Human-machine Interface using functional electrostimulation and inertial sensors for lower limb rehabilitation in spinal cord injury individuals: a proof of concept

Abstract

A spinal cord injury (SCI) is a neurological disorder that impairs motor and physiological functions and leads to a reduced quality of life and autonomy for the person affected. In this scenario, human-machine interfaces (HMIs) have emerged as an effective tool to leverage residual motor capabilities and benefit injured persons. This work aims to develop a closed-loop HMI system for lower-limb rehabilitation composed of an in-house multi-channel Functional Electrical Stimulation (FES), which is activated by considering gait and pedaling cycles measured by an Inertial Measurement Unit. Two experiments were conducted with individuals suffering partial SCI who performed cycling and walking activities by using our proposed HMI, while inertial and electroencephalography signals were collected for further analysis and validation. Relative power changes were observed in mu (8–13 Hz) and high beta (20–30 Hz) bands over the foot area (Cz location), comparing both FES and non-FES conditions during gait and pedaling. This comparison also showed that the volunteers performed physical activities with greater speed and cadence by using the proposed HMI system, which correctly identified the movement phases. Graphical abstract A spinal cord injury (SCI) is a neurological disorder that impairs motor and physiological functions, leading to reduced quality of life and autonomy for affected individuals. In this context, human–machine interfaces (HMIs) have emerged as effective tools to enhance residual motor capabilities and support rehabilitation. This study aims to develop a closed-loop HMI system for lower-limb rehabilitation composed of an in-house multi-channel Functional Electrical Stimulation (FES) device, activated based on gait and pedaling cycles measured by an Inertial Measurement Unit (IMU). Two experiments were conducted with individuals with partial SCI who performed cycling and walking tasks using the proposed HMI system, while inertial and electroencephalography (EEG) signals were recorded for further analysis and validation. Relative power changes were observed in the mu (8–13 Hz) and high beta (20–30 Hz) bands over the foot area (Cz location) when comparing FES and non-FES conditions during gait and pedaling. This comparison also revealed that participants performed physical activities with greater speed and cadence when using the proposed HMI system, which successfully identified movement phases in real time.

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