Intille and colleagues (Ponnada et al., 2019, 2021), affiliated with the TIME study, have developed customized tool, such as the Signaligner Pro, that can provide simultaneous visual display as well as annotations of possible physical activity statuses at the individual level (e.g., wake, sleep, walking, running, biking). Signaligner Pro is pre-packaged with three algorithms that enable classification of different types of physical activities, sleep/wear/non-wear statuses, as well as segments of problematic data that warrant closer investigation or possible data removal. New extensions are currently underway to allow integration of information from Signaligner Pro with that from other data streams.
The following paper, Signaligner Pro: A Tool to Explore and Annotate Multi-day Raw Accelerometer Data, provides the details of how the tool works. https://api.semanticscholar.org/CorpusID:235206472. This paper can also be found by searching: Aditya Ponnada, Seth Cooper, Qu Tang, Binod Thapa-Chhetry, Josh Miller, Dinesh John, and Stephen Intille. (2021, March). Signaligner Pro: A Tool to Explore and Annotate Multi-day Raw Accelerometer Data. In 2021 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops). IEEE, pp. 475-480, doi: 10.1109/PerComWorkshops51409.2021.9431110.
It is also available as open-access on PMC: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8389719/
