2011 06 01 (uned) emadrid aelsaddik uottawa tangible objects for e learning

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The document discusses the creation of tangible learning objects using haptics and multimedia authoring tools. It describes a proposed multimedia authoring tool called HAMLAT that allows non-programmers to integrate haptic, visual and audio elements. An example application discussed is a multilingual handwriting learning and evaluation tool that provides haptic guidance. The tool uses dynamic time warping to measure and evaluate similarities between handwritten letters. Several case studies and demonstrations are proposed to verify the feasibility of identifying patterns with haptics and developing toddler learning interfaces.

Education Technology

Tangible Objects for E-Learning Abdulmotaleb El Saddik FIEEE, FCAE, FEIC

Introduction ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Multimedia and Authoring ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Multimedia Authoring Tools ,[object Object],[object Object]

HAMLAT ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

A Multimedia Handwriting Learning and Evaluation Tool ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Multimedia Learning Tool ,[object Object],[object Object],[object Object],[object Object],[object Object]

Learning Mode -1- ,[object Object],[object Object]

Learning Mode -2- ,[object Object],[object Object],[object Object],[object Object]

$Evaluation Mode -2- ,[object Object],[object Object],[object Object],[object Object],Given two time series // x i is a point in the test trajectory X = x 0 , x 1 , …, x N-1 // y i is a point in the ref. trajectory Y = y 0 , y 1 , …, y T-1 D(x i ,y i ) = sqrt((abs(x i )-abs(y i ))^2 + (ord(x i )-ord(y i ))^2) DTW[0,0] = D(x 0 ,y 0 ) For i=1 to T-1 DTW[0,i] = D(x 0 ,y i ) + DTW[0,i-1] For i=1 to N-1 DTW[i,0] = D(x i ,y 0 ) + DTW[i-1,0] For i=1 to N-1 For j=1 to T-1 DTW[i,j]=D(x i ,y j )+min{DTW[i,j-1],DTW[i-1,j],DTW[i-1,j-1]} Return (DTW[N-1,T-1])$

Performance Evaluation -1- ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Performance Evaluation -2- ,[object Object]

Performance Evaluation -3- ,[object Object]

Performance Evaluation -4- (Demo) The DTW distances for five Japanese characters Character Threshold Distance (x10 3 mm) Easy Medium Difficult Sa 5.5 2.5 1 Shi 4 1 0.5 Chi 5 2.5 1 Tsu 4 1.5 1 Na 10 6 1

Haptic signal prediction, compression, and watermarking Watermark haptic signals to protect copyright and authenticate content.

Case Study: Identifying Human Pattern with Haptics The Experiment Methodology Dynamic Time Warping : + Nelder-Mead non-linear minimization Spectral analysis: Fast Fourier Transform Unsupervised Method: K-Means Graphic Representation Data Trial Timestamp Position X Position Y … Force X ( N) 1 0 0.23344 0.56768 0.00456 1 … .. … … 1 0.0123090 0.37676 0.98976 0,03767

Verifying such feasibility ( demo ) Virtual Check Virtual Mobile Phone Performance Ambient Intelligent Engine

Navigation ,[object Object],[object Object],[object Object]

Toddler Interfaces ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Alphabet Learning Evaluation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Demo 1 Demo 2

Clustering and 3D Mapping Semantic keyword metrics MST tree Overall clustering process design

Sample Interface: Dynamic Traffic Layout

October 4, 2005 Dankie WAD MAHAD SAN TAHAY GADDA GUEY Ευχαριστώ Urakoze Asante متشکرم DMnvwd go raibh maith agaibh

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End-to-end Speech Recognition with Recurrent Neural Networks (D3L6 Deep Learn...

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Mini Project- Audio Enhancement

University of Hertfordshire, School of Electronic Communications and Electrical Engineering

Speech recognition system

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Esophageal Speech Recognition using Artificial Neural Network (ANN)

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International Islamic University

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2011 06 01 (uned) emadrid aelsaddik uottawa tangible objects for e learning

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2011 06 01 (uned) emadrid aelsaddik uottawa tangible objects for e learning

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