An Advanced Adaptive Control of Lower Limb Rehabilitation Robot

Rehabilitation robots play an important role in the rehabilitation field, and effective human-robot interaction contributes to promoting the development of the rehabilitation robots. Though many studies about the human-robot interaction have been carried out, there are still several limitations in the flexibility and stability of the control system. Therefore, we proposed an advanced adaptive control method for lower limb rehabilitation robot. The method was devised with a dual closed loop control strategy based on the surface electromyography (sEMG) and plantar pressure to improve the robustness of the adaptive control for the rehabilitation robots. First, in the outer loop control, an advanced variable impedance controller based on the sEMG and plantar pressure was designed to correct robot's reference trajectory. Then, in the inner loop control, a sliding mode iterative learning controller (SMILC) based on the variable boundary saturation function was designed to achieve the tracking of the reference trajectory. The experiment results showed that, in the designed dual closed loop control strategy, a variable impedance controller can effectively reduce trajectory tracking errors and adaptively modify the reference trajectory synchronizing with the motion intention of patients; the designed sliding mode iterative learning controller can effectively reduce chattering in sliding mode control and excellently achieve the tracking of rehabilitation robot's reference trajectory. This study can improve the performance of the human-robot interaction of the rehabilitation robot system, and expand the application to the rehabilitation field

Real-time EMG based pattern recognition control for hand prostheses : a review on existing methods, challenges and future implementation

Upper limb amputation is a condition that significantly restricts the amputees from performing their daily activities. The myoelectric prosthesis, using signals from residual stump muscles, is aimed at restoring the function of such lost limbs seamlessly. Unfortunately, the acquisition and use of such myosignals are cumbersome and complicated. Furthermore, once acquired, it usually requires heavy computational power to turn it into a user control signal. Its transition to a practical prosthesis solution is still being challenged by various factors particularly those related to the fact that each amputee has different mobility, muscle contraction forces, limb positional variations and electrode placements. Thus, a solution that can adapt or otherwise tailor itself to each individual is required for maximum utility across amputees. Modified machine learning schemes for pattern recognition have the potential to significantly reduce the factors (movement of users and contraction of the muscle) affecting the traditional electromyography (EMG)-pattern recognition methods. Although recent developments of intelligent pattern recognition techniques could discriminate multiple degrees of freedom with high-level accuracy, their efficiency level was less accessible and revealed in real-world (amputee) applications. This review paper examined the suitability of upper limb prosthesis (ULP) inventions in the healthcare sector from their technical control perspective. More focus was given to the review of real-world applications and the use of pattern recognition control on amputees. We first reviewed the overall structure of pattern recognition schemes for myo-control prosthetic systems and then discussed their real-time use on amputee upper limbs. Finally, we concluded the paper with a discussion of the existing challenges and future research recommendations

Towards electrodeless EMG linear envelope signal recording for myo-activated prostheses control

After amputation, the residual muscles of the limb may function in a normal way, enabling the electromyogram (EMG) signals recorded from them to be used to drive a replacement limb. These replacement limbs are called myoelectric prosthesis. The prostheses that use EMG have always been the first choice for both clinicians and engineers. Unfortunately, due to the many drawbacks of EMG (e.g. skin preparation, electromagnetic interferences, high sample rate, etc.); researchers have aspired to find suitable alternatives. One proposes the dry-contact, low-cost sensor based on a force-sensitive resistor (FSR) as a valid alternative which instead of detecting electrical events, detects mechanical events of muscle. FSR sensor is placed on the skin through a hard, circular base to sense the muscle contraction and to acquire the signal. Similarly, to reduce the output drift (resistance) caused by FSR edges (creep) and to maintain the FSR sensitivity over a wide input force range, signal conditioning (Voltage output proportional to force) is implemented. This FSR signal acquired using FSR sensor can be used directly to replace the EMG linear envelope (an important control signal in prosthetics applications). To find the best FSR position(s) to replace a single EMG lead, the simultaneous recording of EMG and FSR output is performed. Three FSRs are placed directly over the EMG electrodes, in the middle of the targeted muscle and then the individual (FSR1, FSR2 and FSR3) and combination of FSR (e.g. FSR1+FSR2, FSR2-FSR3) is evaluated. The experiment is performed on a small sample of five volunteer subjects. The result shows a high correlation (up to 0.94) between FSR output and EMG linear envelope. Consequently, the usage of the best FSR sensor position shows the ability of electrode less FSR-LE to proportionally control the prosthesis (3-D claw). Furthermore, FSR can be used to develop a universal programmable muscle signal sensor that can be suitable to control the myo-activated prosthesis

Modelo adaptativo baseado em sensor virtual para eletromiografia de superfície com sistema de classificação tolerante a falhas

Apenas alguns sistemas de controle protético na literatura científica são baseados em algoritmos de reconhecimento de padrões, os quais são adaptados às mudanças que ocorrem no sinal mioelétrico ao longo do tempo, e, frequentemente, tais sistemas não são naturais e intuitivos. As mudanças no sinal mioelétrico são alguns dos vários desafios para as próteses mioelétricas serem amplamente utilizadas. O conceito do sensor virtual, que tem como objetivo fundamental estimar medidas indisponíveis por trás de outras medidas disponíveis, vem sendo utilizado em outras áreas de pesquisa. O sensor virtual aplicado à eletromiografia de superfície (sEMG) pode ajudar a minimizar esses problemas, tipicamente relacionados à degradação do sinal mioelétrico, os quais geralmente provocam uma diminuição na taxa de acerto da classificação dos movimentos por sistemas de inteligência computacional. A principal contribuição deste trabalho é o desenvolvimento de um sistema de classificação de movimentos tolerante a falhas, o qual utiliza o conceito de sensores virtuais para reduzir o impacto de degradação de sinais de sEMG. A segunda contribuição é um modelo do sinal de sEMG dinâmico e adaptativo para o sensor virtual, o qual produz um modelo de saída de sinal independente da aquisição física do sinal de interesse. A modelagem do sinal de sEMG é projetada de forma a combinar os conceitos de multicanais e sua correlação cruzada, além de utilizar um sistema de ajuste dos coeficientes de correlação, a fim de substituir os canais de sinais degradados Dois modelos são avaliados e detalhados: Time-Varying Autoregressive Moving Average (TVARMA) e o Time- Varying Kalman Filter (TVK). A terceira contribuição é a combinação de uma análise e detecção da contaminação do sinal realizada por um sensor de detecção tolerante a falhas (Sensor Fault-Tolerant Detector – SFTD). Os resultados da classificação dos movimentos foram apresentados comparando as técnicas usuais de classificação com o método da substituição do sinal degradado e um processo de retreinamento do classificador simplificado. Os resultados foram avaliados para cinco tipos de ruído em 16 estudos de caso da degradação dos canais de sEMG. O sistema adaptativo proposto sem o uso de técnicas de retreinamento do classificador recuperou a taxa de acerto média de classificação em até 46% para os ruídos de deslocamento de eletrodos e de saturação. Devido às limitações do sistema proposto quanto aos ruídos de artefato de movimento, de interferência de linha de energia e ECG, o sistema apresentado pode ser utilizado como uma técnica complementar com outras técnicas de classificação para aumentar o impacto clínico da prótese mioelétrica. Entretanto, o sistema ainda requer uma análise quanto a diferentes níveis de SNR antes de uma otimização do algoritmo. Além disso, o modelo TVARMA do sensor virtual obteve uma taxa de acerto média superior em comparação ao modelo TVK na maioria das situações avaliadas neste trabalho.Nowadays, only a few prosthetic control systems in the scientific literature are founded on pattern recognition algorithms adapted to changes that occur in the myoelectric signal over time and, frequently, such systems are not natural and intuitive. These are some of the several challenges for myoelectric prostheses for everyday use. The concept of the virtual sensor, which has as its fundamental objective to estimate unavailable measures based on other available measures, is already being used in other fields of research. The virtual sensor technique applied to surface electromyography (sEMG) can help to mitigate these problems, typically related to the degradation of the myoelectric signal that usually leads to a decrease in the classification accuracy of the movements characterized by intelligent computational systems. Therefore, the main contribution of this work is the Fault-Tolerant Classification System, that was developed using the concept of virtual sensors to reduce the degradation impact of sEMG signals. The second contribution is a dynamic and adaptive virtual sensor model, which produces a signal output model independent of the physical acquisition of the interest signal. The sEMG signal modeling was designed to combine multichannel concepts and their cross-correlation, in addition to the use of the correlation coefficient adjustment system to replace degraded signal channels. Two models were evaluated and detailed: Time-Varying Autoregressive Moving Average (TVARMA) and Time-Varying Kalman Filter (TVK) The third contribution is the analysis and detection of signal contamination by a Sensor Fault-Tolerant Detector (SFTD). The classification results of the movements were compared to the traditional classification techniques, the classification with the degraded signal replacement method and a simplified retraining process of the classifier. The results were evaluated for five noise types in 16 case studies of the sEMG channels degradation. The adaptive system proposed, without the classifier re-training techniques, was able to recover 46% of the mean classification accuracy for the electrodes displacement and saturation noise. Moreover, the proposed system can be used as a complementary technique with other classification techniques to increase the clinical impact of the myoelectric prosthesis since there are still limitations in the proposed method regarding the movement artifact noise, power line, and ECG interference. However, the system still requires an analysis of different SNR levels before the algorithm optimization. Also, the TVARMA model of the virtual sensor obtained a higher classification accuracy compared to the TVK model in most of the evaluated situations