Details
| Original language | English |
|---|---|
| Pages (from-to) | 1205-1213 |
| Number of pages | 9 |
| Journal | Medical Engineering and Physics |
| Volume | 38 |
| Issue number | 11 |
| Early online date | 7 Jul 2016 |
| Publication status | Published - 1 Nov 2016 |
| Externally published | Yes |
Abstract
Many stroke patients suffer from the drop foot syndrome, which is characterized by a limited ability to lift (the lateral and/or medial edge of) the foot and leads to a pathological gait. In this contribution, we consider the treatment of this syndrome via functional electrical stimulation (FES) of the peroneal nerve during the swing phase of the paretic foot. A novel three-electrodes setup allows us to manipulate the recruitment of m. tibialis anterior and m. fibularis longus via two independent FES channels without violating the zero-net-current requirement of FES. We characterize the domain of admissible stimulation intensities that results from the nonlinearities in patients’ stimulation intensity tolerance. To compensate most of the cross-couplings between the FES intensities and the foot motion, we apply a nonlinear controller output mapping. Gait phase transitions as well as foot pitch and roll angles are assessed in realtime by means of an Inertial Measurement Unit (IMU). A decentralized Iterative Learning Control (ILC) scheme is used to adjust the stimulation to the current needs of the individual patient. We evaluate the effectiveness of this approach in experimental trials with drop foot patients walking on a treadmill and on level ground. Starting from conventional stimulation parameters, the controller automatically determines individual stimulation parameters and thus achieves physiological foot pitch and roll angle trajectories within at most two strides.
Keywords
- Biomedical systems, Drop foot syndrome, Foot motion assessment, Functional electrical stimulation, Gait phase detection, Inertial sensor, Iterative learning control, Multivariable control systems, Neuroprosthetics, Rehabilitation engineering, Validation by experiments
ASJC Scopus subject areas
- Biochemistry, Genetics and Molecular Biology(all)
- Biophysics
- Engineering(all)
- Biomedical Engineering
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In: Medical Engineering and Physics, Vol. 38, No. 11, 01.11.2016, p. 1205-1213.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - The adaptive drop foot stimulator – Multivariable learning control of foot pitch and roll motion in paretic gait
AU - Seel, Thomas
AU - Werner, Cordula
AU - Schauer, Thomas
N1 - Funding Information: This work was conducted within the research project BeMobil, which is supported by the German Federal Ministry of Research and Education (FKZ 16SV7069K).
PY - 2016/11/1
Y1 - 2016/11/1
N2 - Many stroke patients suffer from the drop foot syndrome, which is characterized by a limited ability to lift (the lateral and/or medial edge of) the foot and leads to a pathological gait. In this contribution, we consider the treatment of this syndrome via functional electrical stimulation (FES) of the peroneal nerve during the swing phase of the paretic foot. A novel three-electrodes setup allows us to manipulate the recruitment of m. tibialis anterior and m. fibularis longus via two independent FES channels without violating the zero-net-current requirement of FES. We characterize the domain of admissible stimulation intensities that results from the nonlinearities in patients’ stimulation intensity tolerance. To compensate most of the cross-couplings between the FES intensities and the foot motion, we apply a nonlinear controller output mapping. Gait phase transitions as well as foot pitch and roll angles are assessed in realtime by means of an Inertial Measurement Unit (IMU). A decentralized Iterative Learning Control (ILC) scheme is used to adjust the stimulation to the current needs of the individual patient. We evaluate the effectiveness of this approach in experimental trials with drop foot patients walking on a treadmill and on level ground. Starting from conventional stimulation parameters, the controller automatically determines individual stimulation parameters and thus achieves physiological foot pitch and roll angle trajectories within at most two strides.
AB - Many stroke patients suffer from the drop foot syndrome, which is characterized by a limited ability to lift (the lateral and/or medial edge of) the foot and leads to a pathological gait. In this contribution, we consider the treatment of this syndrome via functional electrical stimulation (FES) of the peroneal nerve during the swing phase of the paretic foot. A novel three-electrodes setup allows us to manipulate the recruitment of m. tibialis anterior and m. fibularis longus via two independent FES channels without violating the zero-net-current requirement of FES. We characterize the domain of admissible stimulation intensities that results from the nonlinearities in patients’ stimulation intensity tolerance. To compensate most of the cross-couplings between the FES intensities and the foot motion, we apply a nonlinear controller output mapping. Gait phase transitions as well as foot pitch and roll angles are assessed in realtime by means of an Inertial Measurement Unit (IMU). A decentralized Iterative Learning Control (ILC) scheme is used to adjust the stimulation to the current needs of the individual patient. We evaluate the effectiveness of this approach in experimental trials with drop foot patients walking on a treadmill and on level ground. Starting from conventional stimulation parameters, the controller automatically determines individual stimulation parameters and thus achieves physiological foot pitch and roll angle trajectories within at most two strides.
KW - Biomedical systems
KW - Drop foot syndrome
KW - Foot motion assessment
KW - Functional electrical stimulation
KW - Gait phase detection
KW - Inertial sensor
KW - Iterative learning control
KW - Multivariable control systems
KW - Neuroprosthetics
KW - Rehabilitation engineering
KW - Validation by experiments
UR - http://www.scopus.com/inward/record.url?scp=84994491696&partnerID=8YFLogxK
U2 - 10.1016/j.medengphy.2016.06.009
DO - 10.1016/j.medengphy.2016.06.009
M3 - Article
C2 - 27396367
AN - SCOPUS:84994491696
VL - 38
SP - 1205
EP - 1213
JO - Medical Engineering and Physics
JF - Medical Engineering and Physics
SN - 1350-4533
IS - 11
ER -