Design of active orthoses for a robotic gait rehabilitation system

Villa-Parra, Ana Cecilia; Broche, Leonardo; Delisle-Rodríguez, Denis; Sagaró, R.; Bastos, Teodiano; Frizera, Anselmo

doi:10.1007/s11465-015-0350-1

Cited by 6 publications

(7 citation statements)

References 26 publications

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“…The control of active orthoses for gait rehabilitation based on EEG and EMG has been presented in [ 43 ]. The work proposes an approach for the control of active orthoses that incorporates motion intention recognition based on the detection of ERD/ERS to predict the gait phase.…”

Section: Resultsmentioning

confidence: 99%

“…Especially, movement prediction combined with P300 detection can ensure a robust prediction of the upcoming movements with an increased prediction accuracy, since the P300 is detected with a higher accuracy compared to movement prediction. A further possibility is to combine an EEG-based BCI-system with EMG signals, which can similarly increase the reliability of the combined system [ 25 , 42 , 43 , 82 , 83 , 84 , 85 , 86 , 87 ]. However, to combine several modalities (e.g., EEG, EMG), it is important that the utilized computing system is capable to process several different data streams in parallel and real-time to be able to combine the individual predictions.…”

Section: Related Workmentioning

confidence: 99%

“…Moreover, the feasibility to predict movements using EMG might depend on the severity of a paresis, is not applicable for paralyzed patients, and is difficult for patients with spasms or tremor. Furthermore, the combination can help to enhance the reliability of control commands of the device [ 43 , 44 ]. A further advantage of such a combination is the possibility to adapt the data-dependent signal processing and machine learning methods used for the EEG-based movement detection by using the EMG-based predictions [ 45 , 46 ].…”

Section: Introductionmentioning

confidence: 99%

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A Hybrid FPGA-Based System for EEG- and EMG-Based Online Movement Prediction

Wöhrle

Tabie

Kim

et al. 2017

Sensors

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A current trend in the development of assistive devices for rehabilitation, for example exoskeletons or active orthoses, is to utilize physiological data to enhance their functionality and usability, for example by predicting the patient’s upcoming movements using electroencephalography (EEG) or electromyography (EMG). However, these modalities have different temporal properties and classification accuracies, which results in specific advantages and disadvantages. To use physiological data analysis in rehabilitation devices, the processing should be performed in real-time, guarantee close to natural movement onset support, provide high mobility, and should be performed by miniaturized systems that can be embedded into the rehabilitation device. We present a novel Field Programmable Gate Array (FPGA) -based system for real-time movement prediction using physiological data. Its parallel processing capabilities allows the combination of movement predictions based on EEG and EMG and additionally a P300 detection, which is likely evoked by instructions of the therapist. The system is evaluated in an offline and an online study with twelve healthy subjects in total. We show that it provides a high computational performance and significantly lower power consumption in comparison to a standard PC. Furthermore, despite the usage of fixed-point computations, the proposed system achieves a classification accuracy similar to systems with double precision floating-point precision.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Hybrid FPGA-Based System for EEG- and EMG-Based Online Movement Prediction

Wöhrle

Tabie

Kim

et al. 2017

Sensors

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“…ALLOR has a mechanical structure of aluminum (type 7075), which is attached to the user's joints. It was built using active orthoses design-criteria for lower-limb devices for assistance and rehabilitation reported by (Villa-Parra et al, 2015). It is mounted on the left leg of the user, and is adaptable to different anthropometric setups, which include heights of 1.5 to 1.85 m and weights from 50 to 95 kg.…”

Section: Advance Lower Limb Orthosis For Rehabilitation (Allor)mentioning

confidence: 99%

Control of a robotic knee exoskeleton for assistance and rehabilitation based on motion intention from sEMG

et al. 2018

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This work presents the development of a novel robotic knee exoskeleton controlled by motion intention based on sEMG, which uses admittance control to assist people with reduced mobility and improve their locomotion. Clinical research remark that these devices working in constant interaction with the neuromuscular and skeletal human system improves functional compensation and rehabilitation. Hence, the users become an active part of the training/rehabilitation, facilitating their involvement and improving their neural plasticity. For recognition of the lower-limb motion intention and discrimination of knee movements, sEMG from both lower-limb and trunk are used, which implies a new approach to control robotic assistive devices. Methods: A control system that includes a stage for human-motion intention recognition (HMIR), based on techniques to classify motion classes related to knee joint were developed. For translation of the user's intention to a desired state for the robotic knee exoskeleton, the system also includes a finite state machine and admittance, velocity and trajectory controllers with a function that allows stopping the movement according to the users intention. Results: The proposed HMIR showed an accuracy between 76% to 83% for lower-limb muscles, and 71% to 77% for trunk muscles to classify motor classes of lower-limb movements. Experimental results of the controller showed that the admittance controller proposed here offers knee support in 50% of the gait cycle and assists correctly the motion classes. Conclusion: The robotic knee exoskeleton introduced here is an alternative method to empower knee movements using sEMG signals from lower-limb and trunk muscles.

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“…However, it presents high sensitivity with magnetic field interferences and need frequent calibration (El-Gohary and McNames, 2012). Other common devices are the potentiometers that usually need mechanical supports precisely assembled Compensation technique for environmental and light source power variations applied in a polymer optical fiber curvature sensor for wearable devices to the joint, which are a widely applied technology to measure angles in active orthosis and exoskeletons (Moreno et al, 2008;Villa-Parra et al, 2015). However, it can result in a less compact and more intrusive system (Wang et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Compensation technique for environmental and light source power variations applied in a polymer optical fiber curvature sensor for wearable devices

Leal‐Junior

Frizera

2018

Res. Biomed. Eng.

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Introduction: Polymer optical fibers (POF) are lightweight, present high elastic strain limits, fracture toughness, flexibility in bend, and are not influenced by electromagnetic fields. These characteristics enable the application of POF as curvature sensor and can overcome the limitations of the conventional technologies, especially for wearable and soft robotics devices. Nevertheless, POF based curvature sensors can suffer from environmental and light source power deviations. This paper presents a compensation technique for the environmental and light source power deviations in a POF curvature sensor. Methods: The curvature sensor was submitted to variations of temperature, humidity and light source power to characterize the sensor response and evaluate the proposed compensation technique. In addition, tests with the simultaneous variation of the angle and light source power variation were performed. Results: Results show that temperature and humidity effects do not lead to significative errors on the sensor measurement for wearable devices application, where a hardware-based compact and portable compensation technique of the light source deviation is applied. Moreover, the sensor with the compensation technique developed is compared with a potentiometer for dynamic measurements and the root-mean-square error of about 1° is obtained, which is lower than sensors based on similar operation principle presented in the literature and some commercially available devices. Conclusions: The compensation technique proposed was able to compensate power deviations applied and resulted in a sensor with low errors with the additional advantages of compactness and low-cost, which enable its application as wearable sensors and on the instrumentation of wearable robots.

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Design of active orthoses for a robotic gait rehabilitation system

Cited by 6 publications

References 26 publications

A Hybrid FPGA-Based System for EEG- and EMG-Based Online Movement Prediction

A Hybrid FPGA-Based System for EEG- and EMG-Based Online Movement Prediction

Control of a robotic knee exoskeleton for assistance and rehabilitation based on motion intention from sEMG

Compensation technique for environmental and light source power variations applied in a polymer optical fiber curvature sensor for wearable devices

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