Object Manipulation in Virtual Reality Under Increasing Levels of Translational Gain

Room-scale Virtual Reality (VR) has become an affordable consumer reality, with applications ranging from entertainment to productivity. However, the limited physical space available for room-scale VR in the typical home or office environment poses a significant problem. To solve this, physical spaces can be extended by amplifying the mapping of physical to virtual movement (translational gain). Although amplified movement has been used since the earliest days of VR, little is known about how it influences reach-based interactions with virtual objects, now a standard feature of consumer VR. Consequently, this paper explores the picking and placing of virtual objects in VR for the first time, with translational gains of between 1x (a one-to-one mapping of a 3.5m*3.5m virtual space to the same sized physical space) and 3x (10.5m*10.5m virtual mapped to 3.5m*3.5m physical). Results show that reaching accuracy is maintained for up to 2x gain, however going beyond this diminishes accuracy and increases simulator sickness and perceived workload. We suggest gain levels of 1.5x to 1.75x can be utilized without compromising the usability of a VR task, significantly expanding the bounds of interactive room-scale VR

Continuous Myoelectric Prediction of Future Ankle Angle and Moment Across Ambulation Conditions and Their Transitions

A hallmark of human locomotion is that it continuously adapts to changes in the environment and predictively adjusts to changes in the terrain, both of which are major challenges to lower limb amputees due to the limitations in prostheses and control algorithms. Here, the ability of a single-network nonlinear autoregressive model to continuously predict future ankle kinematics and kinetics simultaneously across ambulation conditions using lower limb surface electromyography (EMG) signals was examined. Ankle plantarflexor and dorsiflexor EMG from ten healthy young adults were mapped to normal ranges of ankle angle and ankle moment during level overground walking, stair ascent, and stair descent, including transitions between terrains (i.e., transitions to/from staircase). Prediction performance was characterized as a function of the time between current EMG/angle/moment inputs and future angle/moment model predictions (prediction interval), the number of past EMG/angle/moment input values over time (sampling window), and the number of units in the network hidden layer that minimized error between experimentally measured values (targets) and model predictions of ankle angle and moment. Ankle angle and moment predictions were robust across ambulation conditions with root mean squared errors less than 1° and 0.04 Nm/kg, respectively, and cross-correlations (R2) greater than 0.99 for prediction intervals of 58 ms. Model predictions at critical points of trip-related fall risk fell within the variability of the ankle angle and moment targets (Benjamini-Hochberg adjusted p \u3e 0.065). EMG contribution to ankle angle and moment predictions occurred consistently across ambulation conditions and model outputs. EMG signals had the greatest impact on noncyclic regions of gait such as double limb support, transitions between terrains, and around plantarflexion and moment peaks. The use of natural muscle activation patterns to continuously predict variations in normal gait and the model’s predictive capabilities to counteract electromechanical inherent delays suggest that this approach could provide robust and intuitive user-driven real-time control of a wide variety of lower limb robotic devices, including active powered ankle-foot prostheses

System Identification of Bipedal Locomotion in Robots and Humans

The ability to perform a healthy walking gait can be altered in numerous cases due to gait disorder related pathologies. The latter could lead to partial or complete mobility loss, which affects the patients’ quality of life. Wearable exoskeletons and active prosthetics have been considered as a key component to remedy this mobility loss. The control of such devices knows numerous challenges that are yet to be addressed. As opposed to fixed trajectories control, real-time adaptive reference generation control is likely to provide the wearer with more intent control over the powered device. We propose a novel gait pattern generator for the control of such devices, taking advantage of the inter-joint coordination in the human gait. Our proposed method puts the user in the control loop as it maps the motion of healthy limbs to that of the affected one. To design such control strategy, it is critical to understand the dynamics behind bipedal walking. We begin by studying the simple compass gait walker. We examine the well-known Virtual Constraints method of controlling bipedal robots in the image of the compass gait. In addition, we provide both the mechanical and control design of an affordable research platform for bipedal dynamic walking. We then extend the concept of virtual constraints to human locomotion, where we investigate the accuracy of predicting lower limb joints angular position and velocity from the motion of the other limbs. Data from nine healthy subjects performing specific locomotion tasks were collected and are made available online. A successful prediction of the hip, knee, and ankle joints was achieved in different scenarios. It was also found that the motion of the cane alone has sufficient information to help predict good trajectories for the lower limb in stairs ascent. Better estimates were obtained using additional information from arm joints. We also explored the prediction of knee and ankle trajectories from the motion of the hip joints

Description of motor control using inverse models

Humans can perform complicated movements like writing or running without giving them much thought. The scientific understanding of principles guiding the generation of these movements is incomplete. How the nervous system ensures stability or compensates for injury and constraints – are among the unanswered questions today. Furthermore, only through movement can a human impose their will and interact with the world around them. Damage to a part of the motor control system can lower a person’s quality of life. Understanding how the central nervous system (CNS) forms control signals and executes them helps with the construction of devices and rehabilitation techniques. This allows the user, at least in part, to bypass the damaged area or replace its function, thereby improving their quality of life. CNS forms motor commands, for example a locomotor velocity or another movement task. These commands are thought to be processed through an internal model of the body to produce patterns of motor unit activity. An example of one such network in the spinal cord is a central pattern generator (CPG) that controls the rhythmic activation of synergistic muscle groups for overground locomotion. The descending drive from the brainstem and sensory feedback pathways initiate and modify the activity of the CPG. The interactions between its inputs and internal dynamics are still under debate in experimental and modelling studies. Even more complex neuromechanical mechanisms are responsible for some non-periodic voluntary movements. Most of the complexity stems from internalization of the body musculoskeletal (MS) system, which is comprised of hundreds of joints and muscles wrapping around each other in a sophisticated manner. Understanding their control signals requires a deep understanding of their dynamics and principles, both of which remain open problems. This dissertation is organized into three research chapters with a bottom-up investigation of motor control, plus an introduction and a discussion chapter. Each of the three research chapters are organized as stand-alone articles either published or in preparation for submission to peer-reviewed journals. Chapter two introduces a description of the MS kinematic variables of a human hand. In an effort to simulate human hand motor control, an algorithm was defined that approximated the moment arms and lengths of 33 musculotendon actuators spanning 18 degrees of freedom. The resulting model could be evaluated within 10 microseconds and required less than 100 KB of memory. The structure of the approximating functions embedded anatomical and functional features of the modelled muscles, providing a meaningful description of the system. The third chapter used the developments in musculotendon modelling to obtain muscle activity profiles controlling hand movements and postures. The agonist-antagonist coactivation mechanism was responsible for producing joint stability for most degrees of freedom, similar to experimental observations. Computed muscle excitations were used in an offline control of a myoelectric prosthesis for a single subject. To investigate the higher-order generation of control signals, the fourth chapter describes an analytical model of CPG. Its parameter space was investigated to produce forward locomotion when controlled with a desired speed. The model parameters were varied to produce asymmetric locomotion, and several control strategies were identified. Throughout the dissertation the balance between analytical, simulation, and phenomenological modelling for the description of simple and complex behavior is a recurrent theme of discussion