{"id":2067522,"date":"2026-08-10T13:45:44","date_gmt":"2026-08-10T11:45:44","guid":{"rendered":"https:\/\/www.luxsure.fr\/2026\/08\/10\/ifm-x-ecole-42-when-the-fashion-industry-learns-to-code-its-blind-spots\/"},"modified":"2026-08-10T13:48:35","modified_gmt":"2026-08-10T11:48:35","slug":"ifm-x-ecole-42-when-the-fashion-industry-learns-to-code-its-blind-spots","status":"publish","type":"post","link":"https:\/\/www.luxsure.fr\/en\/2026\/08\/10\/ifm-x-ecole-42-when-the-fashion-industry-learns-to-code-its-blind-spots\/","title":{"rendered":"IFM x \u00c9cole 42: When the Fashion Industry Learns to Code Its Blind Spots"},"content":{"rendered":"\n

At VivaTech, the Institut Fran\u00e7ais de la Mode and \u00c9cole 42 brought together nearly 40 students for a Fashion Tech hackathon. Three projects emerged from the event: inclusive virtual try-on, health-tracking jewelry for menopause, and AI-powered merchandising for fashion houses. The most interesting aspect isn\u2019t the technology itself, but what it reveals: fashion is beginning to code its own friction points. <\/p>\n\n

Data is no substitute for trying something on<\/h2>\n\n

EasyTry is based on a common experience: buying a piece of clothing online without knowing if it will fit right. The promise is simple: to integrate an extension into an e-commerce site that allows users to visualize how an item will look on their own body, then generate a fit and comfort score, along with a size recommendation. <\/p>\n\n

The issue is less trivial than it seems. The French apparel market is worth more than 35 billion euros, 30% of which is generated online. Yet online shopping is still hampered by very tangible uncertainties: fit, fabric, posture, and comfort. EasyTry adds an often-overlooked factor to this equation: accessibility. In France, 14 million people report having a severe functional limitation; for some of them, getting dressed is already a daily challenge. <\/p>\n\n

The project does not claim to eliminate doubt. Rather, it attempts to define it. In the fashion world, that\u2019s already a lot. <\/p>\n\n

Jewelry as a Health Interface<\/h2>\n\n

Continuum shifts the focus to a more intimate topic: menopause. The project takes the form of a ring capable of detecting the early signs of a hot flash\u2014heart rate, body temperature, movement\u2014and then activating localized cooling and pulsations designed to guide breathing. <\/p>\n\n

The project\u2019s appeal lies in its rejection of mere tracking. For several years now, connected devices have been capable of measuring. They are less capable of acting at the right moment. Continuum therefore poses a question that is more profound than it seems: what good is a beautiful object if it does nothing to enhance the experience? <\/p>\n\n

The proposed ring combines a PPG sensor, thermal sensors, an accelerometer, a microcontroller, and a Peltier module. The idea has yet to be prototyped, but the intuition is strong: the jewelry of tomorrow might no longer merely signal a status, but restore a form of discreet control. <\/p>\n\n

Details<\/h2>\n\n

Luma, the third project, tackles an area that is less visible but central to the Maisons: the loss of in-store feedback. A sales associate hears that a pair of sandals is considered uncomfortable; the information then makes its way through a manager, a regional office, WhatsApp messages, Excel files, and sometimes phone calls. At each step, some detail gets lost. <\/p>\n\n

Luma offers an agent accessible via WhatsApp to collect feedback from sales associates, followed by a dashboard capable of transforming this raw data into summaries, visualizations, analyses, and merchandising recommendations. Qualitative data is finally receiving the same level of attention that has long been reserved for quantitative data. <\/p>\n\n

This may be the project with the most direct application to the luxury sector. For it does not seek to predict an abstract desire; rather, it organizes an existing but scattered body of local memory. <\/p>\n\n

The fashion industry doesn’t need more technology\u2014it needs better translations<\/h2>\n\n

These three projects convey the same message from three different perspectives. EasyTry translates the body into the digital shopping experience. Continuum translates physiological discomfort into a discreet intervention. Luma translates the in-store voice into collection intelligence. <\/p>\n\n

So the common thread isn’t AI. It’s translation. <\/p>\n\n

For fashion houses, the challenge is not to generate more data or to add another layer of technology to the customer journey. The challenge is to capture what current systems miss: the hesitation before a purchase, the unspoken discomfort, the sales associate\u2019s comment, the subtle nuances of a fitting, and the friction of use. <\/p>\n\n

Fashion has long excelled at interpreting the body, movement, and desire. The risk would be to entrust this interpretation to generic tools, incapable of distinguishing useful data from noise. The IFM x \u00c9cole 42 hackathon points to a more appropriate path: integrating technology into the grammar of fashion, rather than bending fashion to the logic of the platform. <\/p>\n\n

The next step will be less spectacular: testing, measuring, refining, and prototyping. But that\u2019s where the difference between a flashy demonstration and a method will be decided. In the luxury industry, an idea is never valued for its initial brilliance. It is valued for its ability to hold up in the real world. <\/p>\n\n

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