Recovery of motor function after stroke: a polymyography-based analysis.

We present a method for assessing muscle activation patterns during goal-directed movement. We present a cohort study from a randomized clinical trial that followed the recovery of motor function during and after intensive gait training, assisted by sensor-driven, four-channel electrical stimulation. The instrument that we developed allows for the simultaneous recordings of up to 16 channels that are wirelessly sent to a host computer, which then provides feedback to the subject. The inputs to the portable instrument support electromyography (EMG) amplifiers, inertial sensors and goniometers. We show that this method is sensitive enough to show changes in muscle activation patterns in stroke patients before and after gait training (four weeks, five days a week, 30 min daily). We also show that the recovery decreases the differences between patterns of muscle activities (e.g., levels of muscle activations and median frequencies) assessed in hemiplegic and healthy subjects. This method allows for the analysis of muscle contributions and activation patterns; therefore, it might be possible to better understand the physiology behind the recovery of function. This EMG analysis provides a quantification of recovery that is a valuable addition to other measures, such as the Fugl-Meyer score, the Berg-Balance score, gait speed, and the symmetry index.

Sensor-driven four-channel stimulation of paretic leg: functional electrical walking therapy.

This study introduces a Functional Electrical Therapy (FET) system based on sensor-driven electrical stimulation for the augmentation of walking. The automatic control relates to the timing of stimulation of four muscles. The sensor system comprises accelerometers and force-sensing resistors. The automatic control implements IF-THEN rules designed by mapping of sensors and muscle activation patterns. The new system was tested in 13 acute stroke patients assigned to a FET group or a control (CON) group. Both groups were treated with a standard rehabilitation program and 45min of walking daily for 5 days over the course of 4 weeks. The FET group received electrical stimulation during walking. The Fugl-Meyer (FM) test for the lower extremities, Barthel Index (BI), mean walking velocity (v(mean)) over a 6-m distance, and Physiological Cost Index (PCI) were assessed at the entry point and at the end of the treatment. Subjects within the FET and CON groups had comparable baseline outcome measures. In the FET group, we determined significant differences in the mean values of all outcomes between the entry and end points of treatment (p<0.05), contrary to the CON group where we found no significant differences (p>0.05). We also found significant differences in the changes of FM, BI, v(mean) and PCI which occurred during the 4 weeks of treatment between the FET and CON groups (p<0.05). The statistical strength of the clinical study was low (<70%), suggesting the need for a larger, randomized clinical trial.

Control aspects of motor neural prosthesis: sensory interface.

A neural prosthesis (NP) has two applications: permanent assistance of function, and temporary assistance that contributes to long-term recovery of function. Here, we address control issues for a therapeutic NP which uses surface electrodes. We suggest that the effective NP for therapy needs to implement rule-based control. Rule-based control relies on the triggering of preprogrammed sequences of electrical stimulation by the sensory signals. The sensory system in the therapeutic NP needs to be simple for installation, allow self-calibration, it must be robust, and sufficiently redundant in order to guarantee safe operation. The sensory signals need to generate control signals; hence, sensory fusion is needed. MEMS technology today provides sensors that fulfill the technical requirements (accelerometers, gyroscopes, force sensing resistors). Therefore, the task was to design a sensory signal processing method from the mentioned solid state sensors that would recognize phases during the gait cycle. This is necessary for the control of multi channel electrical stimulation. The sensory fusion consists of the following two phases: 1) estimation of vertical and horizontal components of the ground reaction force, center of pressure, and joint angles from the solid-state sensors, and 2) fusion of the estimated signals into a sequence of command signals. The first phase was realized by the use of artificial neural networks and adaptive neuro-fuzzy inference systems, while the second by the use of inductive learning described in our earlier work [1].