title: Non-weight-bearing neural control of a powered transfemoral prosthesis.
abstract: Lower limb prostheses have traditionally been mechanically passive devices without electronic control systems. Microprocessor-controlled passive and powered devices have recently received much interest from the clinical and research communities. The control systems for these devices typically use finite-state controllers to interpret data measured from mechanical sensors embedded within the prosthesis. In this paper we investigated a control system that relied on information extracted from myoelectric signals to control a lower limb prosthesis while amputee patients were seated. Sagittal plane motions of the knee and ankle can be accurately (>90%) recognized and controlled in both a virtual environment and on an actuated transfemoral prosthesis using only myoelectric signals measured from nine residual thigh muscles. Patients also demonstrated accurate (~90%) control of both the femoral and tibial rotation degrees of freedom within the virtual environment. A channel subset investigation was completed and the results showed that only five residual thigh muscles are required to achieve accurate control. This research is the first step in our long-term goal of implementing myoelectric control of lower limb prostheses during both weight-bearing and non-weight-bearing activities for individuals with transfemoral amputation.
authors.affiliation: Center for Bionic Medicine, Rehabilitation Institute of Chicago, IL, USA. l-hargrove@northwestern.edu
authors.forename: Levi J
authors.initials: LJ
authors.lastname: Hargrove
authors.forename: Ann M
authors.initials: AM
authors.lastname: Simon
authors.forename: Robert
authors.initials: R
authors.lastname: Lipschutz
authors.forename: Suzanne B
authors.initials: SB
authors.lastname: Finucane
authors.forename: Todd A
authors.initials: TA
authors.lastname: Kuiken
mesh_terms: D000674:Amputees / Q000534:rehabilitation*; D001186:Artificial Limbs; D001696:Biomechanical Phenomena; D006801:Humans; D007719:Knee Joint; D010363:Pattern Recognition, Automated; D011474:Prosthesis Design / Q000295:instrumentation*; D012371:Robotics / Q000295:instrumentation*; D016474:Weight-Bearing