  Tracking body pose on-the-go could have powerful uses in fitness, mobile
gaming, context-aware virtual assistants, and rehabilitation. However, users
are unlikely to buy and wear special suits or sensor arrays to achieve this
end. Instead, in this work, we explore the feasibility of estimating body pose
using IMUs already in devices that many users own -- namely smartphones,
smartwatches, and earbuds. This approach has several challenges, including
noisy data from low-cost commodity IMUs, and the fact that the number of
instrumentation points on a users body is both sparse and in flux. Our pipeline
receives whatever subset of IMU data is available, potentially from just a
single device, and produces a best-guess pose. To evaluate our model, we
created the IMUPoser Dataset, collected from 10 participants wearing or holding
off-the-shelf consumer devices and across a variety of activity contexts. We
provide a comprehensive evaluation of our system, benchmarking it on both our
own and existing IMU datasets.
