A hybrid haptic stimulation prosthetic wearable device to recover the missing sensation of the upper limb amputees

A hybrid haptic feedback stimulation system that is capable in sensing the contact pressure, the surface texture, and the temperature, simultaneously, was designed for a prosthetic hand to provide a tactile sensation to amputation patients. In addition, the haptic system was developed to enable the prosthetic’s users to implement withdrawal reflexes due to the thermal noxious stimulus in a quick manner. The re-sensation is achieved by non-invasively stimulating the skin of the patients’ residual limbs, based on the type and the level of tactile signals provided by the sensory system of the prostheses. Accordingly, three stages of design and development were performed to satisfy the research methodology. A vibrotactile prosthetic device, which is designed for the detection of contact pressure and surface texture in upper extremity, represents. While, the design of a novel wearable hybrid pressure-vibration haptic feedback stimulation device for conveying the tactile information regarding the contact pressure between the prosthetic hand and the grasped objects represents the second methodology stage. Lastly, the third stage was achieved by designing a novel hybrid pressure-vibration-temperature feedback stimulation system to provide a huge information regarding the prostheses environment to the users without brain confusing or requiring long pre-training. The main contribution of this work is the development and evaluation of the first step of a novel approach for a lightweight, 7 Degrees-Of-Freedom (DOF) tactile prosthetic arm to perform an effective as well as fast object manipulation and grasping. Furthermore, this study investigates the ability to convey the tactile information about the contact pressure, surface texture, and object temperature to the amputees with high identification accuracy by mean of using the designed hybrid pressure-vibration-temperature feedback wearable device. An evaluation of sensation and response has been conducted on forty healthy volunteers to evaluate the ability of the haptic system to stimulate the human nervous system. The results in term of Stimulus Identification Rate (SIR) show that all the volunteers were correctly able to discriminate the sensation of touch, start of touch, end of touch, and grasping objects. While 94%, 96%, 97%, and 95.24% of the entire stimuli were successfully identified by the volunteers during the experiments of slippage, pressure level, surface texture, and temperature, respectively. The position tracking controller system was designed to synchronize the movements of the volunteers’ elbow joints and the prosthetic’s elbow joint to record the withdrawal reflexes. The results verified the ability of the haptic system to excite the human brain at the abnormal noxious stimulus and enable the volunteers to perform a quick withdrawal reflex within 0.32 sec. The test results and the volunteers' response established evidence that amputees are able to recover their sense of the contact pressure, the surface texture, and the object temperature as well as to perform thermal withdrawal reflexes using the solution developed in this work

Métodos de classificação confiável e resiliente de movimentos de membros superiores baseado em extreme learning machines e sinais de eletromiografia de superfície

Apesar de avanços recentes, a classificação confiável de sinais de eletromiografia de superfície (sEMG) permanece uma tarefa árdua sob a perspectiva de Aprendizagem de Máquina. Sinais de sEMG possuem uma sobreposição de classes inerente à sua natureza, o que impede a separação perfeita das amostras e produz ruídos de classificação. Alternativas ao problema geralmente baseiam-se na filtragem do sEMG ou métodos de pós-processamento como o Major-Voting, soluções estas que necessariamente geram atrasos na classificação do sinal e frequentemente não geram melhoras substanciais. A abordagem deste trabalho baseia-se no desenvolvimento de métodos confiáveis e resilientes sob a perspectiva de classificação que gerem saídas mais estáveis e consistentes para o classificador baseado em Extreme Learning Machines (ELM) utilizado. Para tanto, métodos envolvendo o pré-processamento e pós-processamento, a suavização do arg max do classificador, thresholds adaptativos e um classificador binário auxiliar foram utilizados. Os sinais classificados derivam de 12 canais de sEMG envolvendo três bases de dados diferentes onde 99 ensaios compostos pela execução de 17 movimentos distintos do segmento mão-braço foram realizados. Nos melhores resultados, os métodos utilizados atingiram taxas de acerto médio global de 66,99 ± 23,6% para a base de voluntários amputados, 87,10 ± 5,89% para a base de voluntários não-amputados e taxas superiores a 99% para todas as variações de diferentes ensaios que compõe a base de dados adquirida em laboratório. Já para a taxa de acerto média ponderada por classes, nos melhores resultados foram de 53,36 ± 18,2% para a base de voluntários amputados, 77,94 ± 6,22% para a base de voluntários não-amputados e taxas superiores a 91% para os ensaios da base de dados adquirida em laboratório. Ambas as métricas de taxa de acerto consideradas superam ou equivalem-se a alternativas descritas na literatura, utilizando abordagens que não demandam grandes mudanças estruturais no classificador.Despite recent advances, reliable classification of surface electromyography (sEMG) signals remains an arduous task from the perspective of Machine Learning. sEMG signals have inherent class overlaps that prevent optimal labeling due to classification noises. Alternatives to classification ripples usually rely on stochastic sEMG filtering or post-processing methods, like Major-Voting, both solutions that insert constraints and additional delays in signal classification and often do not generate substantial improvements. The approach of this paper focuses on the development of reliable and resilient methods used in combination with an Extreme Learning Machines (ELM) classifier to generate more stable and consistent outputs. Methods of pre-processing and post-processing, a smoothed arg max version of the ELM, adaptive thresholds, and an auxiliary binary classifier were used to process signals derived from 12 EMG channels from three different databases. In total, 99 trials were performed, each one containing 17 different upper-limb movements. The proposed methods reached an average overall accuracy rate of 66.99 ± 23.6% for the amputee individuals’ database, 87.10 ± 5.89% for the non-amputee individuals’ database, and rates over 99% for all variations of our own lab-generated database. The average weighted accuracy rates were 53.36 ± 18.2% for the amputee individuals’ database, 77.94 ± 6.22% for the base of the non-amputee individuals’ database, and higher than 91% for the best-case scenario of our own lab-generated database. In both metrics considered, the results outperform, or match alternatives described in the literature using approaches that do not require significant changes in the classifier's architecture