Adaptive neurofuzzy inference system for an active transfemoral prosthetic leg using in-socket sensory system / Nur Hidayah Mohd Yusof
Prosthetic leg is known as one of the solutions to help lower limb amputees to regain their ambulation ability. However, most of the current existing knee components still lack in the ability to provide active body propulsion, which in turn results in higher metabolic energy consumption required...
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| Format: | Thesis |
| Published: |
2019
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| Subjects: | |
| Online Access: | http://studentsrepo.um.edu.my/10599/1/Nur_Hidayah_Mohd_Yusof.jpg http://studentsrepo.um.edu.my/10599/8/hidayah.pdf http://studentsrepo.um.edu.my/10599/ |
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| Summary: | Prosthetic leg is known as one of the solutions to help lower limb amputees to regain
their ambulation ability. However, most of the current existing knee components still lack
in the ability to provide active body propulsion, which in turn results in higher metabolic
energy consumption required by the amputee in doing locomotion movement. This study
was motivated by the idea of developing a microprocessor controlled prosthetic leg with
user intention recognition for transfemoral amputees which is able to mimic a manner of
ambulation close to that of healthy individuals. In particular, this study focused on
developing the control input framework in order to derive the used intention, as read by
the in-socket sensory system, as control input into the main controller. The first part of
this work is the development of an Adaptive Neurofuzzy Inference System (ANFIS) -
based algorithm to human gait phase recognition. The gait phase is necessary for cadence
and torque control required by the knee joint mechanism. In the second part, the sensing
method was adopted using the in-socket sensors embedded into the transfemoral socket
of the prosthetic leg. A transfemoral amputee walked with in-socket sensorised prosthetic
leg performed several gait cycles and the in-socket sensor signals were used as an input
into the newly developed ANFIS system. Physical simulation of the controller presented
a realistic simulation of the actuated knee joint in terms of knee mechanism, proving that
the novel ANFIS system was validated with real amputee experimental signals. The fuzzy
system successfully replicated human gait cycle by categorizing the cycle into seven gait
phases. In conclusion, this study had established an ANFIS-based control input
framework using in-socket sensory system based on amputee user’s seven phases of gait
cycle. |
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