system underwent a thorough calibration process, referencing nephelometric turbidity unit values acquired through a specialized handheld turbidimeter. Rigorous trials were systematically conducted in 600‐L seawater tanks featuring tubular sea cucumber Holothuria tubulosa and Gilt‐head Sea Bream Sparus auratus to ensure the sensitivity and robustness of the photonic sensor system to the aquaculture environment.ResultA calibration curve revealed a significant correlation between the infrared channel values of the sensor (photon counts) and the turbidity values measured by the turbidimeter. The photonic sensor effectively captured turbidity changes in the aquaculture tanks, with significant differences observed between the tanks. The sensor performance was evaluated in trials with Gilt‐head Sea Bream, which showed sensitivity to high turbidity changes. The photonic sensor system accurately reflects turbidity changes continuously using its own active light source, independent of ambient light intensity, which is essential for turbid water conditions or for taking measurements in total darkness.ConclusionThe photonic sensor is a reliable tool for the continuous and accurate monitoring of turbidity changes in aquaculture systems. However, there are specific usage limitations under low‐turbidity conditions that can be improved in further studies.

The diet and diving behaviour of the ringed seal (Pusa hispida) in Kangia (Ilulissat Icefjord), Greenland

AbstractThis study uses telemetry and stomach content analysis to describe the ringed seals’ dive pattern and prey selection within Kangia (Ilulissat Icefjord), Greenland, where the ringed seals are known to be exceptionally large and genetically distinct. Data from twenty-four ringed seals (Pusa hispida) tagged in Kangia, with satellite relay data loggers from 2012 to 2020, were used to describe the dive patterns. The transmitters were active between 28 and 270 days and showed that subadults most frequently dove to around 150 m. Adult males frequently dove to this depth, but they also had a second frequently visited depth at around 400 m, some adults significantly deeper. Adult males generally dove deeper than females and adult seals showed deeper dives during the winter, a feature that was less prominent in subadults. The stomach content from ringed seals (n = 108) harvested in Kangia by subsistence hunters revealed that polar cod (Boreogadus saida), capelin (Mallotus villosus), mysids (Mysis oculata, M. littoralis, M. Polaris) and shrimps (mainly Pandalus borealis) were the dominant prey. Shrimps were observed more often in adults, likely due to the frequently visited dive depth of around 400 m, whereas mysids were more observed in small subadult seals. The stomach content from harp seals (Pagophilus groenlandicus) and Greenland halibut (Reinhardtius hippoglossoides) caught in Kangia also showed a strong niche overlap with the ringed seals. These findings outline the unique ecological adaptations and the importance of Kangia as a critical habitat of ringed seals.

Evidence for SARS-CoV-2 infected Golden Syrian hamsters (Mesocricetus auratus) reducing daily energy expenditure and body core temperature

Golden Syrian hamsters (Mesocricetus auratus) are a well-established animal model for human infections with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) due to their susceptibility to SARS-CoV-2 infection, robust virus replication and pathological manifestations similar to human COVID-19 pneumonia. To investigate the physiological changes upon infection in this animal model, we explored the alterations in daily energy expenditure (DEE), water turnover, body mass, body temperature, and locomotor activity in non-infected and SARS-CoV-2 infected Golden Syrian hamsters for four days post SARS-CoV-2. DEE was measured using the doubly labelled water method, which allows for the accurate estimation of carbon dioxide production and, consequently, energy expenditure in animals. Additionally, we investigated total water intake (TWI), which comprises drinking water, preformed water in food, and metabolic water. Using intraperitoneally implanted data loggers, we also monitored body core temperature and locomotor activity in some of the animals. Here we provide evidence for infected hamsters exhibiting significantly lower DEE and TWI compared to non-infected animals. We also observed an increase in body weight in the non-infected animals, while infected animals experienced weight loss. Further, infected animals showed a significantly decreased body temperature, indicating a generally lowered metabolic rate.

Evaluations of ground surface heat fluxes and its potential vegetation effects in the permafrost region of northeastern China

Ground surface-air temperature difference (GATD) is the primary source of sensible heat flux from the ground surface, and vegetation inevitably affects it by altering the ground surface characteristics. However, the relationship between vegetation and GATD in the permafrost region has not been well-elucidated. Using meteorological station data, National Oceanic and Atmospheric Administration Climate Data Record Normalized Difference Vegetation Index (NOAA CDR NDVI) data, and some auxiliary data, this study aims to investigate the patterns of GATD in the Northeast China permafrost region and quantify the response of GATD to vegetation changes. The study employs trend analysis, Geodetector, and correlation analysis methods. The results show that the GATD in the Northeast China permafrost region decreased from west to east and the annual mean GATD exhibited an upward from 1982 to 2020. The relationship between NDVI and GATD trend is non-linear, in areas with low vegetation, vegetation changes amplify the GATD trend, while in areas with high vegetation, vegetation changes inhibit the GATD trend. Furthermore, the rate of GATD increase is higher in the discontinuous permafrost (DP) region than in the sporadic permafrost (SP) and isolated permafrost (IP) regions, suggesting that the DP region may have experienced a faster ground surface state change process. The findings of this study will provide new insights for evaluating permafrost degradation and studying the resulting ecological environmental effects in the permafrost region.

Identification of flame transfer functions during transient operation

Predicting thermoacoustic instabilities requires knowledge of the flame response to acoustic perturbations, which is characterized by the Impulse Response Function. Obtaining stability maps across an extensive parameter space is prohibitively costly with existing methods, both experimentally and numerically, as the data records must be obtained independently for each stationary operating condition. To address this problem, a nonparametric identification technique that enables the characterization of instantaneous Flame Transfer Functions (FTFs) under nonstationary operating conditions, based on a series expansion of the time-varying impulse response function (TV-IRF), is proposed. The technique is a direct extension of the Wiener-Hopf equation to nonstationary signals, yielding the linear minimum mean squared error estimator (LMMSEE) of the TV-IRF. This method is applied to data obtained from a model of a premixed, hydrogen-enriched swirl flame. It's hydrogen power fraction is rapidly varied from 12-43% over 100 milliseconds and the flame response to a band-limited, white-noise signal is computed. From the resulting input-output data, the TV-IRF is accurately estimated and the FTF for all hydrogen power fractions is obtained from a single data record. This method paves the way for cost-effective estimation of a map of FTF's from computational or experimental data, significantly reducing expenses in both cases.

Microclimate and the spatio-temporal dynamics of natural production of two ectomycorrhizal fungi from the Guineo-Sudanian ecozone in Benin

The study aims to determine the relation between the micro-climate and the natural production of Amanita subviscosa, and Lactifluus gymnocarpoides, in the Guinea-Sudanian ecozone. Nine permanent plots of 50 ×50 m were installed in three different vegetations of the “Ouémé Supérieur” forest reserve in central Benin. The mycological surveys were carried out during three (3) mycological seasons, each from June to October at a frequency of two visits per week and per plot. The number of fruiting bodies and the fresh biomass were recorded. Microclimatic parameters (soil water content, air temperature, soil temperature, relative humidity and precipitation) were measured using a rain gauge and a HOBO micro station data logger installed at the middle of each plot. We tested several Generalized Linear Mixed Effects Models (GLMM) using the lme4 package to obtain the effect of recorded microclimate factor on the number of fruit bodies of each species and the Linear Mixed Effects Model (LMM) using the package nlme for fresh biomass. The study showed that soil water content is the main driver of fungal fruiting phenology and increased biomass production. The fruiting of A. subviscosa is positively affected by air temperature and soil water content (p = 0.0004) but negatively affected by relative air humidity. The fructification of L. gymnocarpoides is only positively affected by the air temperature and negatively affected by relative air humidity and soil water content. Soil water content significantly affects the fresh biomass of A. subviscosa (p≈0) while the fresh biomass of L. gymnocarpoides is significantly affected by soil water content and relative air humidity (p≈0). All species for their natural production have specific response at microclimate parameters variation.

Effectiveness of Use Partograph during Labour among Staff Nurses

Events such as pregnancy and childbirth affect almost every fact of the human experience. Pregnancy alters a woman's body and mind. A partogram, also known as a partograph, is a single sheet of paper that has a composite graphical record of important labor-related data (fetal and maternal) entered against time. Statistics like cervical dilation, fetal heart rate, labor time, and vital signs are examples of pertinent metrics. The partograph is an invaluable tool for identifying indicators of maternal anxiety, irregular labor progress, and fetal distress. The partograph is intended to record the following: the mother's identity, the fetal heart rate, the color of the amniotic fluid, the fetal skull's molding, cervical dilatation, the fetal descent, the uterine contractions, and whether oxytocin was given or not.  Objectives: Determine staff nurses in the experimental and control groups' degree of expertise & efficiency about using partograph during labor.  Methodology:  Research Design: True experimental research design used in study,  Sampling technique & Population. . A basic random sample procedure was used to choose the 60 staff nurses for the study (30 in the experimental group and 30 in the control group). A study was conducted using a standardized interview schedule. .  Result: In experimental group of staff nurses had 70% of adequate knowledge about partograph in post test and in control group 30 % of staff nurses only have adequate knowledge about partograph in post test .  Conclusion The study helped the researcher to finalize the importance & use of partograph in labour among staff nurses in progress of labour. partograph is an invaluable tool for identifying indicators of maternal anxiety, irregular labor progress, and fetal distress.

Stability of cloud detection methods for Land Surface Temperature (LST) Climate Data Records (CDRs)

The stability of a climate data record (CDR) is essential for evaluating long-term trends in surface temperature using remote sensing products. In the case of a satellite-derived CDR of land surface temperature (LST), this includes the stability of processing steps prior to the estimation of the target climate variable. Instability in the masking of cloud-affected observations can result in non-geophysical trends in a LST CDR. This paper provides an assessment of cloud detection performance stability over a 25-year LST CDR generated using data from the second Along-Track Scanning Radiometer (ATSR-2), the Advanced Along-Track Scanning Radiometer (AATSR), the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Sea and Land Surface Temperature Radiometer (SLSTR). We evaluate three cloud detection methodologies, one fully Bayesian, one naïve probabilistic and the operational threshold-based cloud mask provided with each sensor, at four in-situ ceilometer sites. Of the 12 algorithm-site combinations assessed, only two (17 %) were stable across the full timeseries with respect to both cloud contamination and missed clear-sky observations. Five (42 %) were stable with respect to missed clear-sky observations only. The associated impacts on LST trends in the CDR could be as large as (+/−)0.73 K per decade (0.43 K per decade above the target stability), which means that attention needs to be paid to this aspect of stability in order to understand uncertainty in long-term observed trends. Given that cloud detection stability has not to our knowledge been previously assessed for any target climate variable, this conclusion may apply more broadly to other satellite-derived CDRs.

How realistic are multi-decadal reconstructions of GRACE-like total water storage anomalies?

Reconstructions allow us to extend the Gravity Recovery And Climate Experiment (GRACE) data record into the past and bridge the one-year gap between GRACE and its successor, GRACE-FO (Follow on). Reconstructed total water storage anomalies (TWSA) are obtained by identifying relations between GRACE-derived TWSA and climate variables via statistical and machine learning techniques. However, a comparative analysis of the characteristics and realism of reconstructions is missing.In this contribution, we close this gap by comparing three global reconstructions by Humphrey and Gudmundsson (2019), Li et al. (2021) and Chandanpurkar et al. (2022) mutually and against output from the Water Global Analysis and Prognosis (WaterGAP) hydrological model from 1979 onwards, against large-scale mass-change derived from geodetic satellite laser ranging (SLR) from 1992 onwards, and finally against differing GRACE and GRACE-FO solutions from 2002 onwards. The reconstructions vary regarding design and trained GRACE solution.Reconstructions recover the TWSA signal for humid climate regions but disagree for arid climate regions, which is evident on the inter-annual timescales. At seasonal and sub-seasonal timescales, the reconstructions agree surprisingly well in many regions. Our comparison against independent SLR data reveals that reconstructions (only) partially succeed in representing anomalous TWSA for areas that are influenced by significant climate modes such as El Niño-Southern Oscillation (ENSO).

Calibration and Release of Magnetograms/Dopplergrams Obtained at the Mt. Wilson 150-Foot Tower Telescope (MWO)

The Mt. Wilson Observatory archive of observations of solar disk magnetic fields, Doppler velocities, and spectral line intensities is a resource for studying the Sun’s state from 1967 to 2013. Instrument changes/upgrades during this time must be considered when interpreting this record. Portions of this record have been previously released. This publication documents the data record in order to allow its independent interpretation. The archive is available through two directory trees which can be accessed at http://sha.stanford.edu/mwo/msm.html. The calibration of the observations is impacted by the solar surface convective flows, which produce offsets for both differential rotation and meridional circulation functions. The effects of these offsets have been reduced in this and other publications by temporal averaging.

Enhancing long-term vegetation monitoring in Australia: a new approach for harmonising the Advanced Very High Resolution Radiometer normalised-difference vegetation (NVDI) with MODIS NDVI

Abstract. Long-term, reliable datasets of satellite-based vegetation condition are essential for understanding terrestrial ecosystem responses to global environmental change, particularly in Australia, which is characterised by diverse ecosystems and strong interannual climate variability. We comprehensively evaluate several existing global Advanced Very High Resolution Radiometer (AVHRR) normalised-difference vegetation index (NDVI) products for their suitability for long-term vegetation monitoring in Australia. Comparisons with the MODIS NDVI highlight significant deficiencies, particularly over densely vegetated regions. Moreover, all the assessed products failed to adequately reproduce the interannual variability in the pre-MODIS era as indicated by Landsat NDVI anomalies. To address these limitations, we propose a new approach to calibrating and harmonising NOAA's Climate Data Record of AVHRR NDVI to the MODIS MCD43A4 NDVI for Australia using a gradient-boosting decision tree ensemble method. Two versions of the datasets are developed, one incorporating climate data in the predictors (“AusENDVI-clim”: Australian Empirical NDVI-climate) and another that is independent of climate data (“AusENDVI-noclim”). These datasets, spanning 1982–2013 at a spatial resolution of 0.05° and with a monthly time step, exhibit strong correlations (r2=0.89–0.94) and low mean errors compared with MODIS MCD43A4 NDVI (mean absolute error (MAE) = 0.014–0.028, RMSE = 0.021–0.046), accurately reproducing seasonal cycles over densely vegetated regions. Furthermore, they closely replicate the interannual variability in vegetation condition in the pre-MODIS era. A reliable method for gap-filling the AusENDVI record is also developed that leverages climate, atmospheric CO2 concentration, and woody-cover fraction predictors. The resulting synthetic NDVI dataset shows excellent agreement with the MODIS MCD43A4 NDVI and the recalibrated AVHRR NDVI time series (r2=0.82–0.95, MAE = 0.016–0.029, RMSE = 0.039–0.041). Finally, we provide a complete 41-year dataset where the gap-filled AusENDVI-clim from January 1982 to February 2000 is joined with the MODIS MCD43A4 NDVI from March 2000 to December 2022. Analysing 40-year per-pixel trends in Australia's annual maximum NDVI revealed increasing values, and shifts in the timing, of the annual peak NDVI across most of the continent, underscoring the dataset's potential to address crucial questions regarding the changing vegetation phenology and its drivers. The AusENDVI dataset can be used for studying Australia's changing vegetation dynamics and downstream impacts on the terrestrial carbon and water cycles, and it provides a reliable foundation for further research into the drivers of vegetation change. AusENDVI is open access and available at https://doi.org/10.5281/zenodo.10802703 (Burton et al., 2024).

Utilizing Data Mining Techniques to Predict Students Performance using Data Log from MOODLE

Utilizing Data Mining Techniques to Predict Students Performance using Data Log from MOODLE

Economic assessment and sustainable management of artisanal fisheries: Insights from River Jhelum, Kashmir, India

This study was conducted to comprehensively assess artisanal fishers’ economic and financial viability, relying heavily on a shared resource, namely the River Jhelum. Employing a proportional multi-stage stratified sampling approach, a total of 200 fishers were interviewed across six districts in Kashmir, covering the entire length of the river. The Benefit Cost Ratio (BCR) of 1.1, coupled with a positive Net Present Value (NPV), underscores the economic profitability of artisanal fisheries. Constraint analysis highlighted that fishermen express substantial concerns regarding the ongoing decline in both catch and the composition (Garret score: 83) of native fish species, particularly Schizothorax, and pollution in the river (Garret score: 73). Considering the perspectives of all stakeholders, it becomes evident that comprehensive sustainability measures are imperative. To achieve this, multiple stakeholders must collaborate and establish a unified administrative body, solely dedicated to the protection of riverine resources. In addition to implementing a two-month fishing ban and modifying the licensing policy, maintaining a detailed record of daily fish catches, including both biological and economic data, at each fishing site is also crucial. Such actions will facilitate the implementation of optimal exploitation levels for this invaluable inland water resource. Keywords: Constraint analysis, Economic performance, Financial feasibility, Net present value, Schizothorax, Sustainability

Application of Sensor Technology in Wheelchair Sports for Real-Time Data Collection during Training and Competition and for Assessment of Performance Parameters: A Systematic Review and Future Directions.

This review reports on the use of sensors in wheelchair sports to monitor and analyze performance during match and training time. With rapid advancements in electronics and related technologies, understanding performance metrics in wheelchair sports is essential. We reviewed nine studies using various sensor types, including electric motors, inertial measurement units, miniaturized data loggers with magnetic reed switches, and smartphones with inbuilt accelerometers and gyroscopes, operating at frequencies from 8 Hz to 1200 Hz. These studies measured parameters such as angular and translational velocities, distance, number of starts/pushes, and other performance indicators in sports such as basketball, rugby, tennis, and racing. Despite differences in sport types and methodologies, most studies found sensor-derived data effective for assessment of performance. Future developments and research in this field should focus on multi-sensor systems that could provide real-time match analysis and deeper insights into performance metrics. Overall, sensor technologies show significant potential for improving wheelchair sport performance diagnostics, contributing to better athlete training and future wheelchair design, and enhancing competitive outcomes. This review emphasizes the need for continued innovation and standardization in applying sensor technologies in wheelchair sports.