1. Human Motion Analysis from Inertial Sensor Data Based on Nonlinear Dynamics
- Author
-
Zhong Zhang, Takuma Akiduki, Takashi Imamura, and Tetsuo Miyake
- Subjects
Inertial frame of reference ,Motion field ,Dynamical systems theory ,Control theory ,Inertial measurement unit ,Attractor ,Trajectory ,State space ,ComputerApplications_COMPUTERSINOTHERSYSTEMS ,Dynamical system ,Mathematics - Abstract
This paper discusses the problem of human motion analysis from inertial sensor (accelerometer and gyroscope) data, which is the time sequence data obtained from human motion. The inertial sensors are widely used in the wearable sensing field for understanding human activities. Human motion can be divided into simple movements, such as swinging the arms or legs. These simple movements are fundamentally periodic, and they can be modeled with dynamical systems having periodic attractors. On the other hand, more complex motion like walking can be represented as a sequence of simple movements. To address the problem of analyzing human motion data, we propose a new framework based on a dynamical system. The human motions observed by the inertial sensors are divided into simple movements, and they can be often described as periodical signals. The periodical signals are described by a dynamical system, which can store the periodical or transitional trajectory in a state space by using basin of attraction. The attractors abstract a kind of proto-symbol. The dynamical system having periodical attractors is shown to characterize human motion effectively by exploiting spatiotemporal continuity, because it describes the flow of its transitions into the state space. Moreover, in the designed symbol space from the attractors, the information of human motion dynamics is described in the placement of the points. Thus, each point or transition between points in the symbol space corresponds to a specific type of motion; it can act as a notation method of motion characteristics.
- Published
- 2011