Stroke is a leading cause of long-term disability as well as death worldwide, where aging is among the most significant nonmodifiable factors. Motor impairments related to post-stroke hemiplegia, resulting from the loss of specific brain functions following the acute event, commonly affect both upper and lower limbs, leading to a deterioration in perceived quality of life as daily activities become unsafe and difficult to perform. Various rehabilitation strategies and therapies are commonly adopted in the hospital setting during the post-acute phase to recover, at least partially, major motor functions and improve physical mobility to ensure the patients' safety in daily life. However, these functions should be stimulated continuously and frequently through maintenance activities in order not to lose the level of functional recovery achieved and avoid subsequent hospitalizations for new rehabilitation treatments. This paper proposes the use of gamified tasks in a virtual environment to enhance upper limb mobility. Gamified tasks are performed using a single RGB-D camera-based vision system (specifically, Microsoft Azure Kinect DK) suitable for easy deployment in home environments. Non-invasive body tracking models are employed to capture 3D upper limb trajectories in real time and measure, through objective parameters, the unilateral and bilateral movements required by each task. Preliminary results on a small cohort of post-stroke subjects show a general progress in upper limb mobility and coordination, in agreement with an improvement in some clinical severity scores and tests. This suggests that the proposed solution is suitable for continuous stimulation of upper limb function and performance monitoring over time in the home environment, contributing to the improvement of the patient's general motor condition and increased physical well-being in daily life.

Enhancing upper limb mobility through gamified tasks and Azure Kinect: a preliminary study in post-stroke subjects

AMPRIMO, GIANLUCA;PETTITI, GIUSEPPE;FERRARIS, CLAUDIA
2023

Abstract

Stroke is a leading cause of long-term disability as well as death worldwide, where aging is among the most significant nonmodifiable factors. Motor impairments related to post-stroke hemiplegia, resulting from the loss of specific brain functions following the acute event, commonly affect both upper and lower limbs, leading to a deterioration in perceived quality of life as daily activities become unsafe and difficult to perform. Various rehabilitation strategies and therapies are commonly adopted in the hospital setting during the post-acute phase to recover, at least partially, major motor functions and improve physical mobility to ensure the patients' safety in daily life. However, these functions should be stimulated continuously and frequently through maintenance activities in order not to lose the level of functional recovery achieved and avoid subsequent hospitalizations for new rehabilitation treatments. This paper proposes the use of gamified tasks in a virtual environment to enhance upper limb mobility. Gamified tasks are performed using a single RGB-D camera-based vision system (specifically, Microsoft Azure Kinect DK) suitable for easy deployment in home environments. Non-invasive body tracking models are employed to capture 3D upper limb trajectories in real time and measure, through objective parameters, the unilateral and bilateral movements required by each task. Preliminary results on a small cohort of post-stroke subjects show a general progress in upper limb mobility and coordination, in agreement with an improvement in some clinical severity scores and tests. This suggests that the proposed solution is suitable for continuous stimulation of upper limb function and performance monitoring over time in the home environment, contributing to the improvement of the patient's general motor condition and increased physical well-being in daily life.
2023
Istituto di Elettronica e di Ingegneria dell'Informazione e delle Telecomunicazioni - IEIIT
Upper limb rehabilitation
Azure Kinect
Home Monitoring System
Artificial Intelligence
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/430006
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