The Use of Head-Mounted Display Systems for Upper Limb Kinematic Analysis in Post-Stroke Patients: A Perspective Review on Benefits, Challenges and Other Solutions

Abstract

In recent years, there has been a notable increase in the clinical adoption of instrumental upper limb kinematic assessment. This trend aligns with the rising prevalence of cerebrovascular impairments, one of the most prevalent neurological disorders. Indeed, there is a growing need for more objective outcomes to facilitate tailored rehabilitation interventions following stroke. Emerging technologies, like head-mounted virtual reality (HMD-VR) platforms, have responded to this demand by integrating diverse tracking methodologies. Specifically, HMD-VR technology enables the comprehensive tracking of body posture, encompassing hand position and gesture, facilitated either through specific tracker placements or via integrated cameras coupled with sophisticated computer graphics algorithms embedded within the helmet. This review aims to present the state-of-the-art applications of HMD-VR platforms for kinematic analysis of the upper limb in post-stroke patients, comparing them with conventional tracking systems. Additionally, we address the potential benefits and challenges associated with these platforms. These systems might represent a promising avenue for safe, cost-effective, and portable objective motor assessment within the field of neurorehabilitation, although other systems, including robots, should be taken into consideration.

Keywords: head-mounted display; motion capture; neurorehabilitation; post-stroke; upper limb kinematics; virtual reality.

Figure 1

Conventional MoCap systems used for kinematic analysis of upper limb. (A) An example of a commercial optoelectronic MoCap system setup (Optitrack Flex 13 Motion Capture Camera [40]), which consists of several IR cameras where subjects are instrumented with reflective markers; (B) one of the subjects, who is wearing one of the commercially available IMU Mocap systems (Xsens Awinda [41]); (C) VBMA key points detection using an OpenPose pose estimation algorithm through a camera [42].

Figure 2

HMD-VR platforms. (A) An outside-in HMD-VR platform (HTC Vive, HTC Europe Co., Ltd., Slough, Berkshire, UK). This system consists of an HMD (1), 2 controllers (2), 2 trackers (3), 2 base stations or “lighthouses” (4). (B) An inside-out system (Meta Quest 3, Meta Technologies LLC, New York, NY, USA), which simply consists of an HDM (1) with integrated cameras (2) and 2 controllers (3). (C) Three-dimensional hand model representation developed with Unity [73] software (version 2023.2.1f1) and the device’s software development kit (Meta XR Core SDK, version 65.0) [74].

Figure 3

Tracking systems for HMD to estimate upper limb motion. Figure shows motion tracking technology exploited by HMDs to estimate upper limb (end-effector) position.