Renovo: Sensor-Based Visual Assistive Technology for Physiotherapists in the Rehabilitation of Stroke Patients with Upper Limb Motor Impairments
This addresses the need for more accurate progress assessment in stroke rehabilitation for physiotherapists, though it is incremental as it assists rather than replaces human expertise.
The study tackled the problem of biased and error-prone qualitative assessments in stroke rehabilitation by introducing Renovo, a wearable sensor-based system that provides real-time visualization and quantitative analysis of performance metrics, with results from a pilot study of 16 patients showing it can assist physiotherapists in decision-making.
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.