| 1. |
- Brondin, Anna, et al.
(författare)
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Open source step counter algorithm for wearable devices
- 2020
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Ingår i: Companion Proceedings of the 10th International Conference on the Internet of Things (IoT 2020). - New York, United States : ACM Digital Library. - 9781450388207
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Konferensbidrag (refereegranskat)abstract
- Commercial wearable devices and fitness trackers are commonly sold as black boxes of which little is known about their accuracy. This poses serious issues especially in health-related contexts such as clinical research, where transparency about accuracy and reliability are paramount.We present a validated algorithm for computing step counting that is optimised for use in constrained computing environments. Released as open source, the algorithm is based on the windowed peak detection approach, which has previously shown high accuracy on smartphones. The algorithm is optimised to run on a programmable smartwatch (Pine Time) and tested on 10 subjects in 8 scenarios, with varying varying positions of the wearable and walking paces.Our approach achieves a 89% average accuracy, with the highest average accuracy when walking outdoor (98%) and the lowest in a slow-walk scenario (77%). This result can be compared with the built-in step counter of the smartwatch (Bosch BMA421), which yielded a 94% average accuracy for the same use cases. Our work thus shows that an open-source approach for extracting physical activity data from wearable devices is possible and achieves an accuracy comparable to the one produced by proprietary embedded algorithms.
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| 2. |
- Maus, Benjamin, et al.
(författare)
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Enhancing citizens trust in technologies for data donation in clinical research : validation of a design prototype
- 2020
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Ingår i: Companion Proceedings of the 10th International Conference on the Internet of Things (IoT 2020). - New York, NY, USA : ACM Digital Library. - 9781450388207
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Konferensbidrag (refereegranskat)abstract
- Mobile phones, wearable trackers and Internet of Things devices continuously produce data about our health and lifestyle that can be used for medical research. However, how data is accessed, by whom and for what purpose is not always understood. This lack of transparency undermines citizens trust in the use of such technologies for research purposes. This paper proposes a set of 6 use cases and related mock-up interfaces for citizen science, mobile-based health research: “Curated information about the institution”, “Sequential consent of shared data”, “Updates from the institution”, “Privacy notifications”, “Overview of donated data” and “Personal impact in medical research”. Interviews and Kano analysis of the interfaces with 6 prospective users show that all except “Privacy notifications” are perceived as important and beneficial for increasing users’ trust. The defined use cases can guide the development of future data collection platforms.
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