trackle GmbH
Miriam Imhausen is an experienced Social Media Manager with a strong background in video production and marketing. Currently serving as Social Media-Managerin at trackle GmbH since March 2022, previous roles include Social Media Managerin at intumind GmbH and Creative Video Producer at Urlaubsguru GmbH, where responsibilities encompassed video creation, Social Media management, and optimization. Earlier experience includes a trainee role at Ekom Communication That Works GmbH, internships in video production, and involvement in student recruitment and public relations at Jade Hochschule. Miriam holds a Bachelor of Arts in Medienwirtschaft & Journalismus from Jade Hochschule Wilhelmshaven and has completed an Online Marketing Manager program specializing in Social Media at the Social Media Akademie.
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trackle GmbH
What is trackle?trackle is a fully integrated wearable system that helps women to identify their individual fertility window by tracking the body core temperature.What makes trackle relevant right now? With increasing age, the likelihood of getting pregnant lowers rapidly. Women who want to have kids lately need to know exactly about theirfertility window in order to increase chances of getting pregnant. Same applies for women who want to avoid pregnancy without hormone treatments. For them, it is important to know when their ovulation happens in order to choose the right means for contraception. Knowing the individual fertility window becomes more and more relevant to a growing group of women. What is the problem trackle is solving? Identifying that rise of the body core temperature is challenging – especially as you need to avoid all noise that influences body core temperature. That is why women measure core temperature first thing in the morning before having moved. Thus, they operate with approximate values they enter into old fashioned scale paper graphs.How does trackle work?The wearable sensor is worn vaginal overnight and determines the core temperature every hour. It regularly transmits the values via a wireless connection to a gateway, which passes it on to a server. On the server it is processed and provided for the user’s mobile app. Thus, the one and only relevant value can be identified easily: it is the lowest value measured overnight. In comparison to the other values taken the nights before, ovulation can be identified very securely and with our mature machine learning system, we are able to predict the exact timing of ovulation closer the more data we receive. The gateway serves as a safe and discrete storage box for the sensor also.