For stroke survivors and many other people with upper-extremity impairment, daily life can be difficult without properly functioning arms. Some modern physical therapy exercises focus on rehabilitating people with these troubles by correcting patients' perceptions of their own body to eventually regain complete control and strength over their arms again. Augmentative wearable robots, such as the upper-extremity exoskeletons and exosuits, may be able to assist in this endeavor. A common drawback in many of these exoskeletons, however, is their inability to conform to the natural flexibility of the human body without a rigid base. We have built one such exosuit to address this challenge: Compliant Robotic Upper-extremity eXosuit (CRUX). This robot is a compliant, lightweight, multi-DoF, portable exosuit that affords its wearer the ability to augment themselves in many unconventional settings (i.e. outside of a clinic). These attributes are largely achieved by using a modified tensegrity design situated according to measured lines of minimal-extension, where a network of tension members provide a foundation to apply augmentative forces via precisely placed power-lines. In this paper, we detail the design process of CRUX, the report on CRUX's prototypical composition, and describe the mimetic control algorithm used. We also discuss the results of three studies that illustrate the efficacy of CRUX's mimetic controller, CRUX's flexibility and compliance, and the metabolic cost reduction when users exercise with assistance from CRUX as opposed to without. We conclude this paper with a summary of our findings, potential use cases for this technology, and the direction of future related work.

Wearable robots can potentially offer their users enhanced stability and strength. These augmentations are ideally designed to actuate harmoniously with the user's movements and provide extra force as needed. The creation of such robots, however, is particularly challenging due to the underlying complexity of the human body. In this paper, we present a compliant, robotic exosuit for upper extremities called CRUX. This exosuit, inspired by tensegrity models of the human arm, features a lightweight (1.3 kg), flexible multi-joint design for portable augmentation. We also illustrate how CRUX maintains the full range of motion of the upper-extremities for its users while providing multi-DoF strength amplification to the major muscles of the arm, as evident by tracking the heart rate of an individual exercising said arm. Exosuits such as CRUX may be useful in physical therapy and in extreme environments where users are expected to exert their bodies to the fullest extent.

The flexibility and structural compliance of the biological shoulder joint allows humans to perform a wide range of motions with their arms. The current paper is a preliminary study in which we propose a structurally compliant robotic manipulator joint inspired by the human shoulder joint, which elastically deforms when actuated. The tensile actuation is similar to the contraction and extension of biological muscles. We present four separate models for the shoulder: a simple saddle, a complex saddle, a suspended tubercle, and interlocked tetrahedrons. The analysis explores the dynamics in each design to compare the inherent advantages and disadvantages, which gives insight into the design and development of better interfaces for biologically inspired human-oriented robotics.

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Objective: The adoption of telehealth rapidly accelerated due to the global COVID19 pandemic disrupting communities and in-person healthcare practices. While telehealth had initial benefits in enhancing accessibility for remote treatment, physical rehabilitation has been heavily limited due to the loss of hands-on evaluation tools. This paper presents an immersive virtual reality (iVR) pipeline for replicating physical therapy success metrics through applied machine learning of patient observation. Methods: We demonstrate a method of training gradient boosted decision-trees for kinematic estimation to replicate mobility and strength metrics with an off-the-shelf iVR system. During a two-month study, training data was collected while a group of users completed physical rehabilitation exercises in an iVR game. Utilizing this data, we trained on iVR based motion capture data and OpenSim biomechanical simulations. Results: Our final model indicates that upper-extremity kinematics from OpenSim can be accurately predicted using the HTC Vive head-mounted display system with a Mean Absolute Error less than 0.78 degrees for joint angles and less than 2.34 Nm for joint torques. Additionally, these predictions are viable for run-time estimation, with approximately a 0.74 ms rate of prediction during exercise sessions. Conclusion: These findings suggest that iVR paired with machine learning can serve as an effective medium for collecting evidence-based patient success metrics in telehealth. Significance: Our approach can help increase the accessibility of physical rehabilitation with off-the-shelf iVR head-mounted display systems by providing therapists with metrics needed for remote evaluation.