89 lines
2.8 KiB
Python
89 lines
2.8 KiB
Python
work_packages = [{
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'title': 'Inclusive Gesture Recognition Models (4 PM)',
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'duration':9,
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'start': 0,
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'method': """
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Develop an initial gesture recognition baseline using existing datasets (e.g.,
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$Q, public gesture datasets with diverse motion pattern). Apply unsupervised ML
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techniques like Dynamic Time Warping (DTW) for feature extraction. Build on
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these techniques for pretraing supervised ML datasets while identifying biases.
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Check disparity in model accuracy across participant groups. Assure user studies
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will be conducted in compliance with GDPR and relevant ethics approvals.
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""",
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}, {
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'title': 'Motor Variability in Interaction',
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'duration': 7,
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'start': 6,
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'method': """
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Conduct at least 3 mixed-method user studies with ~30 participants across three
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groups: (1) ageing adults (n≈10), (2) participants with motor
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impairments/disabilities (n≈10), and (3) expert movers/performers (n≈10). Both
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quantitative kinematic data (motion capture, wearable sensors) and qualitative
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data (interviews, observations) will be collected. Motor variability will be
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modelled using clustering algorithms (e.g., k-means, Gaussian mixture models)
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and nonlinear statistical analyses to capture individual patterns. Build
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statistical models to capture variability. The prototype developed in WP1 will
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serve as the baseline architecture for the user studies in WP2. Fuse statistical
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insights with supervised ML to allow the system to adapt rather than enforce
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rigid categories.
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""",
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} , {
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'title': 'Inclusive, Embodied, and Ecofeminist System Refinement',
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'duration': 5,
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'start': 14,
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'method': """
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"""
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} , {
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'title': 'Dissemination and Public Engagement',
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'start': 10,
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'duration': 13,
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'method': """
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"""
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}]
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deliverables = [{
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'title': 'Framework specification',
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'month': 4,
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'wp': 1
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}, {
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'title': 'Baseline prototype',
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'month': 8,
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'wp': 1
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}, {
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'title': 'Annotated Dataset',
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'month': 12,
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'wp': 2,
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}, {
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'title': 'Technical Report on Motor Variability',
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'month': 13,
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'wp': 2,
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}, {
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'title': 'Updated prototype with integrated supervised ML and explainability mechanisms',
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'month': 15,
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'wp': 3,
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}, {
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'title': 'Design and evaluation framework for inclusive, interpretable ML in movement systems',
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'month': 18,
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'wp': 3,
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}]
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milestones = [{
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'title': 'Baseline Gesture Model and Bias Evaluation Metrics Defined',
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'month': 8,
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'wp': 1,
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}, {
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'title': 'Completion of User Studies and Initial Statistical Variability Models',
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'month': 12,
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'wp': 2
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}, {
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'title': 'Completion of Transparent Model Integration and Ethical Evaluation Setup',
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'month': 13,
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'wp': 3
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}]
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