A survey of people-centric sensing studies utilizing mobile phone sensors

Bayındır, Levent

doi:10.3233/ais-170446

Cited by 14 publications

(11 citation statements)

References 128 publications

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“…However, the inference of ISLSPs and OSLSPs depends on the latent speed of a trajectory at Strava segment level (for ISLSPs) or considering a -neighborhood (for OSLSPs). This segment or cluster matching procedure is similar to the mapmatching step performed by many solutions for GPS trajectory processing in the urban and vehicular environment [4,6,21]. This allows to reduce the impact of these noisy points in the speed pattern of a trajectory.…”

Section: Clustering Of the Out-segment Low-speed Points (Oslsps)mentioning

confidence: 99%

Forest path condition monitoring based on crowd-based trajectory data analysis

Arcas-Túnez

Terroso-Sáenz

2021

AIS

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The development of Road Information Acquisition Systems (RIASs) based on the Mobile Crowdsensing (MCS) paradigm has been widely studied for the last years. In that sense, most of the existing MCS-based RIASs focus on urban road networks and assume a car-based scenario. However, there exist a scarcity of approaches that pay attention to rural and country road networks. In that sense, forest paths are used for a wide range of recreational and sport activities by many different people and they can be also affected by different problems or obstacles blocking them. As a result, this work introduces SAMARITAN, a framework for rural-road network monitoring based on MCS. SAMARITAN analyzes the spatio-temporal trajectories from cyclists extracted from the fitness application Strava so as to uncover potential obstacles in a target road network. The framework has been evaluated in a real-world network of forest paths in the city of Cieza (Spain) showing quite promising results.

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Section: Clustering Of the Out-segment Low-speed Points (Oslsps)mentioning

confidence: 99%

Forest path condition monitoring based on crowd-based trajectory data analysis

Arcas-Túnez

Terroso-Sáenz

2021

AIS

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“…Vitality" 241 or "Dacadoo" 242 ). In some other cases, they are on-demandinsurance contracts using only a digital sales and approval process (e.g., "Lings" 243 or "Trov" 244 ).…”

Section: Cybersecuritymentioning

confidence: 99%

Platforms for Insurance 4.0

Nicoletti

2020

Palgrave Studies in Financial Services Technology

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“…In this study, they claimed that 50% of patients from all age groups are interested in using a mobile application to control and monitor their mental health condition. Bayindir [10] presented a review of different works focused on mobile phone sensors used for the detection of human behavior characteristics, describing activity detection at different abstraction levels of activity and characterizing health-related activities, such as physical exercise and sleeping.…”

Section: Introductionmentioning

confidence: 99%

Feature Extraction in Motor Activity Signal: Towards a Depression Episodes Detection in Unipolar and Bipolar Patients

et al. 2019

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Depression is a mental disorder characterized by recurrent sadness and loss of interest in the enjoyment of the positive aspects of life, in addition to fatigue, causing inability to perform daily activities, which leads to a loss of quality of life. To monitor depression (unipolar and bipolar patients), traditional methods rely on reports from patients; nevertheless, bias is commonly present in them. To overcome this problem, Ecological Momentary Assessment (EMA) reports have been widely used, which include data of the behavior, feelings and other types of activities recorded almost in real time through the use of portable devices and smartphones containing motion sensors. In this work a methodology was proposed to detect depressive subjects from control subjects based in the data of their motor activity, recorded by a wearable device, obtained from the “Depresjon” database. From the motor activity signals, the extraction of statistical features was carried out to subsequently feed a random forest classifier. Results show a sensitivity value of 0.867, referring that those subjects with presence of depression have a degree of 86.7% of being correctly classified, while the specificity shows a value of 0.919, referring that those subjects with absence of depression have a degree of 91.9% of being classified with a correct response, using the motor activity signal provided from the wearable device. Based on these results, it is concluded that the motor activity allows distinguishing between the two classes, providing a preliminary and automated tool to specialists for the diagnosis of depression.

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A survey of people-centric sensing studies utilizing mobile phone sensors

Cited by 14 publications

References 128 publications

Forest path condition monitoring based on crowd-based trajectory data analysis

Forest path condition monitoring based on crowd-based trajectory data analysis

Platforms for Insurance 4.0

Feature Extraction in Motor Activity Signal: Towards a Depression Episodes Detection in Unipolar and Bipolar Patients

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