Browsing by Author "ElMasry, Noha"

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ISwimCoach: A Smart Coach guiding System for Assisting Swimmers Free Style Strokes
(MeC'20 Workshop, 10/29/2020) Ehab, Mohamed; Ahmed, Hossam Mohamed Mohamed; Hammad, Mostafa; ElMasry, Noha; Atia, Ayman
In sports, coaching remains an essential aspect of the efficiency of the athlete’s performance. This paper proposes a wrist wearable assistant for the swimmer called iSwimCoach. The key aim behind the system is to detect and analyze incorrect swimming patterns in a free crawl swimming style using an accelerometer sensor. iSwimCoach collects patterns of a swimmer’s stream which enables it to detect the strokes to be analyzed in real-time. Therefore, introducing quick and efficient self-coaching feature for mid-level athlete to enhance their swimming style. In our research, we were able to monitor athlete strokes underwater and hence assist swimming coaches. The proposed system was able to classify four types of strokes done by mid-level players (correct strokes, wrong recovery, wrong hand entry and wrong high elbow). The system informs both the swimmer and the coach when an incorrect movement is detected. iSwimCoach achieved 91% accuracy for the detection and classification of incorrect strokes by a fast non expensive dynamic time warping algorithm. These readings analyzed in real-time to automatically generate reports for the swimmer and coach.
MSR-YOLO: Method to Enhance Fish Detection and Tracking in Fish Farms
(Elsevier, 2020-02) Mohamed, Hussam El-Din; Fadl, Ali; Anas, Omar; Wageeh, Youssef; ElMasry, Noha; Nabil, Ayman; Atia, Ayman
Tasks involving the monitoring of fish farms such as controlling fish ponds is one of the expensive and difficult tasks for fish farmers. Usually, fish farmers are doing these tasks manually which costs them time and money. We propose a system that automates the monitoring of the fish farm. This paper presents a technique to enhance the detection of fish and their trajectories in challenging water conditions. Firstly, we used image enhancement techniques to enhance unclear water images and to better identify fish. Then, we applied an object detection algorithm to detect fish. Finally, the detected objects’ coordinates are then used to extract features like count and trajectories. All experiments were done on our experimental setup. The technique showed promising results in regards to detection and tracking accuracy when applied
The Wanderer: Implementing markerless augmented reality with object position awareness
(IEEE, 2018) Samir, Mina; Hanie, Ahmed; Aboulgheit, Aly; Hossam, Karim; Atia, Ayman; ElMasry, Noha
With the rapid advancement of technology, augmented reality has been very successful in a lot of industries like medical, education and entertainment. We offer an application called The Wanderer which is a markerless augmented reality game. It aims to enhance object position awareness indoors and outdoors by merging geolocation technology and object detection algorithms that allow the virtual objects to be superimposed on a real object. The geolocation is used for filtering the dataset by getting the location of the user enabling the developers to know the nearby objects from the user and making them interact with these objects. The object detection uses ORB Algorithm for feature extraction and FLANN algorithm for finding the nearest neighbour of the extracted frames. The quests will be superimposed by using the markerless augmented realitys algorithm object detection and with the help of the smart phone's sensor, the item will be augmented on real time objects, other features of the game will be applied using the same logic. Moreover, helping with charitable events, donations, and advertisements the Wanderer has a big potential in aiding with extra curricular activities and putting organizations on the map.