LASO: Exploiting Locomotive and Acoustic Signatures over the Edge to Annotate IMU Data for Human Activity Recognition
Links to Fileshttps://dl.acm.org/doi/abs/10.1145/3382507.3418826
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Type of Work10 pages
conference papers and proceedings
Citation of Original PublicationSoumyajit Chatterjee, Avijoy Chakma, Aryya Gangopadhyay, Nirmalya Roy , Bivas Mitra and Sandip Chakraborty, LASO: Exploiting Locomotive and Acoustic Signatures over the Edge to Annotate IMU Data for Human Activity Recognition, ICMI '20: Proceedings of the 2020 International Conference on Multimodal Interaction, Pages 333–342, https://doi.org/10.1145/3382507.3418826
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Annotated IMU sensor data from smart devices and wearables are essential for developing supervised models for fine-grained human activity recognition, albeit generating sufficient annotated data for diverse human activities under different environments is challenging. Existing approaches primarily use human-in-the-loop based techniques, including active learning; however, they are tedious, costly, and time-consuming. Leveraging the availability of acoustic data from embedded microphones over the data collection devices, in this paper, we propose LASO, a multimodal approach for automated data annotation from acoustic and locomotive information. LASO works over the edge device itself, ensuring that only the annotated IMU data is collected, discarding the acoustic data from the device itself, hence preserving the audio-privacy of the user. In the absence of any pre-existing labeling information, such an auto-annotation is challenging as the IMU data needs to be sessionized for different time-scaled activities in a completely unsupervised manner. We use a change-point detection technique while synchronizing the locomotive information from the IMU data with the acoustic data, and then use pre-trained audio-based activity recognition models for labeling the IMU data while handling the acoustic noises. LASO efficiently annotates IMU data, without any explicit human intervention, with a mean accuracy of $0.93$ ($\pm 0.04$) and $0.78$ ($\pm 0.05$) for two different real-life datasets from workshop and kitchen environments, respectively.