# A Digitally Enhanced Ethnography for Craft Action and Process Understanding
## 3 Proposed Method
### Overview of the Proposed Method  
The proposed methodology is presented in Figure 2. It follows a structured, interdisciplinary approach to documenting, analyzing, and digitally representing traditional craft processes. It begins with ethnographic preparation and fieldwork planning, which lays the foundation for effective data collection. This phase involves conducting background research to understand the specific craft, including its techniques, tools, materials, workshops, and products. Prior knowledge is crucial as it helps researchers engage meaningfully with practitioners and accurately interpret their techniques. To facilitate interdisciplinary collaboration and ensure consistent terminology, a multimodal glossary is created. This glossary includes nouns and verbs describing manufacturing processes, craft-specific tools, and idioms unique to particular cultural or technical contexts. It is enriched with multimedia elements to illustrate nuanced concepts that may not be effectively conveyed through text alone. Additionally, the LCT method is employed to analyze how knowledge is transmitted, how practitioners develop expertise over time, and how professional trajectories evolve. This allows for a deeper understanding of how craft knowledge is acquired and sustained. Once the foundational knowledge has been established, the methodology moves into the recording and digital representation phase. Here, researchers work closely with practitioners to select and define key crafting actions. This process involves breaking down actions into their fundamental gestures and understanding their dependencies and sequential relationships. Flow charts and process diagrams are co-designed with practitioners to ensure they align with traditional ways of thinking about the craft. Digital representations of these action schemas help structure the documentation process. In the next step, researchers capture and digitize the craft process using a combination of audiovisual recording, MoCap, and other sensing modalities. A structured scenario and storyboard guide the recordings, ensuring consistency across different practitioners and settings. The recording setup is designed to capture not only the motions of the practitioner but also the transformations that occur in the materials they manipulate. This includes recording dynamic entities such as material deformations, changes in texture, and environmental interactions. Tools such as volumetric video, depth video, and photographic stills are used to document these transformations comprehensively. Furthermore, the digital representation extends beyond the practitioner's movements to include objects, workpieces, and workspaces. These elements are digitized to provide a complete understanding of the crafting environment and its impact on the practitioner's workflow. Figure 1: Overview of fundamental principles employed by proposed representation. After the recording phase, the analysis and representation step begins. The collected data are systematically organized and imported into a knowledge base, where they are annotated with metadata. Parsing techniques are applied to segment the motion data into meaningful units, allowing researchers to isolate and analyze specific gestures. This segmentation is crucial for developing structured representations of crafting actions. To enhance understanding, video elicitation techniques are employed, where practitioners watch and comment on their recorded actions. These verbal annotations provide insights into the cognitive processes, intentions, and decision-making strategies behind each movement. This phase also includes structured semantic annotation, linking gestures to broader action schemas and functional relationships. Figure 2: Method overview. The final step of the methodology involves the structured digital representation of craft knowledge. Actions are modeled within an ontology-based framework, ensuring that relationships between materials, tools, and gestures are clearly defined.