One of the most important senses for living is vision. Millions of people living in this world deal with visual impairment. These people are exposed to many problems in their daily life.

          The blind and visually impaired people cannot perform daily tasks by themselves. To perform these tasks, such as finding the objects in their indoor and outdoor environment, they depend on other people. Those people maybe untrusted, or they rely on other senses such as hands, and this interaction between visually impaired people and objects maybe dangerous or even lethal.

          The objective of this thesis is to change the visual world into an audio world by using the smart eyeglasses’ system for notifying the blind people about the static objects in their indoor environment.

         The Smart Eyeglasses is an intelligent system similar to the human eyes in its function, helps the Blind and Visually Impaired People inside the room to detect and recognize the static objects easily and independently at less effort.

         The smart Eyeglasses’ system consists of USB camera, glasses, Raspberry Pi, Power bank and earphones.  The USB camera which is placed on the glasses is used for capturing an image via using Open source Computer Vision (OpenCV). Then, this image is sent to the Raspberry Pi which is a small device similar to the brain of human in its function, used to analyze the image for detecting the objects in the image by using long complex algorithm known as the deep Learning algorithm. When the objects are detected and recognized, the sound in Arabic language will be generated and sent to the earphones which are placed on the ear of visually impaired people.

         The proposed smart Eyeglasses’ system can detect and recognize eight types of objects (TV, Person, Bottle, Chair, Table, Laptop, Printer and water dispenser).

          The code inside the Raspberry Pi is written by python programming language (Python 2). Google Drive cloud storage is used to save the dataset for the purpose of training the deep neural network. The dataset is consisted of 1960 image (eighty-five percent of total images is used for training the neural network while the remainder is used as testing images). Tensorflow is used to build the proposed model. Google Colab cloud service is used to train the deep neural network online. Playsound library is used for the purpose of converting text into sound. YOLO deep learning algorithm is used for detecting and recognizing the objects and it achieved an accuracy of 98%.

          In addition, this thesis demonstrated that YOLOv3 outperforms YOLOv3-Tiny in detection accuracy.

In many domains, gait rehabilitation is an interdisciplinary subject, which can be utilized in sports and health diagnostics since it can measure, characterize and assess many time and space characteristics. Gait rehabilitation can be utilized in stroke rehabilitation therapy to measure the patient’s level of rehabilitation and offer a quantitative reference for the physician to build a tailored walking training program for the patient. This Thesis proposes an intelligent system based on machine learning techniques to assist clinicians by evaluating enormous volumes of data and ensuring a thorough understanding of patient health records. Furthermore, the use of machine learning classifiers greatly aids in the speed and accuracy of diagnosis because machine learning algorithms work on the principle of training. This means their ability to predict disease based on previous data for patients with the same disease, which aids in the speed and ease of diagnosis. The proposed system includes three main phases: preprocessing phase, feature reduction phase, and classification phase. In the first phase, the bandpass filter was used in order to filter input data. Then the resulting data from the previous phase will be reduced using two feature reduction methods. Finally, six Machine Learning (ML) techniques were used to classify patients from healthy people. The proposed system has been tested using two datasets, one of the two datasets is available on the internet and the other is collected from the hospital. The first data contain four classes: healthy, mild, moderate, and severe. This dataset contains information about 169220 patients. The other dataset was collected by using the sEMG device from Foot Drop Patients in Metro Rehabilitation Hospital in Sydney, Australia using Ethical Approval (UTS HREC NO. ETH15-0152). Six classifiers were used for the purpose of helping to diagnose foot diseases and speed I up leg rehabilitation. A set of operations were applied to the data, the most important and most influential was the use of Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) techniques to extract and reduce features. The results obtained indicate that the classifiers K-Nearest Neighbors (KNN) and Random Forest (RF) were the best for the first dataset, where the classification accuracy rate in both of them was 99% while the Naïve Bayes (NB) returned the lowest accuracy rate equal 78%. As for the second dataset, the classifiers K-Nearest Neighbors (KNN), Random Forest (RF), Decision Tree (DR), and Naïve Bayes (NB) shared the highest classification accuracy, which is 100% while the Stochastic Gradient Descent (SGD) and Logistic Regression (LR) returned the lowest accuracy rate equal 98% for both of them.