Heading Judgment for the WaistMounted MIMU Using LSTM In many athlete or firefighter training practices, such as firefighter rescue training or football player dribbling, many movement trajectories will be generated. Recording the exercise data of these trainees can replay the training process and help to improve their learning. In the generation of motion trajectory, the judgment of the motion heading matters and attracts lots of attentions. A large amount of researches have focused on the footmounted Miniature Inertial Measurement Unit (MIMU), mainly relying on zerovelocity update (ZUPT) to maintain accuracy. However, ZUPT fails to estimate the gyroscope drift errors, causing the heading angle drifts over time. In this paper, we introduce the waistmounted MIMU for heading judgment based on deep learning algorithms to alleviate the heading angle drift problem. Although the market is more demanding for the waistmounted module, there are fewer applications due to its insufficient accuracy. In order to improve the heading judgment of waistmounted model, we propose to use the stacked LSTM model which we prove is powerful to assist the traditional Pedestrian Dead Reckoning (PDR) in the trajectory drawing, and thus is helpful for the heading judgment. Experimental results show that the proposed model has satisfactory performance in heading judgment.

Indoor Trajectory Reconstruction of Walking, Jogging, and Running Activities Based on a FootMounted Inertial Pedestrian DeadReckoning System The evaluation of trajectory reconstruction of the human body obtained by footmounted Inertial Pedestrian DeadReckoning (IPDR) methods has usually been carried out in controlled environments, with very few participants and limited to walking. In this study, a pipeline for trajectory reconstruction using a footmounted IPDR system is proposed and evaluated in two large datasets containing activities that involve walking, jogging, and running, as well as movements such as side and backward strides, sitting, and standing. First, stride segmentation is addressed using a multisubsequence Dynamic Time Warping method. Then, detection of ToeOff and MidStance is performed by using two new algorithms. Finally, stride length and orientation estimation are performed using a Zero Velocity Update algorithm empowered by a complementary Kalman filter. As a result, the ToeOff detection algorithm reached an Fscore between 90% and 100% for activities that do not involve stopping, and between 71% and 78% otherwise. Resulting return position errors were in the range of 0.5% to 8.8% for nonstopping activities and 8.8% to 27.4% otherwise. The proposed pipeline is able to reconstruct indoor trajectories of people performing activities that involve walking, jogging, running, side and backward walking, sitting, and standing.