Cross-tree constraints help to compact feature models by using arbitrary propositional logic formulas, which efficiently capture interdependencies between features. However, the existence of these constraints increases the complexity of reasoning about feature models, whether we use SAT solvers or compile the model to a binary decision diagram for efficient analyses. Although some works have tried to refactor constraints to eliminate them, they deal only with simple constraints (i.e., requires and excludes) or require introducing an additional set of features, increasing the size and complexity of the resulting feature model. This paper presents an approach that eliminates all the cross-tree constraints in regular boolean feature models, including arbitrary constraints in propositional logic formulas. Our approach for removing constraints consists of splitting the semantics of feature models into orthogonal disjoint feature subtrees, which are then analyzed in parallel to alleviate the exponential blow-up in memory of the resulting feature tree . We propose a codification of the constraints and define and analyze different heuristics for constraints ordering to reduce the complexity of identifying the valid disjoint subtrees when removing constraints.

Next-generation mobile networks are expected to flaunt highly (if not fully) automated management. To achieve such a vision, Artificial Intelligence (AI) and Machine Learning (ML) techniques will be key enablers to craft the required intelligence for networking, i.e., Network Intelligence (NI), empowering myriad of orchestrators and controllers across network domains. In this paper, we elaborate on the DAEMON architectural model, which proposes introducing a NI Orchestration layer for the effective end-to-end coordination of NI instances deployed across the whole mobile network infrastructure. Specifically, we first outline requirements and specifications for NI design that stem from data management, control timescales, and network technology characteristics. Then, we build on such analysis to derive initial principles for the design of the NI Orchestration layer, focusing on (i) proposals for the interaction loop between NI instances and the NI Orchestrator, and (ii) a unified representation of NI algorithms based on an extended MAPE-K model. Our work contributes to the definition of the interfaces and operation of a NI Orchestration layer that foster a native integration of NI in mobile network architectures.

Mobility loss has a major impact on autonomy. Smart rollators have been proposed to enhance human abilities when conventional devices are not enough. Many human–robot interaction systems have been proposed in the last decade in this area. Comparative analysis shows that mechanical issues aside, they mainly differ in first, equipped sensors and actuators; second, input interface; third, operation modes, and fourth adaptation capabilities. This article presents a review and a tentative taxonomy of approaches during the last 6 years. In total, 92 papers have been reviewed. We have discarded works not focused on humanrobot interaction or focused only on mechanical adaptation. A critical analysis is provided after the review and classification, highlighting systems tested with their target population.

Motor Imagery (MI) classification from neural activity is thought to represent valuable information that can be provided as real-time feedback during rehabilitation after for example a stroke. Previous studies have suggested that MI induces partly subject-specific EEG activation patterns, suggesting that individualized classification models should be created. However, due to fatigue of the user, only a limited number of samples can be recorded and, for EEG recordings, each sample is often composed of a large number of features. This combination leads to an undesirable input data set for classification. In order to overcome this constraint, we propose a new methodology to create and select features from the EEG signal in two steps. First, the input data is divided into different windows to reduce the cardinality of the input. Secondly, a Hierarchical Genetic Algorithm is used to select relevant features using a novel fitness function which combines the data reduction with a correlation feature selection measure. The methodology has been tested on EEG oscillatory activity recorded from 6 healthy volunteers while they performed an MI task. Results have successfully proven that a classification above 75% can be obtained in a restrictive amount of time (0.02 s), reducing the number of features by almost 90%.

Telepresence robots have been recently used for Comprehensive Geriatric Assessment (CGA). Since the robot can not track a person continuously, there are several strategies to decide when to check them, from cyclic checks to simple requests from users and/or caregivers. In order to adapt to the user needs and condition, it is preferable to perform CGA as soon as regularities appear. However, this requires detection of potential issues in users to offer immediate service. In this work we propose a new low cost force sensor system to detect user's condition and attract attention of CGA robots, so they can perform a full examination on a need basis. The main advantages of this system are: i) it can be attached to any standard commercial cane; ii) its power consumption is very reduced; and iii) it provides continuous information as long as the user walks. It has been tested with several elderly volunteers in care facilities. Results have proven that the sensor readings are indeed correlated with the users' condition.

Fall risk assessments provide a useful tool to prevent morbidity and mortality provoked by falls. Nowadays, these assessments are usually performed manually by the medical staff. This approach has three main drawbacks: (i) it is time consuming, so it is only performed a few times per volunteer during their rehabilitation process; (ii) it requires supervision by medical staff, so assessment at home or preferred environments is not feasible; and (iii) fall risk is evaluated in a global way, so imminent fall risk is not available for decision making in assistive navigation. In this paper we propose an imminent fall risk estimator for rollator's users that can be automatically obtained on the fly. Its main advantages are: (i) it can be used in everyday conditions in any environment; (ii) it does not require assistance of medical staff; and (iii) it is suitable for a variety of users with minimal configuration changes. We have validated our estimator with a set of volunteers (n=10) presenting different physical and cognitive disabilities. Although the number of volunteer is limited, results show that our estimator is coherent to two traditional, well accepted assessments: the Tinetti Mobility Test and the walking speed.

Shared control is a strategy used in assistive platforms to combine human and robot orders to achieve a goal. Collaborative control is a specific shared control approach, in which user's and robot's commands are merged into an emergent one in a continuous way. Robot commands tend to improve efficiency and safety. However, sometimes, assistance can be rejected by users when their commands are too altered. This provokes frustration and stress and, usually, decreases emergent efficiency. To improve acceptance, robot navigation algorithms can be adapted to mimic human behavior when possible. We propose a novel variation of the well-known dynamic window approach (DWA) that we call biomimetical DWA (BDWA). The BDWA relies on a reward function extracted from real traces from volunteers presenting different motor disabilities navigating in a hospital environment using a rollator for support. We have compared the BDWA with other reactive algorithms in terms of similarity to paths completed by people with disabilities using a robotic rollator in a rehabilitation hospital unit. The BDWA outperforms all tested algorithms in terms of likeness to human paths and success rate.

The assistance to people during rehabilitation has to be adapted to their needs. Too little help can lead to frustration and stress in the user; an excess of help may lead to low participation and loss of residual skills. Robotic rollators may adapt assistance. The main challenge to cope with this issue is to estimate how much help is needed on the fly, because it depends not only on the person condition, but also on the specific situation that they are negotiating. Clinical scales provide a global condition based estimation, but no local estimator based on punctual needs. Condition also changes in time, so clinical scales need to be recalculated again and again. In this paper we propose a novel approach to estimate users' condition in a continuous way via a robotic rollator. Our work focuses on predicting the value of the well known Tinetti Mobility test from spatiotemporal gait parameters obtained from our platform while users walk. This prediction provides continuous insight on the condition of the user and could be used to modify the amount of help provided. The proposed method has been validated with 19 volunteers at a local hospital that use a rollator for rehabilitation. All volunteers presented some physical or mental disabilities. Our results successfully show a high correlation of spatiotemporal gait parameters with Tinetti Mobility test gait (R2 = 0.7) and Tinetti Mobility test balance (R2 = 0.6).

Gait analysis provides insightful information about people condition, progress of rehabilitation treatments and fall risk. This analysis is frequently performed in very specific conditions, because it often implies the use of treadmills, cameras and/or wearable sensors. Alternatively, gait can be analyzed using a smart rollator, equipped with a basic set of sensors. This work proposes a methodology to obtain relevant gait parameters using a simple, cheap source of information: wheel odometry and handlebar force sensors. The main advantages of this approach are that users are not bothered with extra equipment. Besides, monitoring can be performed anywhere, even during everyday conditions and for extended periods of time. We have tested our system with a set of rehabilitation patients with different disabilities. Preliminary results have successfully proven that extracted parameters are coherent with reported effects of their specific condition.

In this paper, we introduce an approach called FSBS (Forward Search in Belief Space) for online planning in POMDPs. The approach is based on the RTBSS (Real-Time Belief Space Search) algorithm of [1]. The main departure from the algorithm is the introduction of similarity measures in the belief space. By considering statistical divergence measures, the similarity between belief points in the forward search tree can be computed. Therefore, it is possible to determine if a certain belief point (or one very similar) has been already visited. This way, it is possible to reduce the complexity of the search by not expanding similar nodes already visited in the same depth. This reduction of complexity makes possible the real-time implementation of more complex problems in robots. The paper describes the algorithm, and analyzes different divergence measures. Benchmark problems are used to show how the approach can obtain a ten-fold reduction in the computation time for similar obtained rewards when compared to the original RTBSS. The paper also presents experiments with a quadrotor in a search application.