Ambulatory Assesment of Hand Kinematics using an instrumented glove Kortier H.G. 1 , Schepers H.M. 2 , Sluiter V.I. 1 , Veltink P.H. 1 1 Biomedical Signals and Systems, MIRA Institute, University of Twente, Enschede, The Netherlands 2 Xsens Technologies B.V., Enschede, the Netherlands First author email: h.g.kortier@utwente.nl Inertial and magnetic sensors, attached to various segments of the human hand, can be used to measure movements of the hand. This paper proposes a new method to assess hand kinematics by applying an Extended Kalman Filter in which prior information of the hand is fused with actual measurements obtained from various sensors. Keywords - hand kinematics, inertial movement sensing, instrumented glove, sensor fusion 1. INTRODUCTION Analysis of hand kinematics is important in several application areas, such as rehabilitation, sports and ergonomics. In particular, ambulatory tracking of the whole hand configuration is valuable for kinematic assessment under daily life conditions. Current instrumented glove systems primary use resistive, magnetic or optical sensing methods [1]. A common drawback of these systems is that the sensing elements are mounted across the phalangeal joints, which requires an accurate alignment of body and sensor axes. Moreover, sensors that measure joints with multiple degrees of freedom (DoF) are difficult to calibrate because they often suffer from crosstalk due to misalignments. Finally, current instrumented gloves do not measure the translational and rotational movements of the complete hand, which is important when assessing the functionality of hand movements. Inertial sensors combined with magnetic sensors have proven to be accurate in estimating orientations without the need for external actuators or cameras [2]. The development of MEMS technology resulted in tiny and low-cost Inertial Measurement Units (IMU’s) that could be implemented in textile clothing easily without impairing the freedom of movement and tactile sensations. It is the objective within the PowerSensor project [3,4] to assess the kinematics of the hand using 3D inertial and magnetic sensors, which are attached to the various segments of the hand. To minimize hardware needs, our current version only allows for tracking of the thumb and index finger with 3D accelerometer and magnetometer pairs, and in addition to, tracking of the hand with a full IMU. However, the described algorithm can handle all fingers and thumb in parallel, so as to capture the movement of all degrees of freedom of the hand. 2. METHODS Theory The human hand is a highly articulated system but one that is also highly constrained. It’s skeleton can be modeled with 21 internal DoF (Fig 1). The distal interphalangeal (DIP) joints and proximal interphalangeal (PIP) joints of each finger have one DoF each, while the metacarpophalangeal (MCP) joints have two DoF. Unlike the fingers, the thumb has five DoF. There are two at the trapeziometacarpal (TM) joint, two DoF at the metacarpophalangeal (MCP) joint and the remaining DoF is located at the interphalangeal (IP) joint [1]. Figure 1. (Left) Capital bones of the human hand. Joints are indicated with their abbreviations. (Right) Defined coordinate frames within the various bones of the index finger. DIP PIP MCP IP MCP TM DIP PIP MCP Wrist y z y z y z y z 3DAHM2012 15