Towards human-knee orthosis interaction based on adaptive impedance control through stiffness adjustment

Rehabilitation interventions involving powered, wearable lower limb orthoses that can provide high-challenging locomotor tasks for repetitive training sessions, mainly when assist-as-needed strategies, such as adaptive impedance control, are designed. In this study, the adaptive behavior was ensured by software control of the robotic stiffness involved in the human-knee orthosis interaction in function of the gait cycle and speed. To estimate the stiffness, we analyzed the interaction torque-angle characteristics with experimental data. The speed-stiffness dependency was more evident when high stiffness values are demanded by the user's effort. Experimental evidence from five healthy subjects highlight that the adaptive control strategy provides a more comfortable, natural motion, and kinematic freedom as compared to the trajectory tracking control, allowing the user to contribute to the gait training. Future insights cover the implementation of gravitational compensation and real-time estimation and control of all inner dynamic properties of the impedance control law.This work has been supported by the FCT - Fundacao para a Ciencia e Tecnologia - with the reference scholarship SFRH/BD/108309/2015, with the reference project UID/EEA/04436/2013, and by FEDER funds through the COMPETE 2020 - Programa Operacional Competitividade e Internacionalizacao (POCI) - with the reference project POCI-01-0145-FEDER-006941, and partially supported with grant RYC-2014-16613 by Spanish Ministry of Economy and Competitiveness

Assist-as-needed impedance control strategy for a wearable ankle robotic orthosis

The use of robots in rehabilitation attempts an effective, compliant, and time-efficient gait recovery while adapting the assistance to the user's needs. Assist-as-needed strategies (AAN), such as adaptive impedance control, have been reported as prominent strategies to enable this recovery effects. This study proposes an interaction-based assist-as-needed impedance control strategy for an ankle robotic orthosis that adapts the robotic assistance by changing the Human-Robot interaction stiffness. The adaptability of the interaction stiffness allows the real-time passage from passive assistance to an active one, approaching AAN gait training. The interaction stiffness was successfully estimated by linear regression of the Human-Robot interaction torque vs angle trajectory curve. From the validation with seven able-bodied subjects, we verified the suitability of this adaptive impedance control for a more compliant, natural, and comfortable motion than the trajectory tracking control. Moreover, the proposed strategy considers the users' motion intention and encourages them to interact closely with the robotic device while guiding their ankle trajectory according to desired trajectories. These achievements contribute to AAN gait training.This work has been supported by the FEDER Funds through the Programa Operacional Regional do Norte and national funds from Fundação para a Ciência e Tecnologia with the project SmartOs under Grant NORTE-01-0145-FEDER-030386, and through the COMPETE 2020—Programa Operacional Competitividade e Internacionalização (POCI)—with the Reference Project under Grant POCI-01-0145-FEDER-006941

Kinematic and dynamic modeling and validation of an assistive robotic device for knee rehabilitation

La articulación de la rodilla está frecuentemente expuesta a lesiones en personas de todas las edades. En todos los casos, la terapia física se prescribe para recuperar la fuerza y la movilidad de un paciente. Los dispositivos de asistencia robótica están ganando la atención de la comunidad y apuntan a mejorar la calidad de vida de los pacientes. En este artículo, se propone el diseño mecánico de un dispositivo de rehabilitación de rodilla de enlace de 5 barras basado en la definición de los parámetros físicos de la población colombiana y/o latinoamericana, de acuerdo a los datos de antropometría. Se obtiene el modelo dinámico completo del sistema de rehabilitación propuesto y se realizan las comparaciones respectivas de movimiento con el prototipo real para desarrollar y evaluar estrategias de control apropiadas en trabajos futuros. Para este propósito, se presenta la formulación cinemática del dispositivo y luego se deriva la dinámica utilizando dos enfoques para validar el modelo; se obtiene la ecuación de movimiento utilizando la aproximación de Lagrange y un método algebraico que simplifica el modelado. Ambas aproximaciones producen un modelo único, que se valida en simulación y en ensayos experimentales, mostrando la funcionalidad del sistema y la validez de los modelos cuando se realizan rutinas de rehabilitación.The knee joint is frequently exposed to injuries in people of all ages. In all cases, physical therapy is prescribed to recover the strength and mobility of a patient. The robotic assistance devices are gaining the community attention and aim to improve the quality of life of patients. In this article, we propose the mechanical design of a 5-bar-linkage knee rehabilitation device based on the definition of the physical parameters of Colombian and/or Latin-American population, according to anthropomorphic data. We obtain the complete dynamic model of the proposed rehabilitation system and perform the respective comparisons of movement with the real prototype in order to develop and evaluate appropriate control strategies in future work. For this purpose, we present the kinematic formulation of the device and then we derive the dynamics using two approaches to validate the model; we obtain the motion equation using the Lagrange approach and an algebraic method that simplifies modeling. Both approaches yield a unique model, which is validated either in simulation and by experimental trials, showing the functionality of the system and the validity of the models when performing rehabilitation routines.Pregrad

Towards a human-in-the-loop control for a smart orthotic system

Dissertação de mestrado integrado em Engenharia Biomédica (área de especialização em Eletrónica Médica)Stroke is the main cause of paralysis. This pathology has provoked a considerable increase of persons with motor impairments. With a therapy focused on each clinical case, the total or partial recovery can be achieved. Powered orthoses have been developed to promote an effective recover, based on repetitive gait training and user’s active participation. Many control approaches have been developed to control these devices, but none of them promotes an user-oriented strategy focused to the user’s needs. In an attempt of solving this issue, a new approach named Human-in-the-loop is emerging. This strategy allows the adaptation of some assistive parameters based on the user’s energetic cost, promoting a therapy tailored to each end-user needs. However, to estimate the energy expenditure, the use of non-ergonomic sensors, not suitable for clinical context, is required. Thus, it is necessary to find new ways of estimating energy expenditure using wearable and comfortable sensors. In this dissertation, the first steps to introduce the Human-in-the-loop strategy into a powered orthosis are presented. For this purpose, two strategies were developed: a strategy that allows the angular trajectory adaptation in real-time and other that promotes a stiffness adaptation all over the gait cycle. Both strategies were validated with healthy subjects. In the first strategy, the orthosis was able to modify its assistance in a fraction of microseconds, and the end-users were able to follow her with a median error below 10%. Regarding the second strategy, the results show that the orthosis allowed an effective change in the systems’ interaction stiffness, promoting an active participation of each user during its assistance. The energetic impact of using the robotic assistive device is also presented. As it promotes an energy expenditure augmentation in more than 30% in comparison to walk without the device, the necessity of implementing the Human-in-the-loop strategy was highlighted. In an attempt of finding an ergonomic technique to estimate the energetic cost, the use of machine learning algorithms was tested. The results, obtained with a MLP and a LSTM, prove that it is possible to estimate the energy expenditure with a mean error close to 11%. Future work consists in the implementation of the model in real-time and the collection of more data with the aforementioned control approaches, in a way of constructing a more robust model.O AVC é uma das maiores causas de paralisia. Esta patologia, cada vez mais com maior incidência nos jovens, tem provocado um aumento considerável de pessoas com problemas de mobilidade. Com uma terapia focada a cada caso clínico, a recuperação total ou parcial pode ser conseguida. As ortóteses ativas têm vindo a ser desenvolvidas com o propósito de promover uma recuperação eficaz, baseada em treinos repetitivos e numa participação ativa dos utilizadores. Várias abordagens de controlo têm vindo a ser desenvolvidas para controlar estes dispositivos, mas nenhuma delas promove uma estratégia orientada às necessidades do utilizador. Na tentativa de solucionar este problema, uma nova abordagem, designada por Human-in-the-loop está a emergir. Baseada no custo energético, esta estratégia permite adaptar parâmetros da assistência, promovendo uma terapia focada e direcionada a cada utilizador. No entanto, para estimar o custo energético, recorre-se ao uso de sensores que não são adequados para contexto clínico. Assim, torna-se necessário estudar novas formas de estimar o custo energético. Nesta dissertação são apresentados os primeiros passos para introduzir o controlo Human-in-the-loop numa ortótese ativa. Para isso, duas estratégias foram apresentadas: uma estratégia que permite adaptar a trajetória angular da ortótese, em tempo real, e outra que promove a adaptação da complacência do sistema ao longo do ciclo da marcha. Ambas foram validadas com sujeitos saudáveis. Relativamente à primeira abordagem, a ortótese foi capaz de modificar a sua assistência em microssegundos, e os utilizadores foram capazes de a seguir com um erro mediano inferior a 10%. No que diz respeito à segunda abordagem, os resultados mostram que a ortótese promoveu uma alteração eficaz da complacência de interação, promovendo uma participação ativa do utilizador durante a sua assistência. O impacto energético do uso do sistema robótico é, também, apresentado. Promovendo um aumento do custo energético em mais de 30%, a necessidade da estratégia Human-in-the-loop foi realçada. Na tentativa de encontrar uma técnica para estimar o custo energético, recorreu-se ao uso de machine learning. Os resultados, obtidos com uma MLP e uma LSTM, provam que é possível estimar o custo energético com um erro médio próximo dos 11%. Trabalho futuro passa pela implementação do modelo em tempo real e a recolha de mais dados com as abordagens de controlo apresentadas, de forma a construir um modelo mais robusto