MYNursingHome: A fully-labelled image dataset for indoor object classification.

Ismail, A. S.; Ahmad, Siti Anom; Soh, Azura Che; Hassan, Mohd Khair; Harith, Hazreen Haizi

doi:10.1016/j.dib.2020.106268

Cited by 9 publications

(7 citation statements)

References 3 publications

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“…This indicates that the proposed system is generally applicable to everyday scenarios but may not be sufficient for scenarios that are either unusual or are very specific, which will therefore require the inclusion of a specific data set tailored to the target scenario. For instance, there exist databases specifically targeted at indoor objects (Bashiri et al, 2018 ; Damen et al, 2018 ; Ismail et al, 2020 ; Samani et al, 2021 ), which contain a wider variety of object types and a greater number of images for each object. Also, depending on the analysis scenarios, training can be tailored to specific situations or specific types of objects using open image databases widely available at present, such as groceries (Klasson et al, 2019 ), documents (Antonacopoulos et al, 2009 ), or objects in low lighting conditions (Loh & Chan, 2019 ).…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Deep-SAGA: a deep-learning-based system for automatic gaze annotation from eye-tracking data

2022

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With continued advancements in portable eye-tracker technology liberating experimenters from the restraints of artificial laboratory designs, research can now collect gaze data from real-world, natural navigation. However, the field lacks a robust method for achieving this, as past approaches relied upon the time-consuming manual annotation of eye-tracking data, while previous attempts at automation lack the necessary versatility for in-the-wild navigation trials consisting of complex and dynamic scenes. Here, we propose a system capable of informing researchers of where and what a user’s gaze is focused upon at any one time. The system achieves this by first running footage recorded on a head-mounted camera through a deep-learning-based object detection algorithm called Masked Region-based Convolutional Neural Network (Mask R-CNN). The algorithm’s output is combined with frame-by-frame gaze coordinates measured by an eye-tracking device synchronized with the head-mounted camera to detect and annotate, without any manual intervention, what a user looked at for each frame of the provided footage. The effectiveness of the presented methodology was legitimized by a comparison between the system output and that of manual coders. High levels of agreement between the two validated the system as a preferable data collection technique as it was capable of processing data at a significantly faster rate than its human counterpart. Support for the system’s practicality was then further demonstrated via a case study exploring the mediatory effects of gaze behaviors on an environment-driven attentional bias.

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Section: Conclusion and Discussionmentioning

confidence: 99%

Deep-SAGA: a deep-learning-based system for automatic gaze annotation from eye-tracking data

2022

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“…For example, the MCIndoor2000 [223] dataset includes 2055 images of three object classes including doors, stairs, and hospital signs. The MYNursingHome [224] dataset focuses on object classification and detection in nursing homes, containing 37,500 images featuring objects commonly found in elderly home care centers, such as toilet seats, tables, and wheelchairs. The Hospital Indoor Object Detection (HIOD) dataset comprises 4417 images covering 56 object categories, including items like surgical lights, IV poles, and bedside monitors, with a total of 51,869 annotations.…”

Section: Computer Vision Datasetsmentioning

confidence: 99%

Applied Artificial Intelligence in Healthcare: A Review of Computer Vision Technology Application in Hospital Settings

Lindroth,

Nalaie,

Raghu

et al. 2024

J. Imaging

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Computer vision (CV), a type of artificial intelligence (AI) that uses digital videos or a sequence of images to recognize content, has been used extensively across industries in recent years. However, in the healthcare industry, its applications are limited by factors like privacy, safety, and ethical concerns. Despite this, CV has the potential to improve patient monitoring, and system efficiencies, while reducing workload. In contrast to previous reviews, we focus on the end-user applications of CV. First, we briefly review and categorize CV applications in other industries (job enhancement, surveillance and monitoring, automation, and augmented reality). We then review the developments of CV in the hospital setting, outpatient, and community settings. The recent advances in monitoring delirium, pain and sedation, patient deterioration, mechanical ventilation, mobility, patient safety, surgical applications, quantification of workload in the hospital, and monitoring for patient events outside the hospital are highlighted. To identify opportunities for future applications, we also completed journey mapping at different system levels. Lastly, we discuss the privacy, safety, and ethical considerations associated with CV and outline processes in algorithm development and testing that limit CV expansion in healthcare. This comprehensive review highlights CV applications and ideas for its expanded use in healthcare.

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“…The foremost goal of this research is to predict real-world object recognition in federated learning using the proposed FP-SFOA-DQN-FL. The overall process will be explained as follows: initially, the dataset from [24] are fed as input to the device at the time, and the appropriate local training is achieved based on the local data at every node.…”

Section: Proposed Fp-sfoa-dqn-fl For Real-world Object Recognition In Flmentioning

confidence: 99%

“…The process of object recognition is performed at the training model of each device. The first step is to acquire the indoor images from a specific dataset field [24], and it is then pre-processed using a bilateral filter for the purpose of discarding the noises. The objectdetection process is successfully accomplished through SegNet, which is trained using the designed PSFOA, and it is the combination of PO and SFOA.…”

Section: Training Modelmentioning

confidence: 99%

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Deep Q Network Based on a Fractional Political–Smart Flower Optimization Algorithm for Real-World Object Recognition in Federated Learning

Soomro,

Fu,

Aslam

et al. 2023

Applied Sciences

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An imperative application of artificial intelligence (AI) techniques is visual object detection, and the methods of visual object detection available currently need highly equipped datasets preserved in a centralized unit. This usually results in high transmission and large storage overheads. Federated learning (FL) is an eminent machine learning technique to overcome such limitations, and this enables users to train a model together by processing the data in the local devices. In each round, each local device performs processing independently and updates the weights to the global model, which is the server. After that, the weights are aggregated and updated to the local model. In this research, an innovative framework is designed for real-world object recognition in FL using a proposed Deep Q Network (DQN) based on a Fractional Political–Smart Flower Optimization Algorithm (FP-SFOA). In the training model, object detection is performed by employing SegNet, and this classifier is effectively tuned based on the Political–Smart Flower Optimization Algorithm (PSFOA). Moreover, object recognition is performed based on the DQN, and the biases of the classifier are finely optimized based on the FP-SFOA, which is a hybridization of the Fractional Calculus (FC) concept with a Political Optimizer (PO) and a Smart Flower Optimization Algorithm (SFOA). Finally, the aggregation at the global model is accomplished using the Conditional Autoregressive Value at Risk by Regression Quantiles (CAViaRs) model. The designed FP-SFOA obtained a maximum accuracy of 0.950, minimum loss function of 0.104, minimum MSE of 0.122, minimum RMSE of 0.035, minimum FPR of 0.140, maximum average precision of 0.909, and minimum communication cost of 0.078. The proposed model obtained the highest accuracy of 0.950, which is a 14.11%, 6.42%, 7.37%, and 5.68% improvement compared to the existing methods.

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MYNursingHome: A fully-labelled image dataset for indoor object classification.

Cited by 9 publications

References 3 publications

Deep-SAGA: a deep-learning-based system for automatic gaze annotation from eye-tracking data

Deep-SAGA: a deep-learning-based system for automatic gaze annotation from eye-tracking data

Applied Artificial Intelligence in Healthcare: A Review of Computer Vision Technology Application in Hospital Settings

Deep Q Network Based on a Fractional Political–Smart Flower Optimization Algorithm for Real-World Object Recognition in Federated Learning

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