Domestic Users to Master Professional Culinary Arts, a paper co-authored by Daisuke Uriu, Mizuki Namai, Satoru Tokuhisa, Ryo Kashiwagi, Masahiko Inami, and Naohito Okude. This paper was presented at CHI 2012 in Austin, TX and a full list of this paper's references can be found here under the 'References' tab.
Author Bios:
- Daisuke Uriu (Personal Webpage)
- Mizuki Namai (Limited Profile)
- Satoru Tokuhisa (Keio University Webpage)
- Ryo Kashiwagi (Limited Profile)
- Masahiko Inami (Inami Laboratory)
- Naohito Okude (Keio University Webpage)
TL;DR (Summary):
The team discussed Panavi, a sensors-embedded frying pan that is wirelessly connected to a computer system that shows text messages with sounds, analyzes sensor data and user conditions, and provides the user with instructions. Panavi is designed as a way to teach users be expert chefs in a domestic environment without much prior experience. It utilizes projected images, LED indicators, and vibration to interact with the user.
The team then discussed the design process they used to make the pan, including the sensors and related system used to allow users to cook with little or no prior experience. The system has so far included how to prepare pancetta and carbonara, but could be extended to other recipes as well. The user study the team performed included 4 beginner and intermediate cooks using Panavi in a simulated kitchen environment and concluded that the system was beneficial to each person, but that some of the users had problems understanding instructions or picking out the important details in the recipe.
The team concluded that the Panavi system was a success and could be applied to other menus, though it needs a few tweaks before it is ready for public use.
Related Works Not Referenced:
- Smart kindergarten: sensor-based wireless networks for smart developmental problem-solving environments - talks about imbedded wireless sensors in children's toys to enhance learning. Relevant in that it is an embedded sensor that is used to enhance learning, though not related to cooking.
- Cooking procedure recognition and inference in sensor embedded kitchen - talks about an algorithm that shows users instructional videos based on sensory input determining which step the user is at in a recipe. Relevant in that it is trying to teach a user to cook, but does not provide anything more than instructional videos.
- Development of a wearable motion capture suit and virtual reality biofeedback system for the instruction and analysis of sports rehabilitation exercises - talks about a wearable motion capture suit that instructs users in sports rehabilitation exercises. Relevant in the fact that it uses a sensor-embedded suit to help teach users to perform a task, but does not go beyond that.
There was not much other related work that was relevant to this paper, but the works described above were ways to instruct a user to do a task using a 'smart' object. This paper chose to make a 'smart' frying pan which the team did successfully and better than the other cooking instruction paper.
Evaluation:
The team used a qualitative method of evaluation by getting to know the users involved and gathering user input at the end of the experiment. They also used a somewhat objective method of evaluating the final food product from each user by comparing it to a 'perfect' example of the dish. The test was systemic because it tested the use of the entire system to determine if it worked.
Discussion:
This technology is very interesting because it would allow someone with minimal cooking experience to cook on a reasonable level. It would probably need to have a 'beginner' mode (along with other modes) that would go into more or less detail and highlight the more important aspects of each recipe before it could be taken to market. This is a novel idea in that it combines sensor-embedded objects to facilitate cooking instruction, rather than just video or written instruction.
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