Khaled Safi

Understanding the behavior of the human postural system has become a very attractive topic for many researchers. This system plays a crucial role in maintaining balance during both stationary and moving states. Parkinson’s disease (PD) is a prevalent degenerative movement disorder that significantly impacts human stability, leading to falls and injuries. This research introduces an innovative approach that utilizes a hidden Markov model (HMM) to distinguish healthy individuals and those with PD. Interestingly, this methodology employs raw data obtained from stabilometric signals without any preprocessing. The dataset used for this study comprises 60 subjects divided into healthy and PD patients. Impressively, the proposed method achieves an accuracy rate of up to 98% in effectively differentiating healthy subjects from those with PD.

During the past few years, software-defined networking (SDN) has become a successful architecture that decouples the control plane from the data plane. SDN has the capability to monitor and control the network in a central fashion through a softwarization process. The central element is the controller. For the current SDN architectures, there is an essential need for multiple controllers. The process of placing the controllers efficiently in an SDN environment is called the controller placement problem (CPP). Earlier CPP solutions focused on improving the propagation delays through the capacity of the controllers and the dynamic load on the switches. In this paper, we develop a novel algorithm called dynamic feedback algorithm for controller placement for SDN (DFBCP_SDN). DFBCP_SDN is compared with the varna-based optimization (VBO) towards solving the CPP. We used the VBO as the reference model to this work since it is relatively a new algorithm. Moreover, the VBO extensively outperformed many other existing models. To the best of our knowledge, this is one of the first attempts to minimize the total average latency of SDN using feedback control theoretic techniques. Experimental results indicate that the DFBCP_SDN outperforms the VBO algorithm implemented in two well-known topologies, namely Internet2 OS3E topology and EU-GÉANT topology. We observe that for uncapacitated CPP, the DFBCP_SDN outperforms the VBO for Internet2 OS3E and EU-GÉANT topologies by 11% and 9%, respectively, in terms of total average latency. On the other hand, for capacitated CPP, the DFBCP_SDN algorithm outperforms the VBO reference model by 10% and 8%, respectively.

There has recently been increasing interest in postural stability aimed at gaining a better understanding of the human postural system. This system controls human balance in quiet standing and during locomotion. Parkinson’s disease (PD) is the most common degenerative movement disorder that affects human stability and causes falls and injuries. This paper proposes a novel methodology to differentiate between healthy individuals and those with PD through the empirical mode decomposition (EMD) method. EMD enables the breaking down of a complex signal into several elementary signals called intrinsic mode functions (IMFs). Three temporal parameters and three spectral parameters are extracted from each stabilometric signal as well as from its IMFs. Next, the best five features are selected using the feature selection method. The classification task is carried out using four known machine-learning methods, KNN, decision tree, Random Forest and SVM classifiers, over 10-fold cross validation. The used dataset consists of 28 healthy subjects (14 young adults and 14 old adults) and 32 PD patients (12 young adults and 20 old adults). The SVM method has a performance of 92% and the Dempster–Sahfer formalism method has an accuracy of 96.51%.

The tonic postural system guarantees the stability of the human body in upright stance and during locomotion. It is considered as a complex organization which involves several internal systems and has multiple sources of information. In this paper, we provide a review of techniques and materials for human stability assessment. The primary tools to record and evaluate human sways in static posture are presented such as video-based methods, sensors-based methods and force platform-based methods. Moreover, various protocols and postural conditions in the literature are presented for recording the center of pressure (CoP) displacements. In addition, several relevant studies in the literature are shown and analyzed by highlighting the performances and limitations of the proposed techniques.

The human activity recognition is widely used for human behavior prediction especially for dependent people. This is achieved to provide safety, health monitoring, and well being of this population at home. In this paper, the problem of human activity recognition is reformulated as joint segmentation of multidimensional time series. The hidden Markov model regression (HMMR) is used to perform unsupervised segmentation strategy between activities using the expectation-maximization algorithm. This is accomplished over six logical scenarios of twelve daily activities such as stair descent, standing, sitting down, sitting, From sitting to sitting on the ground and sitting on the ground. To evaluate the performance of HMMR model, other unsupervised methods are used including K-means, Gaussian mixtures model and the hidden Markov model. The results show that the HMMR model provides the best results for the different scenarios with up to 97% in terms of correct classification rate.