The majority of current smart health applications are deployed on a smartphone paired with a smartwatch. The phone is used as the computation platform or the gateway for connecting to the cloud while the watch is used mainly as the data sensing device. In the case of fall detection applications for older adults, this kind of setup is not very practical since it requires users to always keep their phones in proximity while doing the daily chores. When a person falls, in a moment of panic, it might be difficult to locate the phone in order to interact with the Fall Detection App for the purpose of indicating whether they are fine or need help. This paper demonstrates the feasibility of running a real-time personalized deep-learning-based fall detection system on a smartwatch device using a collaborative edge-cloud framework. In particular, we present the software architecture we used for the collaborative framework, demonstrate how we automate the fall detection pipeline, design an appropriate UI on the small screen of the watch, and implement strategies for the continuous data collection and automation of the personalization process with the limited computational and storage resources of a smartwatch. We also present the usability of such a system with nine real-world older adult participants.
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A Survey of Patterns for Adapting Smartphone App UIs to SmartWatches
Wearable devices, such as smart watches and fitness trackers are growing in popularity, creating a need for application developers to adapt or extend a UI, typically from a smartphone, onto these devices. Wearables generally have a smaller form factor than a phone; thus, porting an app to the watch necessarily involves reworking the UI. An open problem is identifying best practices for adapting UIs to wearable devices.
This paper contributes a study and data set of the state of practice in UI adaptation for wearables. We automatically extract UI designs from a set of 101 popular Android apps that have both a phone and watch version, and manually label how each UI element, as well as how screens in the app, are translated from the phone to the wearable. The paper identifies trends in adaptation strategies and presents design guidelines.
We expect that the UI adaptation strategies identified in this paper can have wide-ranging impacts for future research and identifying best practices in this space, such as grounding future user studies that evaluate which strategies improve user satisfaction or automatically adapting UIs.
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- NSF-PAR ID:
- 10191878
- Date Published:
- Journal Name:
- 22nd International Conference on Human-Computer Interaction with MobileDevices and Services (MobileHCI ’20),
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3379503.3403564
