A third major advantage of motion tracking in rehabilitation systems is that it enables biofeedback that informs the patients about their own motion and positive or negative aspects of that motion and their performance ( Zhi et al., 2018), for example in a virtual reality environment. At the same time, such sensor systems facilitate objective recording and assessment of the patients' motor performance, such as speed of execution, completion of tasks and reaction times ( Oña et al., 2018).ģ. This requires sufficiently precise sensor systems that yield real-time measurements of the currently conducted motion.Ģ. Feedback control is commonly used to adjust the motion support to the individual patient in real time and thereby enable the execution of accurate movements ( Marchal-Crespo and Reinkensmeyer, 2009 Schauer, 2017). The role of sensor systems in such rehabilitation systems is 3-fold:ġ. These systems actively support patients during motions that they cannot perform sufficiently well or not often enough without support. Robot-assisted rehabilitation and Functional Electrical Stimulation (FES) are well-known technologies and popular means for enhancement of the physical therapy in modern rehabilitation settings ( Oujamaa et al., 2009 McCabe et al., 2015). Stroke patients can often additionally benefit from regained motor functions due to the therapy. Primary objectives during rehabilitation training are the enhancement of patients' health situation and self-sufficiency. As a result, patients are often gravely impaired in activities of daily living for the rest of their lives. Spinal cord injury or stroke can lead to movement disorders like a paresis of the upper limb ( Gowland et al., 1992 Popovic and Sinkjaer, 2000). The results indicate that wearable inertial sensors and end-effector-based robots can be combined to provide means for effective rehabilitation therapy with likewise detailed and accurate motion tracking for performance assessment, real-time biofeedback and feedback control of robotic and neuroprosthetic motion support.ġ.
Using a camera-based system as a ground truth, we demonstrate that the shoulder position and the elbow angle are tracked with median errors around 4 cm and 4°, respectively and that undesirable compensatory shoulder movements, which were defined as shoulder displacements greater ☑0 cm for more than 20% of a motion cycle, are detected and classified 100% correctly across all 445 performed motions. Experimental data from five healthy subjects who performed 282 proper executions of a typical rehabilitation motion and 163 executions with compensation motion are evaluated. We apply this method to an upper-limb rehabilitation robotics use case in which the orientation and position of the forearm and elbow are known, and the orientation and position of the upper arm and shoulder are estimated by the proposed method using an inertial sensor worn on the upper arm. It uses a quaternion-based algorithm to track the heading of a limb segment in real time by combining the gyroscope and accelerometer readings with position measurements of one point along that segment. In contrast, we propose a magnetometer-free sensor fusion method. Most existing inertial motion tracking approaches rely on a homogeneous magnetic field and thus fail in indoor environments and near ferromagnetic materials and electronic devices.
We demonstrate in one particular experimental setup that this limitation can be overcome by augmenting an end-effector-based robot with a wearable inertial sensor. However, measurement information is obtained only about the motion of the limb segments to which the systems are attached and not about the adjacent limb segments. Control Systems Group, Technische Universität Berlin, Berlin, GermanyĮnd-effector-based robotic systems provide easy-to-set-up motion support in rehabilitation of stroke and spinal-cord-injured patients.
Arne Passon *, Thomas Schauer and Thomas Seel