Design of wearable system for closed-loop control of gait restoration system by Functional Electrical Stimulation

Arrofiqi, Fauzan; Arifin, Achmad; Indrajaya, Benicditus

doi:10.1109/isitia.2015.7219967

Cited by 7 publications

(4 citation statements)

References 3 publications

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“…Therefore, one future challenge is to work on a wearable designs that are comfortable and aesthetically pleasing. For example, [191] targets the design of the wearable system based on closedloop control of the gait restoration system by functional electrical stimulation. But, this design is specific to the sensor's placement at a single leg.…”

Section: A Wearable Designmentioning

confidence: 99%

Latest Research Trends in Gait Analysis Using Wearable Sensors and Machine Learning: A Systematic Review

et al. 2020

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Gait is the locomotion attained through the movement of limbs and gait analysis examines the patterns (normal/abnormal) depending on the gait cycle. It contributes to the development of various applications in the medical, security, sports, and fitness domains to improve the overall outcome. Among many available technologies, two emerging technologies that play a central role in modern day gait analysis are: A) wearable sensors which provide a convenient, efficient, and inexpensive way to collect data and B) Machine Learning Methods (MLMs) which enable high accuracy gait feature extraction for analysis. Given their prominent roles, this paper presents a review of the latest trends in gait analysis using wearable sensors and Machine Learning (ML). It explores the recent papers along with the publication details and key parameters such as sampling rates, MLMs, wearable sensors, number of sensors, and their locations. Furthermore, the paper provides recommendations for selecting a MLM, wearable sensor and its location for a specific application. Finally, it suggests some future directions for gait analysis and its applications.

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Section: A Wearable Designmentioning

confidence: 99%

Latest Research Trends in Gait Analysis Using Wearable Sensors and Machine Learning: A Systematic Review

et al. 2020

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“…The function of the output current source is to provide a consistent current and also to amplify the stimulus signal before it is sent to the electrode pads. An output current in the range of 0–100 mA is typically used for knee extension exercise to stimulate the quadriceps muscles [ 43 ].…”

Section: System Overview Of Closed-loop Fes For Knee Extension Applicationmentioning

confidence: 99%

“…To date, only a few research works have been published on the implementation of FLC in embedded systems for the closed-loop FES application. Some of the published research works by Basith et al (2017), Basith et al (2016) and Arrofiqi et al (2015) have reported the implementation of FLC in an embedded Advanced RISC Machines (ARM) microcontroller for wearable closed-loop FES applications such as knee walking cycles and gait control movements [18,42,43]. FLC implementation in a field-programmable gate array (FPGA) has recently piqued the interest of researchers in both linear and non-linear applications to improve control performance [44,48,49].…”

Section: Introductionmentioning

confidence: 99%

“…Although fuzzy logic has been widely used by researchers in recent decades, particularly for modelling and control in closed-loop FES applications, few research works have been published on the implementation of FLC in embedded systems for real-world closed-loop FES applications. The implementation of FLC as a feedback controller in an embedded system is very important for a wearable closed-loop FES device application [ 18 , 42 , 43 ]. The possible digital controllers that can be used to develop FLC in embedded systems are microcontroller, Digital Signal Processor (DSP), application-specific integrated circuit (ASIC) and field-programmable gate array (FPGA) technologies [ 44 , 45 ].…”

Section: Introductionmentioning

confidence: 99%

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Design of an FPGA-Based Fuzzy Feedback Controller for Closed-Loop FES in Knee Joint Model

et al. 2021

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Functional electrical stimulation (FES) device has been widely used by spinal cord injury (SCI) patients in their rehab exercises to restore motor function to their paralysed muscles. The major challenge of muscle contraction induced by FES is early muscle fatigue due to the open-loop stimulation strategy. To reduce the early muscle fatigue phenomenon, a closed-loop FES system is proposed to track the angle of the limb’s movement and provide an accurate amount of charge according to the desired reference angle. Among the existing feedback controllers, fuzzy logic controller (FLC) has been found to exhibit good control performance in handling complex non-linear systems without developing any complex mathematical model. Recently, there has been considerable interest in the implementation of FLC in hardware embedded systems. Therefore, in this paper, a digital fuzzy feedback controller (FFC) embedded in a field-programmable gate array (FPGA) board was proposed. The digital FFC mainly consists of an analog-to-digital converter (ADC) Data Acquisition and FLC sub-modules. The FFC was designed to monitor and control the progress of knee extension movement by regulating the stimulus pulse width duration to meet the target angle. The knee is expected to extend to a maximum reference angle setting (70°, 40° or 30°) from its normal position of 0° once the stimulus charge is applied to the muscle by the FES device. Initially, the FLC was modelled using MATLAB Simulink. Then, the FLC was hardcoded into digital logic using hardware description language (HDL) Verilog codes. Thereafter, the performance of the digital FLC was tested with a knee extension model using the HDL co-simulation technique in MATLAB Simulink. Finally, for real-time verification, the designed digital FFC was downloaded to the Intel FPGA (DE2-115) board. The digital FFC utilized only 4% of the total FPGA (Cyclone IV E) logic elements (LEs) and required 238 µs to regulate stimulus pulse width data, including 3 µs for the FLC computation. The high processing speed of the digital FFC enables the stimulus pulse width duration to be updated every stimulation cycle. Furthermore, the implemented digital FFC has demonstrated good control performance in accurately controlling the stimulus pulse width duration to reach the desired reference angle with very small overshoot (1.4°) and steady-state error (0.4°). These promising results are very useful for a real-world closed-loop FES application.

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Gait Data Compression using Linear Prediction Modeling and data decomposition based on discrete wavelet transform

Nahdliyah

Arifin

Fatoni

et al. 2020

2020 International Conference on Computer Engineering, Network, and Intelligent Multimedia (CENIM)

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Design of wearable system for closed-loop control of gait restoration system by Functional Electrical Stimulation

Cited by 7 publications

References 3 publications

Latest Research Trends in Gait Analysis Using Wearable Sensors and Machine Learning: A Systematic Review

Latest Research Trends in Gait Analysis Using Wearable Sensors and Machine Learning: A Systematic Review

Design of an FPGA-Based Fuzzy Feedback Controller for Closed-Loop FES in Knee Joint Model

Gait Data Compression using Linear Prediction Modeling and data decomposition based on discrete wavelet transform

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