Details
| Original language | English |
|---|---|
| Pages (from-to) | 87-97 |
| Number of pages | 11 |
| Journal | Control engineering practice |
| Volume | 48 |
| Publication status | Published - 2016 |
Abstract
Many stroke patients suffer from the drop foot syndrome, which is characterized by a limited ability to lift the foot and leads to a pathological gait. We consider treatment of this syndrome via Functional Electrical Stimulation (FES) of the peroneal nerve during the swing phase of the paretic foot. We highlight the role of feedback control for addressing the challenges that result from the large individuality and time-variance of muscle response dynamics. Unlike many previous approaches, we do not reduce the control problem to the scalar case. Instead, the entire pitch angle trajectory of the paretic foot is measured by means of a 6D Inertial Measurement Unit (IMU) and controlled by an Iterative Learning Control (ILC) scheme for variable-pass-length systems. While previously suggested controllers were often validated for the strongly simplified case of sitting or lying subjects, we demonstrate the effectiveness of the proposed approach in experimental trials with walking drop foot patients. Our results reveal that conventional trapezoidal stimulation intensity profiles may produce a safe foot lift, but often at the cost of too high intensities and an unphysiological foot pitch motion. Starting from such conservative intensity profiles, the proposed learning controller automatically achieves a desired foot motion within one or two strides and keeps adjusting the stimulation to compensate time-variant muscle dynamics and disturbances.
Keywords
- Biomedical engineering application, Functional electrical stimulation, Inertial measurement unit, Iterative learning control, Motor impairment, Realtime motion analysis
ASJC Scopus subject areas
- Mathematics(all)
- Applied Mathematics
- Engineering(all)
- Electrical and Electronic Engineering
- Engineering(all)
- Control and Systems Engineering
- Computer Science(all)
- Computer Science Applications
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In: Control engineering practice, Vol. 48, 2016, p. 87-97.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Iterative learning control of a drop foot neuroprosthesis—Generating physiological foot motion in paretic gait by automatic feedback control
AU - Seel, Thomas
AU - Werner, Cordula
AU - Raisch, Jörg
AU - Schauer, Thomas
N1 - Publisher Copyright: © 2016 Elsevier Ltd.
PY - 2016
Y1 - 2016
N2 - Many stroke patients suffer from the drop foot syndrome, which is characterized by a limited ability to lift the foot and leads to a pathological gait. We consider treatment of this syndrome via Functional Electrical Stimulation (FES) of the peroneal nerve during the swing phase of the paretic foot. We highlight the role of feedback control for addressing the challenges that result from the large individuality and time-variance of muscle response dynamics. Unlike many previous approaches, we do not reduce the control problem to the scalar case. Instead, the entire pitch angle trajectory of the paretic foot is measured by means of a 6D Inertial Measurement Unit (IMU) and controlled by an Iterative Learning Control (ILC) scheme for variable-pass-length systems. While previously suggested controllers were often validated for the strongly simplified case of sitting or lying subjects, we demonstrate the effectiveness of the proposed approach in experimental trials with walking drop foot patients. Our results reveal that conventional trapezoidal stimulation intensity profiles may produce a safe foot lift, but often at the cost of too high intensities and an unphysiological foot pitch motion. Starting from such conservative intensity profiles, the proposed learning controller automatically achieves a desired foot motion within one or two strides and keeps adjusting the stimulation to compensate time-variant muscle dynamics and disturbances.
AB - Many stroke patients suffer from the drop foot syndrome, which is characterized by a limited ability to lift the foot and leads to a pathological gait. We consider treatment of this syndrome via Functional Electrical Stimulation (FES) of the peroneal nerve during the swing phase of the paretic foot. We highlight the role of feedback control for addressing the challenges that result from the large individuality and time-variance of muscle response dynamics. Unlike many previous approaches, we do not reduce the control problem to the scalar case. Instead, the entire pitch angle trajectory of the paretic foot is measured by means of a 6D Inertial Measurement Unit (IMU) and controlled by an Iterative Learning Control (ILC) scheme for variable-pass-length systems. While previously suggested controllers were often validated for the strongly simplified case of sitting or lying subjects, we demonstrate the effectiveness of the proposed approach in experimental trials with walking drop foot patients. Our results reveal that conventional trapezoidal stimulation intensity profiles may produce a safe foot lift, but often at the cost of too high intensities and an unphysiological foot pitch motion. Starting from such conservative intensity profiles, the proposed learning controller automatically achieves a desired foot motion within one or two strides and keeps adjusting the stimulation to compensate time-variant muscle dynamics and disturbances.
KW - Biomedical engineering application
KW - Functional electrical stimulation
KW - Inertial measurement unit
KW - Iterative learning control
KW - Motor impairment
KW - Realtime motion analysis
UR - http://www.scopus.com/inward/record.url?scp=84954547339&partnerID=8YFLogxK
U2 - 10.1016/j.conengprac.2015.11.007
DO - 10.1016/j.conengprac.2015.11.007
M3 - Article
VL - 48
SP - 87
EP - 97
JO - Control engineering practice
JF - Control engineering practice
SN - 0967-0661
ER -