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Wearable sensors have become more commonly used in everyday basis and powerful in terms of computational capacity and sensing resources, including capability to collect data from different bio-signals. The data collected from everyday wearables offers huge opportunities to monitor people’s everyday life without expensive laboratory measurements, including also behaviours and conditions only rarely seen in controlled laboratory environments. So far wearable sensors have mostly been used to monitor motion, but bio-sensor powered wearables can do a lot more: they can be used to monitor physiological reactions inside the human body as well as some psycho-physical reactions such as affection and stress. This development enables multiple interesting and important applications, such as early detection of diseases, seizures, and attacks. With stock wearables worn in everyday basis, one of the biggest challenges for such applications is the sensing data itself. In order to train reliable recognition and prediction models, high quality training data with labels needs to be collected. This paper focuses on lessons learned of challenges in data quality and quantity when such data sets are gathered. We discuss our own experiences when collecting data using wearable sensors for early detection of migraine attacks, but the same lessons learned can be generalized to other studies utilizing wearables for recognition medical symptoms and users’ everyday behaviour.

The migraine is a chronic, incapacitating neurovascular disorder, characterized by attacks of severe headache and autonomic nervous system dysfunction, concerning 15% of people in developed countries. It is one of the most understated and incapacitating diseases in the world and costing yearly 111 Billion Euros in Europe only. In our study, we discarded the mechanisms affecting to the migraine but instead the focus was on early detection of the attacks by using human measured biosignals. By using a single, easy and comfortable wearable sensor device the aim was to develop a model that assists individuals to take their medication on time and hence help to avoid the pain in migraine. In this paper, a preliminary study concept is presented as well as the experiences of the data collection using Empatica E4 device is carried out. The experiences are introduced from the point of view of the researchers themselves but also the volunteers actually using the device answered to a short survey about the usability issues as well as gave opinions of the future migraine detection device itself.

The wearable sensor market is currently one of the most rapidly growing area in consumer electronics. One of the most interesting application field for wearable sensors is early detection of diseases, seizures and attacks as it would save a huge amount of money from society. In order to train reliable recognition models for these purposes, a high quality training data needs to be collected. This study concentrates on challenges on gathering such data set based on our own experiences when collecting data using wearable sensors to detect migraine attacks.