45,381 research outputs found
Learning Human Behaviour Patterns by Trajectory and Activity Recognition
The world’s population is ageing, increasing the awareness of neurological and behavioural
impairments that may arise from the human ageing. These impairments can be manifested
by cognitive conditions or mobility reduction. These conditions are difficult to be
detected on time, relying only on the periodic medical appointments. Therefore, there is
a lack of routine screening which demands the development of solutions to better assist
and monitor human behaviour. The available technologies to monitor human behaviour
are limited to indoors and require the installation of sensors around the user’s homes
presenting high maintenance and installation costs. With the widespread use of smartphones,
it is possible to take advantage of their sensing information to better assist the
elderly population. This study investigates the question of what we can learn about human
pattern behaviour from this rich and pervasive mobile sensing data. A deployment
of a data collection over a period of 6 months was designed to measure three different
human routines through human trajectory analysis and activity recognition comprising
indoor and outdoor environment. A framework for modelling human behaviour was
developed using human motion features, extracted in an unsupervised and supervised
manner. The unsupervised feature extraction is able to measure mobility properties such
as step length estimation, user points of interest or even locomotion activities inferred
from an user-independent trained classifier. The supervised feature extraction was design
to be user-dependent as each user may have specific behaviours that are common to
his/her routine. The human patterns were modelled through probability density functions
and clustering approaches. Using the human learned patterns, inferences about
the current human behaviour were continuously quantified by an anomaly detection
algorithm, where distance measurements were used to detect significant changes in behaviour.
Experimental results demonstrate the effectiveness of the proposed framework
that revealed an increase potential to learn behaviour patterns and detect anomalies
Anticipatory Mobile Computing: A Survey of the State of the Art and Research Challenges
Today's mobile phones are far from mere communication devices they were ten
years ago. Equipped with sophisticated sensors and advanced computing hardware,
phones can be used to infer users' location, activity, social setting and more.
As devices become increasingly intelligent, their capabilities evolve beyond
inferring context to predicting it, and then reasoning and acting upon the
predicted context. This article provides an overview of the current state of
the art in mobile sensing and context prediction paving the way for
full-fledged anticipatory mobile computing. We present a survey of phenomena
that mobile phones can infer and predict, and offer a description of machine
learning techniques used for such predictions. We then discuss proactive
decision making and decision delivery via the user-device feedback loop.
Finally, we discuss the challenges and opportunities of anticipatory mobile
computing.Comment: 29 pages, 5 figure
Smartphone apps usage patterns as a predictor of perceived stress levels at workplace
Explosion of number of smartphone apps and their diversity has created a
fertile ground to study behaviour of smartphone users. Patterns of app usage,
specifically types of apps and their duration are influenced by the state of
the user and this information can be correlated with the self-reported state of
the users. The work in this paper is along the line of understanding patterns
of app usage and investigating relationship of these patterns with the
perceived stress level within the workplace context. Our results show that
using a subject-centric behaviour model we can predict stress levels based on
smartphone app usage. The results we have achieved, of average accuracy of 75%
and precision of 85.7%, can be used as an indicator of overall stress levels in
work environments and in turn inform stress reduction organisational policies,
especially when considering interrelation between stress and productivity of
workers
Tourism and the smartphone app: capabilities, emerging practice and scope in the travel domain.
Based on its advanced computing capabilities and ubiquity, the smartphone has rapidly been adopted as a tourism travel tool.With a growing number of users and a wide varietyof applications emerging, the smartphone is fundamentally altering our current use and understanding of the transport network and tourism travel. Based on a review of smartphone apps, this article evaluates the current functionalities used in the domestic tourism travel domain and highlights where the next major developments lie. Then, at a more conceptual level, the article analyses how the smartphone mediates tourism travel and the role it might play in more collaborative and dynamic travel decisions to facilitate sustainable travel. Some emerging research challenges are discussed
Validation of a smartphone app to map social networks of proximity
Social network analysis is a prominent approach to investigate interpersonal
relationships. Most studies use self-report data to quantify the connections
between participants and construct social networks. In recent years smartphones
have been used as an alternative to map networks by assessing the proximity
between participants based on Bluetooth and GPS data. While most studies have
handed out specially programmed smartphones to study participants, we developed
an application for iOS and Android to collect Bluetooth data from participants
own smartphones. In this study, we compared the networks estimated with the
smartphone app to those obtained from sociometric badges and self-report data.
Participants (n=21) installed the app on their phone and wore a sociometric
badge during office hours. Proximity data was collected for 4 weeks. A
contingency table revealed a significant association between proximity data
(rho = 0.17, p<0.0001), but the marginal odds were higher for the app (8.6%)
than for the badges (1.3%), indicating that dyads were more often detected by
the app. We then compared the networks that were estimated using the proximity
and self-report data. All three networks were significantly correlated,
although the correlation with self-reported data was lower for the app (rho =
0.25) than for badges (rho = 0.67). The scanning rates of the app varied
considerably between devices and was lower on iOS than on Android. The
association between the app and the badges increased when the network was
estimated between participants whose app recorded more regularly. These
findings suggest that the accuracy of proximity networks can be further
improved by reducing missing data and restricting the interpersonal distance at
which interactions are detected.Comment: 20 pages, 5 figure
Automatic Stress Detection in Working Environments from Smartphones' Accelerometer Data: A First Step
Increase in workload across many organisations and consequent increase in
occupational stress is negatively affecting the health of the workforce.
Measuring stress and other human psychological dynamics is difficult due to
subjective nature of self- reporting and variability between and within
individuals. With the advent of smartphones it is now possible to monitor
diverse aspects of human behaviour, including objectively measured behaviour
related to psychological state and consequently stress. We have used data from
the smartphone's built-in accelerometer to detect behaviour that correlates
with subjects stress levels. Accelerometer sensor was chosen because it raises
fewer privacy concerns (in comparison to location, video or audio recording,
for example) and because its low power consumption makes it suitable to be
embedded in smaller wearable devices, such as fitness trackers. 30 subjects
from two different organizations were provided with smartphones. The study
lasted for 8 weeks and was conducted in real working environments, with no
constraints whatsoever placed upon smartphone usage. The subjects reported
their perceived stress levels three times during their working hours. Using
combination of statistical models to classify self reported stress levels, we
achieved a maximum overall accuracy of 71% for user-specific models and an
accuracy of 60% for the use of similar-users models, relying solely on data
from a single accelerometer.Comment: in IEEE Journal of Biomedical and Health Informatics, 201
Smartphones Adoption and Usage of 50+ Adults in the United Kingdom
This is an Accepted Manuscript of a book chapter published by Routledge in Jyoti Choudrie, Sherah Kurnia, and Panayiota Tsatsou, eds., Social Inclusion and Usability of ICT-enabled Services, on October 2017, available online at: https://www.routledge.com/Social-Inclusion-and-Usability-of-ICT-enabled-Services/Choudrie-Kurnia-Tsatsou/p/book/9781138935556. Under embargo until 30 April 2019.Peer reviewedFinal Accepted Versio
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