Renovo: Sensor-Based Visual Assistive Technology for Physiotherapists in the Rehabilitation of Stroke Patients with Upper Limb Motor Impairments

TL;DR

Renovo, a working prototype of a wearable motion sensor-based assistive technology that assists physiotherapists with real-time visualization and quantitative analysis of performance metrics, suggests that while the expertise of a physiotherapist is irreplaceable, Renovo can assist in the decision-making process by providing valuable quantitative information.

Abstract

Stroke patients with upper limb motor impairments are re-acclimated to their corresponding motor functionalities through therapeutic interventions. Physiotherapists typically assess these functionalities using various qualitative protocols. However, such assessments are often biased and prone to errors, reducing rehabilitation efficacy. Therefore, real-time visualization and quantitative analysis of performance metrics, such as range of motion, repetition rate, velocity, etc., are crucial for accurate progress assessment. This study introduces Renovo, a working prototype of a wearable motion sensor-based assistive technology that assists physiotherapists with real-time visualization of these metrics. We also propose a novel mathematical framework for generating quantitative performance scores without relying on any machine learning model. We present the results of a three-week pilot study involving 16 stroke patients with upper limb disabilities, evaluated across three successive sessions at one-week intervals by both Renovo and physiotherapists (N=5). Results suggest that while the expertise of a physiotherapist is irreplaceable, Renovo can assist in the decision-making process by providing valuable quantitative information.

Figures (13)

• Figure 1: Illustrations of the basic motions of the upper limb gerhardt2001goniometricmaciejasz2014surveyli2017motor with respective range of motion wiki:xxxvroman2013occupational.
• Figure 2: Placement of the wearable sensors ($IMU_1$ and $IMU_2$) on a patient's affected left upper limb. $IMU_1$ is worn on the upper arm, while the position of $IMU_2$ varies depending on the intervention (worn on the forearm in this case).
• Figure 3: Layout of the user interface of Renovo.
• Figure 4: Workflow diagram of Renovo. Motion data is acquired from the inertial sensors, followed by data processing and real-time visualization using the user interface.
• Figure 5: Workflow diagram of generating the Reference PMV (RPMV) of a particular motion of the upper limb from the corresponding data of the 5 healthy subjects.