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Background: Balance control is important for mobility, yet exoskeleton research has mainly focused on improving metabolic energy efficiency. Here we present a biomimetic exoskeleton controller that supports walking balance and reduces muscle activity. Methods: Humans restore balance after a perturbation by adjusting activity of the muscles actuating the ankle in proportion to deviations from steady-state center of mass kinematics. We designed a controller that mimics the neural control of steady-state walking and the balance recovery responses to perturbations. This controller uses both feedback from ankle kinematics in accordance with an existing model and feedback from the center of mass velocity. Control parameters were estimated by fitting the experimental relation between kinematics and ankle moments observed in humans that were walking while being perturbed by push and pull perturbations. This identified model was implemented on a bilateral ankle exoskeleton. Results: Across twelve subjects, exoskeleton support reduced calf muscle activity in steady-state walking by 19% with respect to a minimal impedance controller (p < 0.001). Proportional feedback of the center of mass velocity improved balance support after perturbation. Muscle activity is reduced in response to push and pull perturbations by 10% (p = 0.006) and 16% (p < 0.001) and center of mass deviations by 9% (p = 0.026) and 18% (p = 0.002) with respect to the same controller without center of mass feedback. Conclusion: Our control approach implemented on bilateral ankle exoskeletons can thus effectively support steady-state walking and balance control and therefore has the potential to improve mobility in balance-impaired individuals.

Current control of prosthetic hands is ineffective when grasping deformable, irregular, or heavy objects. In humans, grasping is achieved under spinal reflexive control of the musculotendon skeletal structure, which produces a hand stiffness commensurate with the task. We hypothesize that mimicking reflex on a prosthetic hand may improve grasping performance and safety when interacting with human. Here, we present a design of compliant controller for prosthetic hand with a neuromorphic model of human reflex. The model includes 6 motoneuron pools containing 768 spiking neurons, 1 muscle spindle with 128 spiking afferents, and 1 modified Hill-type muscle. Models are implemented using neuromorphic hardware with 1 kHz real-time computing. Experimental tests showed that the prosthetic hand could sustain a 40 N load compared to 95 N for an adult. Stiffness range was adjustable from 60 to 640 N/m, about 46.6% of that of human hand. The grasping velocity could be ramped up to 14.4 cm/s, or 24% of the human peak velocity. The complaint control could switch between free movement and contact force when pressing a deformable beam. The amputee can achieve a 47% information throughput of healthy humans. Overall, the reflex-enabled prosthetic hand demonstrated the attributes of human compliant grasping with the neuromorphic model of spinal neuromuscular reflex. Electronic supplementary material The online version of this article (10.1007/s10439-020-02596-9) contains supplementary material, which is available to authorized users.

Buçalışmada, insan kol ve el hareketlerinin taklit edilmesiyle insan-robotetkileşimini sağlayan biyomimetik bir yaklaşım sunulmuştur. İnsan kolhareketleriyle robotun aynı doğrultuda hareket etmesi sağlanmış ve elhareketleri ile de robot tutucusunun kontrolü sağlanmıştır. Robot hareketiiçin; ilk olarak insan elinin, bel hizasında orijin noktası olarak belirlenennoktaya olan konumunu verecek kinematik model oluşturulmuştur. Modellemede,insan kolu, ön kol, pazı ve omuz olmak üzere üç ayrı uzuv olarak incelenmiştir.Omuza, pazıya ve ön kola yerleştirilen algılayıcılar ile dönüş açısı bilgilerielde edilmiş ve uzuv uzunlukları ile birlikte matematiksel modeldekullanılmıştır. Bu hesaplamalarda rotasyon kinematiği ve hareket kinematiğimatrisleri kullanılmıştır. Tutucu kontrolü için ise bünyesinde EMG sensörleribulunduran MYO kol bandı kullanılmıştır. Bu kol bandı üzerindeki EMG sensörleriile kol kaslarından parmak hareketleri algılatılmıştır ve bu hareketlerdoğrultusunda pnömatik tutucu kontrol edilmiştir. Uygulamalarda 6-eksen robot kolukullanılmıştır. Hesaplanan konum verileri ve tutucu bilgisi ethernet üzerindenTCP/IP protokolü ile robot denetleyicisine aktarılmaktadır. Robotun hesaplanankonuma gitmesini ve tutucu kontrolünü sağlayan kod oluşturularak robotaaktarılmıştır. Yapılan testlerde,endüstriyel robotun insan kol ve el hareketleri ile başarılı biçimde kontroledildiği gözlemlenmiştir., Inthis work, a biomimetic approach to provide human-robot interaction bymimicking the motion of human arm and fingers is presented. The movement of anindustrial robot is performed by human arm movement in same direction and thecontrol of gripper is also performed by hand movements. For the movement ofrobot, as a first step, a kinematic model is obtained to give the position ofthe human hand to the point determined as the origin point in the waist line.In the modelling, the human arm is considered as three limp that are forearm,biceps and shoulder. The rotational angles are obtained from sensors placed inthe shoulder, biceps, and forearm, are used in the mathematical model with limblengths. Rotation kinematics and kinematics matrices are used in thesecalculations. For the gripper control, a MYO armband with EMG sensors is used.With this EMG sensor on the armband, finger movements are detected from the armmuscles and the pneumatic gripper was controlled in the direction of thesemovements. A 6-axis robot arm is used in the applications. The calculatedposition data and the gripper information are transferred to the robotcontroller via the TCP/IP protocol over Ethernet. A code that provides reachingof robot to calculated position and control the gripper is created andtransferred to robot. In the tests, it has been observed that the industrialrobot has been successfully controlled by human arm and hand movements