In this paper, we consider a dynamical system whose trajectory is a result of minimizing a multiphase cost function. The multiphase cost function is assumed to be a weighted sum of specified features (or basis functions) with phase-dependent weights that switch at some unknown phase transition points. A new inverse optimal control approach for recovering the cost weights of each phase and estimating the phase transition points is proposed. The key idea is to use a length-adapted window moving along the observed trajectory, where the window length is determined by finding the minimal observation length that suffices for a successful cost weight recovery. The effectiveness of the proposed method is first evaluated on a simulated robot arm, and then, demonstrated on a dataset of human participants performing a series of squatting tasks. The results demonstrate that the proposed method reliably retrieves the cost function of each phase and segments each phase of motion from the trajectory with a segmentation accuracy above 90%.

Inertial measurement units (IMUs) enable human motion measurement in any environment, which can be useful for human robot interaction, exoskeletons, and active prosthetics. This paper proposes an approach for estimating the orientation between a wearable IMU sensor and the body frame of the wearer using a simple and fast calibration procedure. The proposed approach uses the generalized Hebbian algorithm to incrementally estimate the axis aligned with gravity using acceleration measurements obtained during a static pose, and the axis perpendicular to the saggital plane using gyro measurements obtained during sagittal plane movements. An automated convergence criterion based on the sensor measurement variance is used. The proposed approach is tested in simulation and with human movement and demonstrates excellent and fast calibration performance.

Movement primitive segmentation enables long sequences of human movement observation data to be segmented into smaller components, termed movement primitives, to facilitate movement identification, modeling, and learning. It has been applied to exercise monitoring, gesture recognition, human-machine interaction, and robot imitation learning. This paper proposes a segmentation framework to categorize and compare different segmentation algorithms considering segment definitions, data sources, application-specific requirements, algorithm mechanics, and validation techniques. The framework is applied to human motion segmentation methods by grouping them into online, semionline, and offline approaches. Among the online approaches, distance-based methods provide the best performance, while stochastic dynamic models work best in the semionline and offline settings. However, most algorithms to date are tested with small datasets, and algorithm generalization across participants and to movement changes remains largely untested.

To facilitate human-robot interactions with the user, it is necessary for the robot to identify the interaction partner. We propose the use of a single wearable sensor worn at the center of the user's belt to record the gait when the interaction partner approaches the robot. Based on the data of a single gait cycle recorded with a single inertial measurement unit (IMU), we identify a person by his/her walking style. For identification, we first detect individual strides. We introduce a simple feature that characterizes the individual's asymmetry of gait and classify the individual using a Bayes classifier. To evaluate our approach, we collect motion data from 20 persons; the classification accuracy based on the proposed asymmetry-based feature reaches 99.3%. We further investigate the robustness of our approach against slight variations in the sensor placement, variations in speed, and walking straight versus walking on a curved route.