Sharif Digital Repository

Mobile networks have been growing rapidly, where it is of paramount importance to evaluate the performance of any protocol designed for such networks before deployment. Mobility is one of the main mobile network issues that directly affects performance metrics. Since mobile nodes are often carried by their owners, nodes movement should be modeled according to humans movement. In this paper, we extend our previously published human mobility model based on real collected data. The real human movement is traced, cleaned up and analyzed. To the best of our knowledge, our collected data set is the only one that contains the detail of both walking and driving nodes. The analysis shows several... 

Wearable measuring system has major effects on biomechanics of human movements especially in daily activities in order to monitor and analyze the human movements to achieve the most important kinematics parameters. In the recent decade, inertial sensors were utilized by researchers in order to developing wearable system for instrumentation of human movements. In this study, Sharif-Human Movement Instrumentation System (SHARIF-HMIS) was designed and manufactured. The system consists of inertial measurement units (IMUs), stretchable clothing and data logger. The IMU sensors are installed on the human body. The system can be used at home and also industrial environments. The main features of... 

A modular hierarchical structure is developed to describe human movement planning level. The modular feature of the proposed model enables it to generalize planning a task. The movements are planned based on decomposing a task into its corresponding subtasks (motion phases). There is a module responsible for one condition. The final plan is constructed using soft computing of the plans proposed by different modules. Each module estimates the kinematics of the joints at the end of each subtask; we call them kinematic estimator modules (KEMs). A timing module estimates the duration of motion and a gating module determines the responsibility of each KEM under different conditions. To evaluate... 

Human movement analysis is an important part of biomechanics and rehabilitation, for which many measurement systems are introduced. Among these, wearable devices have substantial biomedical applications, primarily since they can be implemented both in indoor and outdoor applications. In this study, a Trunk Motion System (TMS) using printed Body‐Worn Sensors (BWS) is designed and developed. TMS can measure three‐dimensional (3D) trunk motions, is lightweight, and is a portable and non‐invasive system. After the recognition of sensor locations, twelve BWSs were printed on stretchable clothing with the purpose of measuring the 3D trunk movements. To integrate BWSs data, a neural network data... 

In the current paper, we have investigated the generalized FitzHugh-Nagumo model. We have shown that symmetric bursting behaviors of different types could be observed in this model with an appropriate recovery term. A modified version of this system is used to construct bursting activities. Furthermore, we have shown some numerical examples of delayed Hopf bifurcation and canard phenomenon in the symmetric bursting of super-Hopf/homoclinic type near its super-Hopf and homoclinic bifurcations, respectively  

Objectives: The purpose of this paper is to describe the development and evaluation of a new medial linkage reciprocating gait orthosis (MLRGO) that incorporates a reciprocal mechanism and is sensitive to pelvic motion to potentially assist paraplegic patients to walk and provide functional independence. Case description and methods: The new orthosis was constructed and tested by a 20-year-old female paraplegic subject with transverse myelitis at T10 level, who was 4 years post injury and had also been an isocentric reciprocating gait orthosis (IRGO) user for 2 years. She received gait training for 12 weeks before undertaking gait analysis, and also completed a questionnaire that was... 

The brain-computer interfacing (BCI), a platform to extract features and classify different motor movement tasks from noisy and highly correlated electroencephalogram signals, is limited mostly by the complex and power-hungry algorithms. Among different techniques recently devised to tackle this issue, real-time onset detection, due to its negligible delay and minimal power overhead, is the most efficient one. Here, we propose a novel algorithm that outperforms the state-of-the-art design by sixfold in terms of speed, without sacrificing the accuracy for a real-time, hand movement intention detection based on the adaptive wavelet transform with only 1 s detection delay and maximum... 

The interest in wearable systems among the biomedical engineering and clinical community continues to escalate as technical refinements enhance their potential use for both indoor and outdoor applications. For example, an important wearable technology known as a microelectromechanical system (MEMS) is demonstrating promising applications in the area of biomedical engineering. Accordingly, this study was designed to investigate the Sharif-Human Movement Instrumentation System (SHARIF-HMIS), consisting of inertial measurement units (IMUs), stretchable clothing, and a data logger—all of which can be used outside the controlled environment of a laboratory, thus enhancing its overall utility.... 

The aim of this study is to employ feedback control loops to provide a stable forward dynamics simulation of human movement under repeated position constraint conditions in the environment, particularly during stair climbing. A ten-degrees-of-freedom skeletal model containing 18 Hill-type musculotendon actuators per leg was employed to simulate the model in the sagittal plane. The postural tracking and obstacle avoidance were provided by the proportional-integral-derivative controller according to the modulation of the time rate change of the joint kinematics. The stability of the model was maintained by controlling the velocity of the body's centre of mass according to the desired centre of... 

Electromyogram signal (EMG) is an electrical manifestation of contractions of muscles. Surface EMG (sEMG) signal collected from the surface of skin has been used in diverse applications. One of its usages is in pattern recognition of hand prosthesis movements. The ability of current prosthesis devices has been generally limited to simple opening and closing tasks, minimizing their efficacy compared to natural hand capabilities. In order to extend the abilities and accuracy of prosthesis arm movements and performance, a novel sEMG pattern recognizing system is proposed. To extract more pertinent information we extracted sEMGs for selected hand movements. These features constitute our main...