Understanding the Lifestyle of Older Population: Mobile Crowdsensing Approach

Marakkalage, Sumudu Hasala; Lau, Billy Pik Lik; Viswanath, Sanjana Kadaba; Balasubramaniam, Thirunavukarasu; Yuen, Chau; Yuen, Belinda; Luo, Jianxi; Nayak, Richi

doi:10.1109/tcss.2018.2883691

Cited by 40 publications

(18 citation statements)

References 19 publications

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“…Then without performing any retraining or fine-tuning, just use the inference to extract features using the feature extractors mentioned in above. Then perform the PCA on extracted features and reduce the dimensionality up to three components [31]. Thereafter, features are visualized on the calculated principal component space.…”

Section: B Feature Extractionmentioning

confidence: 99%

Feature Learning and Analysis for Cleanliness Classification in Restrooms

et al. 2019

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In order to revamp the cleaning contract from the head-count basis into a performance basis, a fair and unbiased cleanliness classification is necessary. However, the perception of cleanliness is very subjective to the observer. Hence, it is not an easy task to quantify the cleanliness. This paper presents an application of principal component analysis (PCA) in conjunction with convolutional neural networks (CNN) to identify the cleanliness of a restroom up to three levels; namely, dirty, average, and clean. The proposed method includes an application specific data augmentation algorithm and a PCA-based feature analysis schema to select the best suited CNN model for our dataset. Since this study focused on a specific application, we benchmark the performances of the proposed method performances with the state-of-the-art computer vision algorithms on our dataset. Moreover, our study shows a machine learning approach toward automating the inspection process of a restroom. INDEX TERMSImage classification, deep learning, principle component analysis, data augmentation, feature learning, internet of things.

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Section: B Feature Extractionmentioning

confidence: 99%

Feature Learning and Analysis for Cleanliness Classification in Restrooms

et al. 2019

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“…The power of crowd-sensing comes into play in this scenario. Mobile crowd-sensing is a popular computing paradigm which enables ubiquitous mobile devices to collect sensing data at large scales [25], [26]. Crowd-sensing techniques can be utilized to unleash the potential of mobile phones of people who move inside the indoor environment [27].…”

Section: Related Workmentioning

confidence: 99%

Crowd-sensing Simultaneous Localization and Radio Fingerprint Mapping Based on Probabilistic Similarity Models

Liu¹,

Marakkalage²,

Padmal³

et al. 2019

ION Pacific PNT

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Design. In 2014, he decided to take up a research intensive role and has since been an Assistant Professor with the Singapore University of Technology and Design. His research interests include mechatronics, medical robotics, sensing, control, mechanism design, and disturbance compensation. He received the STUD Outstanding Education Award-Excellence in Teaching in 2015 and the Best Student Paper Finalist for the IEEE ROBIO 2006. Abstract Simultaneous localization and mapping (SLAM) has been richly researched in past years particularly with regard to rangebased or visual-based sensors. Instead of deploying dedicated devices that use visual features, it is more pragmatic to exploit the radio features to achieve this task, due to their ubiquitous nature and the wide deployment of Wifi wireless network. In this paper, we present a novel approach for crowd-sensing simultaneous localization and radio fingerprint mapping (C-SLAM-RF) in large unknown indoor environments. The proposed system makes use of the received signal strength (RSS) from surrounding Wifi access points (AP) and the motion tracking data from a smart phone (Tango as an example). These measurements are captured duration the walking of multiple users in unknown environments without map information and location of the AP. The experiments were done in a university building with dynamic environment and the results show that the proposed system is capable of estimating the tracks of a group of users with an accuracy of 1.74 meters when compared to the ground truth acquired from a point cloud-based SLAM.

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“…The paradigm of IoT enabled the concept of Mobile Crowd Sensing (MCS), in which a number of mobile devices act as sensing nodes and collectively share sensing data so as to measure or predict a phenomenon of common interest [3], [4], [5]. Owing to the ubiquity of smart devices and the development of information technologies, IoT and MCS have gained huge popularity in the field of data collection [6], [7], and are thus well known areas of research in the present day.…”

Section: Introductionmentioning

confidence: 99%

A Crowd-Based Efficient Fault-Proof Localization System for IoT and MCS

2021

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In this paper, an efficient and fault-proof active node selection approach for localization tasks in Internet of Things (IoT) and Mobile Crowd Sensing (MCS) systems is proposed. The proposed approach is resilient to the presence of anomalous nodes. Localization is the process of fusing data readings from multiple sensing nodes with the aim of finding the location of a specific target, such as radiation source, forest fires and noisy areas. Current active node selection systems for localization tasks perform algorithms like greedy and genetic methods over the whole Area of Interest (AoI). As such, a system that considers anomalous data is required to detect anomalies and perform localization over a large number of active nodes, which usually takes multiple iterations and is computationally costly. To overcome this, we propose a resilient localization approach which a) uses the median filter based image filtering technique to level out anomalous readings, b) uses the filtered readings to reduce the AoI to be around the target location without being influenced by anomalous nodes, c) detects and eliminates anomalies in the new AoI based on the deviation between filtered readings and original readings, and d) selects remaining nodes in new AoI for localization. As a result, there is a huge reduction in the complexity of active node selection and thus reduction in time taken by the system to perform the task of source localization. The efficacy of the proposed system is evaluated for radiation source localization tasks using simulated radiation dataset, by performing experiments for several test scenarios. The results demonstrate that the system is able to perform localization tasks in significantly reduced time and therefore generate near real-time results while also maintaining low localization error.

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Understanding the Lifestyle of Older Population: Mobile Crowdsensing Approach

Cited by 40 publications

References 19 publications

Feature Learning and Analysis for Cleanliness Classification in Restrooms

Feature Learning and Analysis for Cleanliness Classification in Restrooms

Crowd-sensing Simultaneous Localization and Radio Fingerprint Mapping Based on Probabilistic Similarity Models

A Crowd-Based Efficient Fault-Proof Localization System for IoT and MCS

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