This paper presents a method to compute smooth ankle trajectories for lower limb exoskeletons with powered ankle joints. The proposed approach defines ankle trajectories using four polynomial functions, each representing one of the four primary phases of gait. These polynomials are computed according to different safety constraints. During the single support phase, ground contact constraints are enforced. In the swing phase, an optimization problem is solved to achieve minimum jerk planning while respecting a set of equality and inequality constraints designed to minimize the risk of stumbling. The used approach focuses on making the ankle joint able to smoothly adapt in real-time to different walking styles defined by user-selected gait parameters such as step length and clearance. The primary aim is to improve the user experience by producing a secure and comfortable walking pattern. To validate the effectiveness of the proposed method, the new ankle trajectories were tested on a group of healthy volunteers using the TWIN lower limb exoskeleton.

Since the uprising of new biomedical orthotic devices, exoskeletons have been put in the spotlight for their possible use in rehabilitation. Even if these products might share some commonalities among them in terms of overall structure, degrees of freedom and possible actions, they quite often differ in their approach on how to generate a feasible, stable and comfortable gait trajectory pattern. This paper introduces three proposed trajectories that were generated by using a basis function interpolation method and by working closely with two major rehabilitation centers in Italy. The whole procedure has been focused on the concepts of a configurable walk for patients that suffer from spinal cord injuries. We tested the solutions on a group of healthy volunteers and on a spinal-cord injury patient with the use of the new TWIN exoskeleton developed at the Rehab Technologies Lab at the Italian Institute of Technology.

Real-time automatic human behavior recognition is one of the most challenging tasks for intelligent surveillance systems. Its importance lies in the possibility of robust detection of suspicious behaviors in order to prevent possible threats. The widespread integration of tracking algorithms into modern surveillance systems makes it possible to acquire descriptive motion patterns of different human activities. In this work, a statistical framework for human interaction recognition based on Dynamic Bayesian Networks (DBNs) is presented: the environment is partitioned by a topological algorithm into a set of zones that are used to define the state of the DBNs. Interactive and non-interactive behaviors are described in terms of sequences of significant motion events in the topological map of the environment. Finally, by means of an incremental classification measure, a scenario can be classified while it is currently evolving. In this way an autonomous surveillance system can detect and cope with potential threats in real-time.

This paper proposes a computationally efficient approach for estimating camera motion based on a voting mechanism that neither relies on explicit feature matching nor RANSAC. In the presented method, correspondences arise as a consequence of finding the camera movement in a voting space and not as a prerequisite. Experiments show promising results compared with (KLT+RANSAC) while taking less time in computation.

The process of finding representative shape patterns from sparse datasets is a challenging task: especially for non-rigid objects, shape deformations through time can produce very different sets of corners from frame to frame and a proper comparison of point features can be very difficult. Evaluating a multi-objective fitness function in a discrete voting space, partial similarities between deformable objects can be found and a correct data association can be performed. A genomic encoding of corner-based shapes is introduced and, taking advantage of a robust genetic-based search algorithm, sets of corners pertaining to objects of interest are mapped into common models. The most representative features are detected and used to evolve shape prototypes.

A correct video segmentation, namely the detection of moving objects within a scene plays a very important role in many application in safety, surveillance, traffic monitoring and object detection. The main objective of this paper is to implement an effective background segmentation algorithm for corner sets extracted from video sequences. A dynamic prototype of the structure of background corners is produced and incoming corners are classified using a fuzzy ARTMAP neural network and labeled as pertaining to the background or foreground using a spatial clustering method. Finally the accuracy of the proposed algorithm is evaluated using PETS2006 benchmark data.