Analysis of human body kinematics using a hybrid markerless video acquisition and processing

A main objective of the human movement analysis is the quantitative description of joint kinematics and kinetics. This information may have great possibility to address clinical problems both in orthopaedics and motor rehabilitation. Previous studies have shown that the assessment of kinematics and kinetics from stereophotogrammetric data necessitates a setup phase, special equipment and expertise to operate. Besides, this procedure may cause feeling of uneasiness on the subjects and may hinder with their walking. The general aim of this thesis is the implementation and evaluation of new 2D markerless techniques, in order to contribute to the development of an alternative technique to the traditional stereophotogrammetric techniques. At first, the focus of the study has been the estimation of the ankle-foot complex kinematics during stance phase of the gait. Two particular cases were considered: subjects barefoot and subjects wearing ankle socks. The use of socks was investigated in view of the development of the hybrid method proposed in this work. Different algorithms were analyzed, evaluated and implemented in order to have a 2D markerless solution to estimate the kinematics for both cases. The validation of the proposed technique was done with a traditional stereophotogrammetric system. The implementation of the technique leads towards an easy to configure (and more comfortable for the subject) alternative to the traditional stereophotogrammetric system. Then, the abovementioned technique has been improved so that the measurement of knee flexion/extension could be done with a 2D markerless technique. The main changes on the implementation were on occlusion handling and background segmentation. With the additional constraints, the proposed technique was applied to the estimation of knee flexion/extension and compared with a traditional stereophotogrammetric system. Results showed that the knee flexion/extension estimation from traditional stereophotogrammetric system and the proposed markerless system were highly comparable, making the latter a potential alternative for clinical use. A contribution has also been given in the estimation of lower limb kinematics of the children with cerebral palsy (CP). For this purpose, a hybrid technique, which uses high-cut underwear and ankle socks as “segmental markers” in combination with a markerless methodology, was proposed. The proposed hybrid technique is different than the abovementioned markerless technique in terms of the algorithm chosen. Results showed that the proposed hybrid technique can become a simple and low-cost alternative to the traditional stereophotogrammetric systems.

Hybrid Methods for Resource Allocation and Scheduling Problems in Deterministic and Stochastic Environments

This work presents exact, hybrid algorithms for mixed resource Allocation and Scheduling problems; in general terms, those consist into assigning over time finite capacity resources to a set of precedence connected activities. The proposed methods have broad applicability, but are mainly motivated by applications in the field of Embedded System Design. In particular, high-performance embedded computing recently witnessed the shift from single CPU platforms with application-specific accelerators to programmable Multi Processor Systems-on-Chip (MPSoCs). Those allow higher flexibility, real time performance and low energy consumption, but the programmer must be able to effectively exploit the platform parallelism. This raises interest in the development of algorithmic techniques to be embedded in CAD tools; in particular, given a specific application and platform, the objective if to perform optimal allocation of hardware resources and to compute an execution schedule. On this regard, since embedded systems tend to run the same set of applications for their entire lifetime, off-line, exact optimization approaches are particularly appealing. Quite surprisingly, the use of exact algorithms has not been well investigated so far; this is in part motivated by the complexity of integrated allocation and scheduling, setting tough challenges for ``pure'' combinatorial methods. The use of hybrid CP/OR approaches presents the opportunity to exploit mutual advantages of different methods, while compensating for their weaknesses. In this work, we consider in first instance an Allocation and Scheduling problem over the Cell BE processor by Sony, IBM and Toshiba; we propose three different solution methods, leveraging decomposition, cut generation and heuristic guided search. Next, we face Allocation and Scheduling of so-called Conditional Task Graphs, explicitly accounting for branches with outcome not known at design time; we extend the CP scheduling framework to effectively deal with the introduced stochastic elements. Finally, we address Allocation and Scheduling with uncertain, bounded execution times, via conflict based tree search; we introduce a simple and flexible time model to take into account duration variability and provide an efficient conflict detection method. The proposed approaches achieve good results on practical size problem, thus demonstrating the use of exact approaches for system design is feasible. Furthermore, the developed techniques bring significant contributions to combinatorial optimization methods.