## 1 Introduction  
Annotations upon recordings are statements that enrich the curated content, by classifying these recordings, or parts thereof, with respect to a vocabulary or by associating other media objects that provide further information; e.g., the identification of an event, or an explanatory narration of what is happening in the recorded scene. In traditional crafts and the production of handmade products, gestures are part of the production process and may be specific to a technique or the use of a tool. Photographic and video recordings were conventionally used to demonstrate actions. However, in this way, there is no measurement of motion that can serve documentation purposes and accurate re-enaction of the recorded actions. More recently, Motion Capture (MoCap) technologies enabled the acquisition of measurements that are essential in documenting human activity.  
We call motion segmentation the task of separating regions, features, or trajectories from a motion recording that correspond to individual craft actions into coherent subsets of space and time. Motion segmentation is key in the comprehension of human activities because it enables the association of movements with entries in gesture vocabularies. By articulating motion into gestures, a more insightful understanding of an activity is achieved because individual gestures can be isolated, studied, and practiced. Using reference gestures, such as for rocking a treadle, sliding a shuttle, and pulling at the batter, weaving on a loom can be understood as the iteration of three actions,rather than a continuous stream of hand and feet trajectories. In other words, motion segmentation requires action/gesture recognition. Pertinent methods have been proposed for visual methods and MoCap, e.g., [1; 2; 3; 4; 5; 6].  
What underlies recognition methods is the requirement for training examples. In the early days, model-based recognition was explored with an emphasis in well-defined gesture vocabularies, such as those of sign languages [7; 8]. In these cases, gestures models were a priori specified and available in an analytical and unambiguous form. More recently, computer vision, parallel computing, and machine learning make the definition of gesture vocabularies based on examples more feasible [9; 10]. The requirement of analytical gesture models has, thus, been reduced to the provision of unambiguous training data, meaning that (preferably several) examples of each vocabulary gesture are available.  
In our case, the term training data means datasets of a person performing the activity of interest, annotated per semantic action we wish to reference. In this context, training datasets have been developed [11; 12] that offer recordings of individual gestures, where an actor performs the gestures in the environment of a motion capture laboratory. The segmentation of the examples is provided \"by construction\" as training examples contain a gesture in each one. Existing datasets refer to generic gestures and activities (i.e., the CMU Graphics Lab Motion Capture Database [[http://mocap.cs.cmu.edu/](http://mocap.cs.cmu.edu/)]([http://mocap.cs.cmu.edu/](http://mocap.cs.cmu.edu/))), but no datasets for craft practice are available.  
This work proposes a workflow for the creation of multimodal datasets. We consider that the proposed approach will be used from professionals, not the technological domain but the cultural heritage domain, whether they are curators or practitioners. As such, the proposed approach has been designed with simplicity and using known user-interface metaphors, borrowed from video playback. The objective of this design approach was to ensure that the outcome will be usable by all targeted stakeholders requiring minimum prior knowledge and domain expertise. The approach is validated in the context of motion recordings from two activities involved in traditional crafts, namely weaving and mastic cultivation.