Most human actions are composed of two fundamental movement types, discrete and rhythmic movements. These movement types, or primitives, are analogous to the two elemental behaviors of nonlinear dynamical systems, namely, fixed-point and limit cycle behavior, respectively. Furthermore, there is now a growing body of research demonstrating how various human actions and behaviors can be effectively modeled and understood using a small set of low-dimensional, fixed-point and limit cycle dynamical systems (differential equations). Here, we provide an overview of these dynamical motorprimitives and detail recent research demonstrating how these dynamical primitives can be used to model the task dynamics of complex multiagent behavior. More specifically, we review how a task-dynamic model of multiagent shepherding behavior, composed of rudimentary fixed-point and limit cycle dynamical primitives, can not only effectively model the behavior of cooperating human co-actors, but also reveals how the discovery and intentional use of optimal behavioral coordination during task learning is marked by a spontaneous, self-organized transition between fixed-point and limit cycle dynamics (i.e., via a Hopf bifurcation).

The ability to move one's body from sitting to standing is a crucial ability for independent living. Especially for seniors with decreasing muscular strength, sit-to-stand (STS) transitions are exceptionally risky and often call for assistance. In general, an STS transition is a complex full-body activity that requires the synergistic coordination of the upper and lower limbs and trunk. An exoskeleton can support this multiple degrees-of-freedom problem by controlling the trajectory of the center of mass of the resulting human-robot system. However, while human movement is highly variable, exoskeletons usually only support one of multiple possible solutions. In this paper, we first present an analysis of factors that affect human center of mass trajectory and show that different human movement velocity profiles during STS transitions require different control strategies of the center of mass. Therefore, we propose a model based on horizontal and vertical momentums that enables efficient planning of the center of mass trajectory for any STS transition velocity. Finally, we validate this model by presenting an inverse kinematics solution for the CoM to joint angle problem using a deep Long Short-Term Memory (LSTM) network.

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Population of the world above the age of 65 years is increasing rapidly. Aging causes weakening of human joints which increases constraints on mobility of the body. Sit-to-Stand (STS), an important part of Activities of Daily Living (ADL) is one of the motions that is affected because of weakened joints. With the lack of personal care there is going to be a need for devices which can assist the aging population in STS. We propose the use of a lower-limb exoskeleton as an assistive device. One of the main challenges in this area is to generate a human like reference trajectory for exoskeleton to follow. This paper proposes the use of Genetic Algorithm (GA), to generate reference trajectories for the joint angles for lower limb exoskeleton for STS transition. The fitness function for the GA presented here is constructed based on the fact that for a successful STS center of mass (COM) needs to stay in the area of support. After the trajectory generation a simple controller is proposed to control a 3 degrees of freedom exoskeleton. The dynamics of the system being controlled are modelled as an inverse 3 degrees of freedom pendulum and the equations are derived using the Euler-Lagrange equation. The highly non-linear dynamics are linearized using an input-output feedback linearization technique. A PD controller is presented for this linearized dynamic system and the validation of the controller is done using simulations. Simulation results show that GA successfully generates a human like trajectory which eliminates the need to use motion tracking system for measuring human trajectories.

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