In this paper we demonstrate how smart phone sensors, specifically inertial sensors and GPS traces, can be used as an objective "measurement device" for aiding psychiatric diagnosis. In a trial with 12 bipolar disorder patients conducted over a total (summed over all patients) of over 1000 days (on average 12 weeks per patient) we have achieved state change detection with a precision/recall of 96%/94% and state recognition accuracy of 80%. The paper describes the data collection, which was conducted as a medical trial in a real life every day environment in a rural area, outlines the recognition methods, and discusses the results.

With the development of wearable devices that have several embedded sensors, it is possible to collect data that can be analyzed in order to understand the user’s needs and provide personalized services. Examples of these types of devices are smartphones, fitness-bracelets, smartwatches, just to mention a few. In the last years, several works have used these devices to recognize simple activities like running, walking, sleeping, and other physical activities. There has also been research on recognizing complex activities like cooking, sporting, and taking medication, but these generally require the installation of external sensors that may become obtrusive to the user. In this work we used acceleration data from a wristwatch in order to identify long-term activities. We compare the use of Hidden Markov Models and Conditional Random Fields for the segmentation task. We also added prior knowledge into the models regarding the duration of the activities by coding them as constraints and sequence patterns were added in the form of feature functions. We also performed subclassing in order to deal with the problem of intra-class fragmentation, which arises when the same label is applied to activities that are conceptually the same but very different from the acceleration point of view.

Positioning an individual with high accuracy is important since many location-based services rely on the position of the user to provide them with ubiquitous services. Despite the need for accuracy, no perfect solution has been proposed for the problem of accurately positioning an individual. A number of attempts to improve the accuracy has been made achieving an accuracy of about 2 meters using sophisticated techniques and the advances made in the mobile industry. In this paper we explain the methodology to get a propagation model suitable for Bluetooth in order to get a more accurate distance measurement, and also the algorithm to combine it with WiFi to position a user in an indoor environment. Firstly, we get measurements of distance related to a RSSI value obtained from the Bluetooth to get a propagation model, we compute a distance using the known propagation model from WiFi, and finally an algorithm to obtain the location of the receiver combinig Bluetooth and WiFi is presented

User indoor positioning has been under constant improvement especially with the availability of new sensors integrated to the modern mobile devices. These sensory devices allow us to exploit not only infrastructures made for every day use, such as Wifi, but also natural infrastructure, as is the case of natural magnetic fields. In this work, we propose a novel approach that takes advantage of the benefits of using the magnetic sensor incorporated in most modern mobile devices, and the negligible variations of the Earth's magnetic field to position an individual with high accuracy. Most importantly, the methodology proposed allows us to avoid the burden of having to collect magnetic information in different directions in order to construct an accurate magnetic map, showing an improvement on methods that require the individuals to construct bigger magnetic maps that contain redundant information such as magnitude in different directions

After large-magnitude earthquakes, a crucial task for impact assessment is to rapidly and accurately estimate the ground shaking in the affected region. To satisfy real-time constraints, intensity measures are traditionally evaluated with empirical Ground Motion Models that can drastically limit the accuracy of the estimated values. As an alternative, here we present Machine Learning strategies trained on physics-based simulations that require similar evaluation times. We trained and validated the proposed Machine Learning-based Estimator for ground shaking maps with one of the largest existing datasets (<100M simulated seismograms) from CyberShake developed by the Southern California Earthquake Center covering the Los Angeles basin. For a well-tailored synthetic database, our predictions outperform empirical Ground Motion Models provided that the events considered are compatible with the training data. Using the proposed strategy we show significant error reductions not only for synthetic, but also for five real historical earthquakes, relative to empirical Ground Motion Models.

In our previous research work, we proposed a methodology that uses magnetic-field and multivariate methods to estimate user location in an indoor environment. In this paper, we propose the use of this methodology to evaluate the performance of four different classification algorithms: Random Forest, Nearest Centroid, K Nearest Neighbors and Artificial Neural Networks; each classifier will be considered as a cost function of a genetic algorithm (GA) used in the feature selection process task of the methodology. The motivation to evaluate the algorithms of classification was that several ILSs use a classification algorithm in order to estimate the location of the user, but the classifiers performance vary from application to application. In order to evaluate the performance of each classification algorithm, the following issues were considered: (1) the time of the training phase to obtain the final classification algorithm; (2) the number of features needed for getting the model; (3) the type of the features from the final model; and (4) the sensitivity and specificity of the model. Our results indicate that Nearest centroid is the classfier algorithm that is best suited to be implemented in an end-user application given the obtained results on the evaluated criteria for the indoor location system (ILS)