Sharif Digital Repository

Researchers working at Mowafaghian Rehabilitation Research Center have decided to develop a telerehabilitation system named SEPANTA, especially designed for activity recognition of Parkinson's Disease patients. In this regard, the system uses 34 mobility exercises, including 20 LSVT-BIG activities (especially designed for PD patients) and 14 functional daily activities. Human Activity Recognition (HAR) systems faces various challenges e.g., intra-class variabilities, meaning differences in an activity performance by different persons or a person. Data augmentation and utilizing deep learning models are the most common solutions for the risen challenges. However, deep structures require an... 

Human facial activity recognition is one of the endeavors to improve human-computer interaction. Recognition of excitements and emotions on human face by machine and makinga corresponding reaction is essential for man machine intraction.The purpose of this project is recognizingactivities such as speaking, eating, laughing, agree and disagree which have more complexity than usualemotions such as fear and happinesscontained in common datasets.So, adataset in accordance with the above mentioned 5 activities was collected and the appropriate feature vector for analyzing these face activities were implemented.Distance between the interest points located on the face were used as parameters in... 

Smartphone sensors like accelerometer, gyroscope and magnetometer are very common nowadays. This gives us the opportunity for sensor-based activity recognition. This thesis's goal is to collect data from different smartphone sensors and then extract hand-crafted features and classify them using machine learning algorithms. Metro, bus, taxi, bicycle, running, upstairs, walking and standing are studied activities in this thesis. All above steps are covered in this research, later we want to present an activity recognition model and then test it through a web server, after that, we modify the model by proposing to change learning coefficient to gain better accuracy. Finally, an Android app was... 

Ambient Intelligence (AmI) is an environment in which devices are embedded and connected to each other with a communication network, working in concert to predict users’ wishes according to the context of the environment (devices and people) to help them with their everyday activities. An ambient intelligent environment should be context-aware. One of the most complicated problems in context-aware computations is recognition of the activities in which users of the environment are engaged. These activities could be recognized by means of the information hidden in communication networks of the devices, especially different sensors embedded in the environment to ease up the process. Most of... 

In this research, we have used machine learning methods to approach the problem of human activity recognition. As the process of labeling the data in this problem is so costly and time consuming, and regarding the copious available unlabeled data, semi supervised methods have a high performance in this problem. In recent years, graph based methods have became very populaer among semi supervised learning methods. However, constructing a graph on the data which presents their structure in a proper manner has remained a main challenge in these methods. One of the causes of this problem is the existance of the shortcut edges. In this report, we will first introduce a method to solve the problem... 

Human activity recognition is an important and useful area in computer vision that application include surveillance systems, patient monitoring systems, human-computer interaction and analyse video data from big websites.Traditional Human action recognition use the RGB videos as default input that unable describe motion and action as full. On the other hand Kinect camera sendsthe RGB data to output in addition to the Depth Data that allows us to extract skeleton of human easily. Recently Space-time features have been particulary popular in RGB Videos because of their structure. These features are describedby their descriptor and send the good and important information to output.Finally we... 

In recent years, social robots have used simple and complicated context-aware systems to provide better services for users. The navigation module is one of the most important modules in which a context-aware system can be used; Where a social robot can change the way it navigates with respect to the context that the users are experiencing in a manner that fits the cultural background of the users. This kind of navigation helps the robot to keep its users safe both psychologically and physically. In this research, we present a context-aware navigation method that changes its proxemics due to the group context the users are experiencing. This research consists of three main parts: first, a... 

Human action recognition is one of the computer vision branches. Video surveillance and human-computer interaction is among its modern applications. The main goal of this subject is to label RGB-D videos which are captured from acting human. Therefore, labeling the input videos among pre-learned action is called as action recognition.The action recognition problem consists of two primary parts. The first part is selecting a suitable descriptor to describe input videos, and the other part is the way that the system has been learned based on the learning action data. The main goal of this research title is to propose a method for recognizing each action video (that is acted by human and... 

Parkinson's disease is a neurodegenerative disorder that affects motor functions. Performing mobility exercises help patients slowing down the progression of the illness and improving symptoms of the disease. Assessment and evaluation of activities of mobility exercises are critical for any treatment program particularly in tele-rehabilitation system. The purpose of the current study is to design an affordable and accurate wearable device with inertial measurement units (IMUs) for mobility activity recognition in Parkinson’s patients. The optimum number and arrangement (i.e. configuration) were found to minimize the cost while maintaining a fair accuracy. The activity recognition was... 

Parkinson’s disease is considered as a progressive neurodegenerative disease that hasn’t any certain treatment. In Iran until 1390, there were about 150 thousand patient struggling with this disease. Rehabilitation is known as an effective treatment to decrease destructive progress of the disease. Because of motional problems of PD patients, it is hard to come to the clinics. So developing remote rehabilitation would be interested by researchers and occupational therapists. Therefore in the recent years, an activity recognition system has been developed in Mowafaghian research center. This system is based on IMU sensors and a NM classifier.These systems are challenging with some problems,... 

Talking face synthesis is a process in which is made using audio-visual data or its features. Because the face is the first output, face animation plays a crucial role in this process. A high-quality face, a balance between different facial regions, natural movements of facial organs, and the like are basic requirements to synthesize a relatively realistic talking face. There are a wide variety of applications for the photo-realistic talking face. For instance, as a teaching assistant, or reading emails and e-books are only two simple ones to mention. To reach a realistic talking face with mentioned necessary requirements, we set a goal to consider all face regions and their movements. To... 

Parkinson’s disease (PD) is a neurodegenerative disorder and during the last few years considerable measures have been taken to rehabilitate its patients. To prevent the disorder from deteriorating and to control its progress, patients have to undergo some therapy sessions that incorporate some mobility exercises e.g. walking, sitting up and down, and etc. Since transporting the patients to the clinical centers is too burdensome, growing attention is drawn towards telerehabilitation. To this end, DMRCINT has developed a telerehab system for PD patients. This system is an intelligent classifier that uses features of linear acceleration and angular velocity signals to detect the activity that... 

Human Activity Recognition (HAR) has had tremendous improvements in the field of elderly monitoring and telerehabilitation. An anchor point for HAR systems in telerehabilitation is supervising rehabilitative excercises. For Parkinson’s disease (PD) patients, a group of rehabilitative activities, known as Lee Silverman Voice Treatment-BIG, or LSVT-BIG, have shown to be effective in improving motor performance. Similar to any rehabilitative measure, delivering these activities requires the supervision of an expert or clinician, so that the patient receives proper feedbacks. HAR systems can replace human experts. They can recognize activities and provide the user with proper feedback. HAR... 

Parkinson's is a disease caused by a disorder in the central nervous system of the body. There is no definite cure for this disease, but one of the ways to prevent the progress of this disease is to use movement therapy. One of the goals of designing wearable systems consisting of inertial sensors is to make it possible to perform this movement therapy from a distance. The purpose of the present study and research is to use the approach of simple and complex activities in order to increase the accuracy in the detection of activities and also to solve the problems of the previous system, with the help of creating the ability to detect complex meaningful activities for Parkinson's patients. In...