In biomechanics, we create details models of human kinematic and dynamic properties of arms, hands, fingers, and legs. These models are needed to understand which properties of human movement are intrinsic---caused by muscles, tendons, ligaments and bones---and which are controlled by the nervous system. Our resulting models are used in the construction and control of novel robotic systems, including prosthetic hands and robotic arms and legs.
Kinematics of the human hand
The amazing manipulation capabilities that we develop show us clearly the versatility of the human hand. But even everyday tasks like picking a coin from a wallet are---from a robotics point of view---utterly impressive. What is so special about our hands?
Pose estimation of a bone. The points are extracted from the MRI images. The bone shown in blue on the left is taken from one MRI image and the one in red on the right from another. The pose estimation algorithm determines the movement that is necessary to match the blue and red points. The blue points on the right show the result of the pose estimation.
In cooperation with Rechts der Isar hospital, Munich, we took a large series (~50 images) of magnetic resonance images (MRI) of a healthy human hand in different postures. MRI allows three-dimensional views of the inside of the human body. The method works by measuring the response of hydrogen atoms inside the body to magnetic stimulation and is – unlike CT imaging – non-ionising.
To derive a kinematic model from the MRI images, we conducted the following steps:
- Segmentation: Highlight the data that belongs to each individual bone.
- Pose estimation: Determine the position and orientation ("pose") of each bone with respect to a reference pose.
- Identification of joint axes: Numerically determine the position and orientation of joint axes that optimally incorporate the measured bone poses, using different joint models (one or two axes, non-intersecting or intersecting axes).
- Build the hand model: Select joint types that appropriately fulfill the compromise between accuracy and complexity, and combine the joints to so-called kinematic chains.
Resulting hand model with 24 degrees of freedom. The index finger metacarpal bone (marked by a black square) is taken as the base of the model. In joints with two axes, the first axis is shown red and the second one green.
For the pose estimation we used an algorithm that the robot Justin uses to identify the location of objects on a table. (The task is similar: Matching three-dimensional point clouds.)
The resulting hand model is shown to the right. The base of the model is the index finger metacarpal bone ("palm bone"), marked by a black square. From there, the kinematic chains extend, indicated by black lines. A kinematic chain is a series of joints, where the position of the last link (in this case the fingertip) depends on the joint angles of all joints in the chain.
The first joint of the thumb is modeled by two non-intersecting axes of rotation, connected by a thick line. The second joint of the thumb also exhibits significant side ward movement and is therefore also modeled by two joint axes, in this case intersecting ones.
The four fingers all have one axis of rotation that allows for a side ward movement and three axes for bending and stretching. The arching of the palm takes place around three axes pointing roughly in the direction of the long axes of the palm bones.
Apart from kinematics, other aspects of the human hand are also important for its fine manipulation abilities, for example touch sensing, motion planning and motion control.
Impedance of the human arm
Defining the Cartesian stiffness matrix of variable-impedance robots is a quite heuristic task. Furthermore, depending on the desired task the stiffness behaviour must be adapted during movement. Humans learn to control limb stiffness from interaction, and we indeed exhibit fine variation of impedance depending on the task and environment. But how? We want to understand the mechanisms for setting and varying impedance in the human arm and hand, and transfer such models to the robotic domain.
Our main goals are
- to understand according to which cost functions biological systems adjust their impedance, and how does intrinsic---defined by the skeletomuscular structure---impedance play a role or, conversely, how and why does the nervous system fluctuate impedance;
- use the gained knowledge to improve body-machine interfaces and to pave the way towards modern impedance teleoperated systems (including prosthetic devices, rehabilitation devices, tele-surgical robotic systems, and so on).
We have developed different impedance measurement methods for identifying impedance of the human fingers, arms, and legs. We combine classical perturbation approaches with EMG-based identification, using force-torque-sensors and optical tracking systems.