Signaligner Pro: A Tool to Explore and Annotate Multi-day Raw Accelerometer Data

• DOI: 10.1109/percomworkshops51409.2021.9431110
• Publication Date: 2021-05-24T20:26:56Z
• Authors: Aditya Ponnada; Seth Cooper; Qu Tang; Binod Thapa-Chhetry; Josh Aaron Miller; Dinesh John; Stephen Intille
• Keywords: USable; Activity Recognition

Human activity recognition using wearable accelerometers can enable in-situ detection of physical activities to support novel human-computer interfaces. Many the machine-learning-based algorithms require multi-person, multi-day, carefully annotated training data with precisely marked start and end times interest. To date, there is a dearth usable tools that researchers conveniently visualize annotate multiple days high-sampling-rate raw accelerometer data. Thus, we developed Signaligner Pro, an interactive tool explore multi-day high-sampling rate The visualizes time-stamped annotations generated by existing human annotators; then be directly modified create their own, improved, datasets. In this paper, describe tool's features implementation facilitate convenient exploration annotation demonstrate its use in generating annotations.