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.

翻译：在移动中追踪人体姿态对健身、移动游戏、环境感知虚拟助手以及康复训练等领域具有强大应用潜力。然而，用户通常不愿专门购买并穿戴特殊服装或传感器阵列来实现这一目标。为此，本研究探索利用用户已拥有的设备（即智能手机、智能手表和耳机）中的IMU进行人体姿态估计的可行性。该方法面临诸多挑战，包括低成本商用IMU产生的噪声数据，以及用户身体上可检测点的数量既稀疏又处于动态变化之中。我们的处理流程可接收任意可用IMU数据子集（可能仅来自单个设备），并输出最佳估计姿态。为评估模型性能，我们构建了IMUPoser数据集，该数据集采集自10名佩戴或手持商用现成设备的参与者在多种活动场景下的数据。我们通过在自己构建的及现有IMU数据集上进行基准测试，对系统进行了全面评估。