MobiQ: A modular Android application for collecting social interaction, repeated survey, GPS and photographic data

The MobiQ app for Android smartphones is a feature-rich application enabling a novel approach to data collection for longitudinal surveys. It combines continuous automatic background data collection with user supplied data. It can prompt users to complete questionnaires at regular intervals, and allows users to upload photographs for social research projects. The app has the capability to collect GPS location data, and calls and text frequency (excluding content) unobtrusively. The app transmits data to a secure cloud rather than storing research data on the phone, but can also store data temporarily if a data connection is unavailable; hence, MobiQ offers data security advantages over text- or web-based surveys using phones. MobiQ has been pilot tested in the field in a social science research project and is able to collect longitudinal social research data. Due to its modular and flexible design, MobiQ can easily be adapted to suit different research questions. Furthermore, its core design approach which allows for long-term power efficient data collection can be re-used outside the social sciences domain for other kinds of smartphone-based data-driven projects. Projects that have a requirement for communications-based, sensors-based, user-based data collection or any combination of these may find our code and design approach beneficial. For example, MobiQ code and architecture has been successfully adapted to build an app for a project investigating smartphone-based implicit authentication for mobile access control

Use of nonintrusive sensor-based information and communication technology for real-world evidence for clinical trials in dementia

Cognitive function is an important end point of treatments in dementia clinical trials. Measuring cognitive function by standardized tests, however, is biased toward highly constrained environments (such as hospitals) in selected samples. Patient-powered real-world evidence using information and communication technology devices, including environmental and wearable sensors, may help to overcome these limitations. This position paper describes current and novel information and communication technology devices and algorithms to monitor behavior and function in people with prodromal and manifest stages of dementia continuously, and discusses clinical, technological, ethical, regulatory, and user-centered requirements for collecting real-world evidence in future randomized controlled trials. Challenges of data safety, quality, and privacy and regulatory requirements need to be addressed by future smart sensor technologies. When these requirements are satisfied, these technologies will provide access to truly user relevant outcomes and broader cohorts of participants than currently sampled in clinical trials

Affective games:a multimodal classification system

Affective gaming is a relatively new field of research that exploits human emotions to influence gameplay for an enhanced player experience. Changes in player’s psychology reflect on their behaviour and physiology, hence recognition of such variation is a core element in affective games. Complementary sources of affect offer more reliable recognition, especially in contexts where one modality is partial or unavailable. As a multimodal recognition system, affect-aware games are subject to the practical difficulties met by traditional trained classifiers. In addition, inherited game-related challenges in terms of data collection and performance arise while attempting to sustain an acceptable level of immersion. Most existing scenarios employ sensors that offer limited freedom of movement resulting in less realistic experiences. Recent advances now offer technology that allows players to communicate more freely and naturally with the game, and furthermore, control it without the use of input devices. However, the affective game industry is still in its infancy and definitely needs to catch up with the current life-like level of adaptation provided by graphics and animation

Convolutional Neural Network for Stereotypical Motor Movement Detection in Autism

Autism Spectrum Disorders (ASDs) are often associated with specific atypical postural or motor behaviors, of which Stereotypical Motor Movements (SMMs) have a specific visibility. While the identification and the quantification of SMM patterns remain complex, its automation would provide support to accurate tuning of the intervention in the therapy of autism. Therefore, it is essential to develop automatic SMM detection systems in a real world setting, taking care of strong inter-subject and intra-subject variability. Wireless accelerometer sensing technology can provide a valid infrastructure for real-time SMM detection, however such variability remains a problem also for machine learning methods, in particular whenever handcrafted features extracted from accelerometer signal are considered. Here, we propose to employ the deep learning paradigm in order to learn discriminating features from multi-sensor accelerometer signals. Our results provide preliminary evidence that feature learning and transfer learning embedded in the deep architecture achieve higher accurate SMM detectors in longitudinal scenarios.Comment: Presented at 5th NIPS Workshop on Machine Learning and Interpretation in Neuroimaging (MLINI), 2015, (http://arxiv.org/html/1605.04435), Report-no: MLINI/2015/1