Immersive virtual reality (iVR) head-mounted display (HMD) systems paired with serious exercise games can positively augment physical rehabilitation process from both engagement and analytics perspectives. This article presents a serious game for iVR HMD-based long-term upper-extremity exercise. We demonstrate the capabilities of our game through a case study with five users recovering from upper-extremity injuries. We examine how our program maintains engagement and motivation over eight weeks, where users completed biweekly prescribed movements framed as protecting a virtual butterfly. We assess user experiences through a mixture of biomarkers from brainwave, heart rate, and galvanic skin response recorded at runtime, as well as motion capture and behavioral game data. Our results suggest that the iVR game was an effective medium in inducing high compliance, physical performance, and biometric changes even with increasing difficulty beyond the novelty effect period. We conclude with considerations of future work for iVR physical therapy games that adapt to biometric response.

Inactivity and a lack of engagement with exercise is a pressing health problem in the United States and beyond. Immersive Virtual Reality (iVR) is a promising medium to motivate users through engaging virtual environments. Currently, modern iVR lacks a comparative analysis between research and consumer-grade systems for exercise and health. This article examines two such iVR mediums: the Cave Automated Virtual Environment (CAVE) and the head-mounted display (HMD). Specifically, we compare the room-scale Mechdyne CAVE and HTC Vive Pro HMD with a custom in-house exercise game that was designed such that user experiences were as consistent as possible between both systems. To ensure that our findings are generalizable for users of varying abilities, we recruited 40 participants with and without cognitive disabilities with regard to the fact that iVR environments and games can differ in their cognitive challenge between users. Our results show that across all abilities, the HMD excelled in in-game performance, biofeedback response, and player engagement. We conclude with considerations in utilizing iVR systems for exergaming with users across cognitive abilities.

Immersive Virtual Reality paired with soft robotics may be synergized to create personalized assistive therapy experiences. Virtual worlds hold power to stimulate the user with newly instigated low-cost, high-performance commercial Virtual Reality (VR) devices to enable engaging and accurate physical therapy. Soft robotic wearables are a versatile tool in such stimulation. This preliminary study investigates a novel rehabilitative VR experience, Project Butterfly (PBF), that synergizes VR Mirror Visual Feedback Therapy with soft robotic exoskeletal support. Nine users of ranging ability explore an immersive gamified physio-therapy experience by following and protecting a virtual butterfly, completed with an actuated robotic wearable that motivates and assists the user to perform rehabilitative physical movement. Specifically, the goals of this study are to evaluate the feasibility, ease-of-use, and comfort of the proposed system. The study concludes with a set of design considerations for future immersive physio-rehab robotic-assisted games.

This paper discusses a virtual reality (VR) therapeutic video game for treatment of the neurological eye disorder, Amblyopia. Amblyopia is often referred to as lazy eye, and it entails weaker vision in one eye due to a poor connection between the eye and the brain. Until recently it was thought to be untreatable in adults, but new research has proven that with consistent therapy even adults can improve their Amblyopia, especially through perceptual learning and video games. Even so, therapy compliance remains low due to the fact that conventional therapies are perceived as either invasive, dull and/or boring. Our game aims to make Amblyopia therapy more immersive, enjoyable and playful. The game was perceived by our users to be a fun and accessible alternative, as it involves adhering a Bangerter foil (an opaque sticker) on a VR headset to blur vision in an Amblyopic person's dominant eye while having them playa VR video game. To perform well in the video game, their brain must adapt to rely on seeing with their weaker eye, thereby reforging that neurological connection. While testing our game, we also studied users behavior to investigate what visual and kinetic components were more effective therapeutically. Our findings generally show positive results, showing that visual acuity in adults increases with 45 minutes of therapy. Amblyopia has many negative symptoms including poor depth perception (nec-essary for daily activities such as driving), so this therapy could be life changing for adults with Amblyopia.

Most robotics literature model the human's knee and hip as a revolute joint with limited range of rotation. Although somehow close to reality, this approach neglects a critical aspect of these joints, which is their internal flexibility. This paper presents a prototype tensegrity flexural manipulator whose kinematic behavior is inspired by human leg's gait. This prototype, which considers a hybrid (flexible-rigid) structure of the knee and hip would be able to better approximate real behavior and hopefully lead to a better design of artificial (prosthetic) knees and hips. The behavior of the proposed tensegrity manipulator was firstly predicted using OpenSim simulation environment. The paper reports the comparisons between the simulations, physical prototypes and human leg behavior for a variety of ranges of motions and tension analysis.

Traditional forms of physical therapy and rehabilitation are often based on therapist observation and judgment, coincidentally this process oftentimes can be inaccurate, expensive, and non-timely. Modern immersive Virtual Reality systems provide a unique opportunity to make the therapy process smarter. In this paper, we present an immersive virtual reality stroke rehabilitation game based on a widely accepted therapy method, Constraint-Induced Therapy, that was evaluated by nine post-stroke participants. We implement our game as a dynamically adapting system that can account for the user's motor abilities while recording real-time motion capture and behavioral data. The game also can be used for tele-rehabilitation, effectively allowing therapists to connect with the participant remotely while also having access to +90Hz real-time biofeedback data. Our quantitative and qualitative results suggest that our system is useful in increasing affordability, accuracy, and accessibility of post-stroke motor treatment.

We propose a flexible, reconfigurable robot which achieves rectilinear locomotion by coupling structural deformation and directional friction promoting a locomotion strategy ideal for traversing narrow channels. The robot uses two linear actuators to generate structural waves, which propagate through the robot lifting and changing the direction of motion of the contact points (feet) between the robot and ground. The reconfigurations of the robot allow different structural waveforms to alter the robot's gait. The paper describes the modular lattice structure used to build the robot; the finite element modeling approach used to understand the structural deformation induced by the linear actuators as well as the experimental validation of the prototype robot.

The increasing complexity of soft and hybrid-soft robots highlights the need for more efficient methods of minimizing machine learning solution spaces, and creative ways to ease the process of rapid prototyping. In this paper, we present an initial exploration of this process, using hand-chosen morphologies. Four different choices of muscle groups will be actuated on a tensegrity quadruped called MountainGoat: three for a primarily spine-driven morphology, and one for a primarily leg-driven morphology, and the locomotion speed will be compared. Each iteration of design seeks to reduce the total number of active muscles, and consequently reduce the dimensionality of the problem for machine learning, while still producing effective locomotion. The reduction in active muscles seeks to simplify future rapid prototyping of the robot.

In this paper, we present a lightweight, multi-axis compliant tensegrity joint that is biologically inspired by the human elbow. This tensegrity elbow actuates by shortening and lengthening cables in a method inspired by muscular actuation in a person. Unlike many series elastic actuators, this joint is structurally compliant not just along each axis of rotation, but along other axes as well. Compliant robotic joints are indispensable in unpredictable environments, including ones where the robot must interface with a person. The joint also addresses the need for functional redundancy and flexibility, traits which are required for many applications that investigate the use of biologically accurate robotic models.

From the viewpoint of evolution, vertebrates first accomplished locomotion via motion of the spine. Legs evolved later, to enhance mobility, but the spine remains central. Contrary to this, most robots have rigid torsos and rely primarily on movement of the legs for mobility. The force distributing properties of tensegrity structures presents a potential means of developing compliant spines for legged robots, with the goal of driving motion from the robots core. We present an initial exploration of the morphological design of a tensegrity quadruped robot, the first to the authors' knowledge, which we call MountainGoat, and its impact on controllable locomotion. All parts of the robot, including legs and spine, are compliant. Locomotion is aided by the use of central pattern generators, feedback control via a neural network, and machine learning techniques involving the Monte Carlo method as well as genetic evolution for parameter optimization. Control is demonstrated with three variations of MountainGoat, focusing on actuation of the spine as central to the locomotion process.

Most traditional robotic mechanisms feature inelastic joints that are unable to robustly handle large deformations and off-axis moments. As a result, the applied loads are transferred rigidly throughout the entire structure. The disadvantage of this approach is that the exerted leverage is magnified at each subsequent joint possibly damaging the mechanism. In this paper, we present two lightweight, elastic, bio-inspired tensegrity robotic arms adapted from prior static models which mitigate this danger while improving their mechanism's functionality. Our solutions feature modular tensegrity structures that function similarly to the human elbow and the human shoulder when connected. Like their biological counterparts, the proposed robotic joints are flexible and comply with unanticipated forces. Both proposed structures have multiple passive degrees of freedom and four active degrees of freedom (two from the shoulder and two from the elbow). The structural advantages demonstrated by the joints in these manipulators illustrate a solution to the fundamental issue of elegantly handling off-axis compliance. Additionally, this initial experiment illustrates that moving tensegrity arms must be designed with large reachable and dexterous workspaces in mind, a change from prior tensegrity arms which were only static. These initial experiments should be viewed as an exploration into the design space of active tensegrity structures, particularly those inspired by biological joints and limbs.

We report a system for gamifying physical therapy for stroke survivors that provides a fully immersive 3D environment. The system consists of a Kinect, an Oculus Rift immersive goggle and a pair of haptic gloves. The Kinect is used to provide bodily gesture for the game as well as to record data to allow assessment of therapy progress regarding body postures. The haptic gloves operate as a controller for the player's in-game hands as well as to record data that provides therapy progress regarding finger flexibility.