Manuel Fernández-Carmona

Efficiency based modulation for wheelchair driving collaborative control

This work presents a new approach to shared control to assist wheelchair driving. Rather than swapping control from human to robot either by request or on a need basis, the system estimates how much help is needed in a reactive fashion and continuously produces an emergent motory command in combination with human input. To provide time stability and integration, instant commands are modulated by a factor depending on human efficiency in a shifting time window. Thus, the better the person drives, the more control he/she is awarded with. The approach has been tested at Fondazione Santa Lucia (FSL) in Rome with volunteers presenting different disabilities. All volunteers managed to finish a mildly complicated trajectory with door crossing and major turns and the proposed system increased efficiency in all cases.

Adaptive collaborative assistance for wheelchair driving via CBR learning

This work presents a new approach to shared control driving a robotic wheelchair for persons with disabilities. The proposal is based on weighting the robot and human commands by their respective efficiencies to obtain an emergent command in a reactive way. It was tested with a robotized Meyra wheelchair at Fondazione Santa Lucia (FSL) in Rome with volunteers presenting different disabilities and we observed that the system seemed to help less persons with better cognitive skills. This seemed to be due to disagreement between the users and the machine when they realized that they were being helped. In order to improve that, we added a Case Based Reasoning module to absorb how the user drives to replace the robot navigation algorithm. New tests with the adaptive system showed an increase in efficiency in all cases.

Efficiency based collaborative control modulated by biometrics for wheelchair assisted navigation

This paper addresses a new collaborative control method for robotic wheelchairs. The original method was specifically designed for disabled people who are unable to drive a robotic wheelchair on their own. Its main novelty was that the wheelchair just provided the amount of help needed at each moment, to avoid loss of residual abilities. This wheelchair was tested by volunteering in-patients in Casa Agevole at Fondazione Santa Lucia (FSL) in Rome. However we found that in-patients with severe cognitive impairment were not able to complete complex trajectories despite wheelchair help. Thus, this works presents the improvement of these control techniques, more suitable for severe patients. We present a modified efficiency based collaborative control scheme based on modulation of assistance using biometric sensors, as well as preliminary results of this technique.

Conventional Joystick vs. Wiimote for Holonomic Wheelchair Control

In nowadays aging society, power wheelchairs provide assistance for non-pedestrian mobility, but inside narrow indoor spaces, holonomic ones are required. While they adapt well to complex environments, it is harder to control them via a conventional joystick. Thus, extra buttons and/or knobs are included to decide what to do. To make control more intuitive, we propose to use a Wiimote for holonomic wheelchair control. Experiments in a narrow environment have been succesful and prove that Wiimote requires less interaction to achieve the same results that a conventional joystick. This has been reported to reduce mental workload and, hence, allow more relaxed interaction with the wheelchair.

Learning-Based Adaptation for Personalized Mobility Assistance

Mobility assistance is of key importance for people with disabilities to remain autonomous in their preferred environments. In severe cases, assistance can be provided by robotized wheelchairs that can perform complex maneuvers and/or correct the user’s commands. User’s acceptance is of key importance, as some users do not like their commands to be modified. This work presents a solution to improve acceptance. It consists of making the robot learn how the user drives so corrections will not be so noticeable to the user. Case Based Reasoning (CBR) is used to acquire a user’s driving model reactive level. Experiments with volunteers at Fondazione Santa Lucia (FSL) have proven that, indeed, this customized approach at assistance increases acceptance by the user.

On-Line Inference Comparison with Markov Logic Network Engines for Activity Recognition in AAL Environments

We address possible solutions for a practical application of Markov Logic Networks to online activity recognition, based on domotic sensors, to be used for monitoring elderly with mild cognitive impairments. Our system has to provide responsive information about user activities throughout the day, so different inference engines are tested. We use an abstraction layer to gather information from commercial domotic sensors. Sensor events are stored using a non-relational database. Using this database, evidences are built to query a logic network about current activities. Markov Logic Networks are able to deal with uncertainty while keeping a structured knowledge. This makes them a suitable tool for ambient sensors based inference. However, in their previous application, inferences are usually made offline. Time is a relevant constrain in our system and hence logic networks are designed here accordingly. We compare in this work different engines to model a Markov Logic Network suitable for such circumstances. Results show some insights about how to design a low latency logic network and which kind of solutions should be avoided.

Towards a Shared Control Navigation Function: Efficiency Based Command Modulation

This paper presents a novel shared control algorithm for robotized wheelchairs. The proposed algorithm is a new method to extend autonomous navigation techniques into the shared control domain. It reactively combines user’s and robot’s commands into a continuous function that approximates a classic Navigation Function (NF) by weighting input commands with NF constraints. Our approach overcomes the main drawbacks of NFs -calculus complexity and limitations on environment modeling- so it can be used in dynamic unstructured environments. It also benefits from NF properties: convergence to destination, smooth paths and safe navigation. Due to the user’s contribution to control, our function is not strictly a NF, so we call it a pseudo-navigation function (PNF) instead.

Smartphone-Based Vehicle Emission Estimation

Intelligent Transportation Systems are expected to contribute to reduce vehicle emissions. Solutions include user behavior changes, which are typically vehicle sharing programs and driving habits changes. To change driving habits, people need personalized feedback on their vehicle emissions in the routes they complete. In this work, we present a smartphone based system that returns a geopositioned emission estimation on the fly. Our estimator is based on the ARTEMIS project results. The system has been successfully tested in different routes in Malaga (Spain) in highway, rural and urban areas.