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Realtime Gait Phase and Terrain Detection

From Fachgebiet Regelungssysteme TU Berlin

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Abstract

This project is concerned with the development and evaluation of algorithms for the analysis of foot motion in human gait. Inertial measurement units (IMUs) are attached to the feet and provide three-dimensional accelerometer and gyroscope signals. The use of magnetometers is avoided to enable indoor use and ensure robustness in the presence of magnetic disturbances.

The gait cycle is modeled by a finite automaton that describes the gait phases for each side. Gait phase transitions are detected in real time using threshold-based algorithms that automatically adjust their parameters to the individual characteristics of the subject's gait. Beyond this, the orientation and position trajectories of both feet are analyzed to detect stairs, slopes and uneven terrain.

People involved

Description

Fig. 1: State automaton modeling the four phases of human gait and the transitions between these phases.

With only a single inertial sensor attached to the foot, a detailed gait phase detection is realized that separates the gait into four phases: loading response, foot flat, pre-swing, and swing phase. Transitions from one to the next gait phase are detected based on elaborate criteria on the measured accelerations and angular rates as well as their time derivatives and integrals. These transitions drive a state automaton which outputs the current gait phase at any point in time. The resulting gait phase detection is capable of dealing with various walking speeds, arbitrary walking directions, abrupt direction changes, stairs stepping, and unforeseen short-time ground contact in mid-swing.

Fig. 2: Gait phase detection for a single step based on measured accelerations and angular velocities. The depicted measurement signals have been transformed into a global coordinate system in which the z-axis is vertical. Subsequently, accelerations due to gravity have been removed. In the resulting signals, it is clear to see that both angular rates and accelerations almost vanish in foot flat phase, and that the accelerations peak in the moment of heel strike. Based on these and a number of more elaborate criteria, the step is successfully divided into four gait phases.

Besides the current gait phase, the algorithm provides accurate estimates of the foot-to-ground angle trajectory and the foot position trajectory for each step. These trajectories are further processed to recognize the terrain, i.e. stairs, slopes and uneven terrain, that the user is walking on. Based on this information, a feedback-controlled gait neuroprosthesis or exoskeleton might adjust the provided motion support, as realized in the Adaptive Peroneal Stimulator (APeroStim).

Fig. 3: Subject with a gait neuroprosthesis walking on different terrains: stairs, uneven ground, slope. The measurement data of the foot-mounted inertial sensor is analyzed in real time to detect these walking terrains.

All algorithms are designed to work both offline and online, on prosthetic feet, on paretic feet, and on healthy feet. Their accuracy and reliability is compared to those of an optical 3D measurement system using a large number of datasets from several patients and healthy subjects.

Related Publications

D. Graurock, T. Schauer, T. Seel. Automatic Pairing of Inertial Sensors to Lower Limb Segments – A Plug-and-Play Approach. Current Directions in Biomedical Engineering (accepted), 2016.

T. Seel, D. Graurock, T. Schauer. Realtime Assessment of Foot Orientation by Accelerometers and Gyroscopes. Current Directions in Biomedical Engineering, 1 (1):466–469, 2015.
P. Müller, T. Seel, T. Schauer. Experimental Evaluation of a Novel Inertial Sensor Based Realtime Gait Phase Detection Algorithm. In Proc. of the 5th European Conference on Technically Assisted Rehabilitation - TAR 2015, Berlin, Germany, 2015.
T. Seel, L. Landgraf, Víctor Cermeño Escobar, J. Raisch, T. Schauer. Online Gait Phase Detection with Automatic Adaption to Gait Velocity Changes Using Accelerometers and Gyroscopes. Biomedical Engineering / Biomedizinische Techik, 59 (s1):795–798, 2014.

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