Abstract

Although commercial motion-capture systems have been widely used in various applications, the complex setup limits their application scenarios for ordinary consumers. To overcome the drawbacks of wearability, human posture reconstruction based on a few wearable sensors have been actively studied in recent years. In this paper, we propose a deep-learning-based sparse inertial sensor human posture reconstruction method. This method uses bidirectional recurrent neural network (Bi-RNN) to build an a priori model from a large motion dataset to build human motion, thereby the low-dimensional motion measurements are mapped to whole-body posture. To improve the motion reconstruction performance for specific application scenarios, two fundamental problems in the model construction are investigated: training data selection and sparse sensor placement. The problem of deep-learning training data selection is to select independent and identically distributed (IID) data for a certain scenario from the accumulated imbalanced motion dataset with sufficient information. We formulate the data selection into an optimization problem to obtain continuous and IID data segments, which comply with a small reference dataset collected from the target scenario. A two-step heuristic algorithm is proposed to solve the data selection problem. On the other hand, the optimal sensor placement problem is studied to exploit most information from partial observation of human movement. A method for evaluating the motion information amount of any group of wearable inertial sensors based on mutual information is proposed, and a greedy searching method is adopted to obtain the approximate optimal sensor placement of a given sensor number, so that the maximum motion information and minimum redundancy is achieved. Finally, the human posture reconstruction performance is evaluated with different training data and sensor placement selection methods, and experimental results show that the proposed method takes advantages in both posture reconstruction accuracy and model training time. In the 6 sensors configuration, the posture reconstruction errors of our model for walking, running, and playing basketball are 7.25°, 8.84°, and 14.13°, respectively.

Highlights

  • Motion capture has a wide range of applications in medical rehabilitation, virtual reality, sports training and other fields [1,2,3]

  • The position and number of sensors placed on the human body affect the performance of human posture reconstruction, so we studied the optimal selection of the sensor placement to improve the reconstruction accuracy for target applications

  • We propose methods for training data selection and sensor position selection in sparse inertial sensor human posture reconstruction under specific application scenarios

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Summary

Introduction

Motion capture has a wide range of applications in medical rehabilitation, virtual reality, sports training and other fields [1,2,3]. The current inertial motion-capture devices require subjects to wear 17 or more inertial sensors [4]. It can be intrusive, timeconsuming, and prone to sensor misplacement during mounting. Many studies have shown that human motion includes a lot of redundant information and can be described by dimensions lower than the degree of freedom of human motion [5,6,7]. This opens the door to the study of using sparse inertial sensors human motion capture. To improve the ease of use of inertial motion-capture technology, in recent years researchers have begun to pay attention to motion-capture technology based on fewer wearable inertial sensors [8,9,10]

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