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The dataset that we published in this data repository can be used to build neural networks-based inverse kinematics for NAO robot arms. This dataset is named ARKOMA. ARKOMA is an acronym for ARif eKO MAuridhi, all of whom are the creators of this dataset. This dataset contains input-output data pairs. In this dataset, the input data is the end-effector position and orientation, while the output data is a set of joint angular positions. For further applications, this dataset was split into the training dataset, validation dataset, and testing dataset.
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608 Views
Extended reality (XR) head-mounted displays (HMDs) are increasingly starting to rely on wireless task
offloading in a bid to allow unobstructed XR user movement, while still rendering high-resolution video on
a remote processing node. An example is the Oculus (Meta) Quest 2. However, congestion and reliability
issues associated with the wireless network can cause high latency and an overall low quality of service (QoS).
Therefore, understanding XR user mobility is of vital importance for supporting XR applications in future
wireless networks.
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180 Views
ommon approaches to stunting prediction, including statistical analysis and machine learning, have poor performance due to shifts in the factors influencing stunting. Causes data cannot be integrated directly when using statistical analysis. At the same time, machine learning causes a decrease in predictive performance down over time. This study proposes a new approach to stunting prediction in infants and toddlers aged 0-5 years using continuous learning methods.
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69 Views
The uploaded dataset appears to be related to various composite materials, and it includes the following columns:
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359 Views
The published Surface Electromyography (sEMG) dataset was captured under the supervision of trained physiotherapists and doctors at Mayo Hospital Lahore and National University of Sciences & Technology by following two months long experimental protocol.
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971 Views
Sentiment analysis, which aims to identify the positive or negative tone of a given text, has seen a surge in interest over the past two decades, making it one of the most studied areas of study in the fields of Natural Language Processing and Information Extraction. Due to the ambiguous nature of sarcasm, however, sarcasm detection is an essential part of sentiment analysis. The task becomes exceedingly challenging when applied to a language with a more intricate morphology and a lack of available resources, such as Telugu.
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734 Views
A recent study [1] alerts on the limitations of evaluating anomaly detection algorithms on popular time-series datasets such as Yahoo, Numenta, or NASA, among others. In particular, these datasets are noted to suffer from known flaws suchas trivial anomalies, unrealistic anomaly density, mislabeled ground truth, and run-to-failure bias. The TELCO dataset corresponds to twelve different time-series, with a temporal granularity of five minutes per sample, collected and manually labeled for a period of seven months between January 1 and July 31, 2021.
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668 Views
Wireless underground sensor networks (WUSNs) promise to deliver substantial social and economic benefits across different verticals. However, many of the relevant application scenarios are located in remote areas with no supporting infrastructure available. To address this challenge, we conceptualize in this study the underground direct-to-satellite (U-DtS) connectivity approach, implying the reception of the signals sent by the underground massive machine-type communication (mMTC) sensors by the gateways operating on the low Earth orbit satellites.
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111 Views
We conducted a randomized controlled clinical trial to evaluate the efficacy of a brain-computer interface ( BCI ) -based visual and motor feedback motor imagery therapy system on cognitive, psychological and limb movement in hemiplegic stroke patients. We recruited more than 100 patients and randomly divided them into three groups : conventional treatment group, MI group and MI group based on brain-computer interface. The data set contains the evaluation data of these three groups of patients before and after treatment.
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345 Views
This dataset includes data from 43 hospitalized patients with type 2 diabetes mellitus. Dexcom G5 and Dexcom G6 mobile continuous glucose measurement (CGM) devices were used to measure continuous blood glucose levels. The data collection period is from April 2019 to January 2022. We selected 43 patients whose records with a more extended recording period of more than seven days. Data was collected for 7 to 10 days at 5-minute intervals. This data can be used for glucose level prediction or hypoglycemia occurrence prediction for patient with type 2 diabetes mellitus.
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1335 Views