Siirtola, P., Peltonen, E., Koskimäki, H., Mönttinen, H., Röning, J., & Pirttikangas, S. (2019). Wrist-worn Wearable Sensors to Understand Insides of the Human Body. The 5th ACM Workshop on Wearable Systems and Applications - WearSys ’19. Presented at the The 5th ACM Workshop. https://doi.org/10.1145/3325424.3329663
Wrist-worn wearable sensors to understand insides of the human body : data quality and quantity
|Author:||Siirtola, Pekka1; Peltonen, Ella1; Koskimäki, Heli1;|
1Faculty of Information Technology and Electrical Engineering, University of Oulu, Finland
|Online Access:||PDF Full Text (PDF, 0.4 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2019121648255
Association for Computing Machinery,
|Publish Date:|| 2019-12-16
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.
|Pages:||17 - 21|
5th ACM Workshop on Wearable Systems and Applications, WearSys 2019, co-located with MobiSys 2019, June 21st Seoul, Korea
ACM Workshop on Wearable Systems and Applications
|Type of Publication:||
A4 Article in conference proceedings
|Field of Science:||
113 Computer and information sciences
213 Electronic, automation and communications engineering, electronics
This research is supported by the Academy of Finland 6Genesis Flagship (grant 318927) and Business Finland funding for Reboot IoT Factory-project1. Authors are also thankful for Infotech Oulu and Nokia Foundation (Jorma Ollila Grant for Dr Ella Peltonen).
|Academy of Finland Grant Number:||
318927 (Academy of Finland Funding decision)
© 2019 Association for Computing Machinery. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in 5th ACM Workshop on Wearable Systems and Applications, WearSys 2019, co-located with MobiSys 2019, June 21st Seoul, Korea, https://doi.org/10.1145/3325424.3329663.