Using Interactive Machine Learning to Support Interface Development Through Workshops with Disabled People
Using Interactive Machine Learning to Support Interface Development Through Workshops with Disabled People
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DOI：
10.1145/2702123.2702474
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发表时间：
2015-04
期刊：
影响因子：
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通讯作者：
S. Katan;M. Grierson;R. Fiebrink
中科院分区：
文献类型：
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作者：
S. Katan;M. Grierson;R. Fiebrink
我们已经将交互式机器学习（IML）应用于与学习和身体残疾人士的六个研讨会中手势控制音乐界面的创建和定制。我们的观察和与参与者的讨论证明了IML作为参与式设计无障碍接口的工具的实用性。这项工作也使人们更好地理解了学习模型的最终用户培训中的挑战，人们如何使用不同类型的预先训练的界面开发个性化的交互策略，以及控制空间和输入设备的属性如何影响人们的定制策略和对仪器的参与。这项工作还揭示了残疾人和专业音乐家的音乐目标和实践之间的相似之处。
We have applied interactive machine learning (IML) to the creation and customisation of gesturally controlled musical interfaces in six workshops with people with learning and physical disabilities. Our observations and discussions with participants demonstrate the utility of IML as a tool for participatory design of accessible interfaces. This work has also led to a better understanding of challenges in end-user training of learning models, of how people develop personalised interaction strategies with different types of pre-trained interfaces, and of how properties of control spaces and input devices influence people's customisation strategies and engagement with instruments. This work has also uncovered similarities between the musical goals and practices of disabled people and those of expert musicians.