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- W2965962987 abstract "In the past, computers - whether personal or at work - required a mouse and keyboard to interact with them, and they are still used to this day. Even for video games aphysical tool (controller) is needed to interact with the gaming environment. Previouslythat was acceptable since that was how these electronic devices were conceived, but withthe recent boom in Virtual reality (VR) and Augmented Reality (AR), that reality has already started to change. VR and AR have existed for well over 2 decades [1], yet only inthe last 5 years have they started getting closer to reaching their true potential. With thisnew technology, we can go to virtual worlds, interact with creatures that never previouslyexisted, and visualize information in ways never thought possible before.With the emergence of VR came the need to change the way we interact withthe virtual environment and, with that, the way we interact with technology as a whole.And what better controller for the job than the human hand. If the user can interact withtechnology with hand gestures then the whole process becomes intuitive, eliminating anytraining time, and giving the user a more natural experience. For this, Hand GestureRecognition (HGR) systems will be needed.HGR systems recognize the user’s hand shape by means of a glove, cameras, orbiosignals. One particularly useful biosignal for this task is the forearm Electromyographic (EMG) Signal. This signal reflects the contraction state of the forearm muscles.EMG signals are already being used in prosthetics to help amputees have more naturalcontrol over their prosthetic limbs. They can also be used for translating sign language,or just generally in Human-Machine-Interaction (HMI).This work proposes a method to interact with computers using hand gestures,specifically for a Computer Aided Design (CAD) software known as Solidworks. Toachieve this a commercial EMG armband (the Myo - Thalmic Labs) was used to record8-channel EMG signals from a group of volunteers over the span of 3 visits. The dataset was then preprocessed and segmented. The resulting data set consisted of 10 handgestures performed by 10 subjects, with 162 samples per gesture. A total of 11 featuresets were extracted and applied to 4 different machine learning models.A 9-fold cross validation and testing was performed and the classifiers over all thefeature sets were evaluated and compared. The best model validation performance wasachieved by the Linear Discriminant Analysis (LDA) model with an average Area UnderCurve (AUC) of 76.35% and an average Equal Error Rate (EER) of 29.73%In future work, we propose to use the HGR method developed in this thesis inmultiple applications such as mapping certain shortcut commands in Solidworks (andother applications) to hand gestures." @default.
- W2965962987 created "2019-08-13" @default.
- W2965962987 creator A5088679537 @default.
- W2965962987 date "2018-01-01" @default.
- W2965962987 modified "2023-09-25" @default.
- W2965962987 title "Hand Gesture Recognition via Electromyographic (EMG) Armband for CAD Software CONTROL" @default.
- W2965962987 hasPublicationYear "2018" @default.
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