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Context-awareness for mobile sensing: a survey and future directions
The evolution of smartphones together with increasing computational power have empowered developers to create innovative context-aware applications for recognizing user related social and cognitive activities in any situation and at any location. The existence and awareness of the context provides the capability of being conscious of physical environments or situations around mobile device users. This allows network services to respond proactively and intelligently based on such awareness. The key idea behind context-aware applications is to encourage users to collect, analyze and share local sensory knowledge in the purpose for a large scale community use by creating a smart network. The desired network is capable of making autonomous logical decisions to actuate environmental objects, and also assist individuals. However, many open challenges remain, which are mostly arisen due to the middleware services provided in mobile devices have limited resources in terms of power, memory and bandwidth. Thus, it becomes critically important to study how the drawbacks can be elaborated and resolved, and at the same time better understand the opportunities for the research community to contribute to the context-awareness. To this end, this paper surveys the literature over the period of 1991-2014 from the emerging concepts to applications of context-awareness in mobile platforms by providing up-to-date research and future research directions. Moreover, it points out the challenges faced in this regard and enlighten them by proposing possible solutions
Combining mobile-health (mHealth) and artificial intelligence (AI) methods to avoid suicide attempts: the Smartcrises study protocol
The screening of digital footprint for clinical purposes relies on the capacity of wearable technologies
to collect data and extract relevant informationâs for patient management. Artificial intelligence (AI) techniques
allow processing of real-time observational information and continuously learning from data to build
understanding. We designed a system able to get clinical sense from digital footprints based on the smartphoneâs
native sensors and advanced machine learning and signal processing techniques in order to identify suicide risk.
Method/design: The Smartcrisis study is a cross-national comparative study. The study goal is to determine the
relationship between suicide risk and changes in sleep quality and disturbed appetite. Outpatients from the
Hospital FundaciĂłn JimĂ©nez DĂaz Psychiatry Department (Madrid, Spain) and the University Hospital of Nimes
(France) will be proposed to participate to the study. Two smartphone applications and a wearable armband will
be used to capture the data. In the intervention group, a smartphone application (MEmind) will allow for the
ecological momentary assessment (EMA) data capture related with sleep, appetite and suicide ideations.
Discussion: Some concerns regarding data security might be raised. Our system complies with the highest level of
security regarding patientsâ data. Several important ethical considerations related to EMA method must also be
considered. EMA methods entails a non-negligible time commitment on behalf of the participants. EMA rely on
daily, or sometimes more frequent, Smartphone notifications. Furthermore, recording participantsâ daily experiences
in a continuous manner is an integral part of EMA. This approach may be significantly more than asking a
participant to complete a retrospective questionnaire but also more accurate in terms of symptoms monitoring.
Overall, we believe that Smartcrises could participate to a paradigm shift from the traditional identification of risks
factors to personalized prevention strategies tailored to characteristics for each patientThis study was partly funded by FundaciĂłn JimĂ©nez DĂaz Hospital, Instituto
de Salud Carlos III (PI16/01852), DelegaciĂłn del Gobierno para el Plan
Nacional de Drogas (20151073), American Foundation for Suicide Prevention
(AFSP) (LSRG-1-005-16), the Madrid Regional Government (B2017/BMD-3740
AGES-CM 2CM; Y2018/TCS-4705 PRACTICO-CM) and Structural Funds of the
European Union. MINECO/FEDER (âADVENTUREâ, id. TEC2015â69868-C2â1-R)
and MCIU Explora Grant âaMBITIONâ (id. TEC2017â92552-EXP), the French Embassy
in Madrid, Spain, The foundation de lâavenir, and the Fondation de
France. The work of D. RamĂrez and A. ArtĂ©s-RodrĂguez has been partly supported
by Ministerio de EconomĂa of Spain under projects: OTOSIS
(TEC2013â41718-R), AID (TEC2014â62194-EXP) and the COMONSENS Network
(TEC2015â69648-REDC), by the Ministerio de EconomĂa of Spain jointly with
the European Commission (ERDF) under projects ADVENTURE (TEC2015â
69868-C2â1-R) and CAIMAN (TEC2017â86921-C2â2-R), and by the Comunidad
de Madrid under project CASI-CAM-CM (S2013/ICE-2845). The work of P.
Moreno-Muñoz has been supported by FPI grant BES-2016-07762
Sensing behaviour in healthcare design
We are entering an era of distributed healthcare that should fit and respond to individual needs, behaviour and lifestyles. Designing such systems is a challenging task that requires continuous information about human behaviour on a large scale, for which pervasive sensing (e.g. using smartphones and wearables) presents exciting opportunities. While mobile sensing approaches are fuelling research in many areas, their use in engineering design remains limited. In this work, we present a collection of common behavioural measures from literature that can be used for a broad range of applications. We focus specifically on activity and location data that can easily be obtained from smartphones or wearables. We further demonstrate how these are applied in healthcare design using an example from dementia care. Comparing a current and proposed scenario exemplifies how integrating sensor-derived information about user behaviour can support the healthcare design goals of personalisation, adaptability and scalability, while emphasising patient quality of life
Hardware for recognition of human activities: a review of smart home and AAL related technologies
Activity recognition (AR) from an applied perspective of ambient assisted living (AAL) and smart homes (SH) has become a subject of great interest. Promising a better quality of life, AR applied in contexts such as health, security, and energy consumption can lead to solutions capable of reaching even the people most in need. This study was strongly motivated because levels of development, deployment, and technology of AR solutions transferred to society and industry are based on software development, but also depend on the hardware devices used. The current paper identifies contributions to hardware uses for activity recognition through a scientific literature review in the Web of Science (WoS) database. This work found four dominant groups of technologies used for AR in SH and AALâsmartphones, wearables, video, and electronic componentsâand two emerging technologies: Wi-Fi and assistive robots. Many of these technologies overlap across many research works. Through bibliometric networks analysis, the present review identified some gaps and new potential combinations of technologies for advances in this emerging worldwide field and their uses. The review also relates the use of these six technologies in health conditions, health care, emotion recognition, occupancy, mobility, posture recognition, localization, fall detection, and generic activity recognition applications. The above can serve as a road map that allows readers to execute approachable projects and deploy applications in different socioeconomic contexts, and the possibility to establish networks with the community involved in this topic. This analysis shows that the research field in activity recognition accepts that specific goals cannot be achieved using one single hardware technology, but can be using joint solutions, this paper shows how such technology works in this regard
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