## 1 Introduction  
Human disabilities affect millions of people worldwide and can result from congenital conditions, accidents, or aging. Among these, motor disorders specifically impair movement and sensory perception, posing significant challenges that require continuous research and innovation related to new technologies that can improve quality of life. Mobility, independence, and the ability to perform activities of daily living (ADLs) are critical factors that greatly influence the quality of life of individuals with motor impairments [1]. Among sensorimotor impairments, spinal cord injuries (SCIs) represent a significant disability, affecting 250,000 to 500,000 people per year [2]. These injuries affect signal transmission between the brain and the body, causing permanent changes in motor, sensory, and automatic functions below the injury level. SCIs are classified by injury level (cervical, thoracic, lumbar, sacral) and completeness (complete or incomplete). Higher injury levels are associated with more severe motor impairments, resulting in greater challenges in leading an independent life and controlling assistive devices designed to support activities of daily living. Although loss of mobility is the most apparent consequence, SCIs also cause critical complications that affect other body functions. Statistics indicate that 20-30% of people with SCI experience clinically significant depression ([[https://www.who.int/publications/i/item/9789241564588](https://www.who.int/publications/i/item/9789241564588)]([https://www.who.int/publications/i/item/9789241564588](https://www.who.int/publications/i/item/9789241564588)), accessed on 9 February 2025), highlighting the need for interdisciplinary care that addresses both physical and emotional well-being. Due to their great impact, spinal cord injuries are a key focus in assistive robotics, whose objective is to improve independence and quality of life [3]. Human-machine interfaces (HMIs) are essential for integrating human cognitive and physical abilities with machine functionalities, facilitating communication between users and external systems [4]. A subset of these systems, known as body-machine interfaces (BoMIs), exploits residual human signals to generate control inputs for external devices such as prosthetic limbs, powered wheelchairs, robotic arms, manipulators, or displays. While BoMIs can rely on various human signals--such as muscle contractions, body movements, eye gaze, or tongue gestures--a particular subgroup, brain-computer interfaces (BCIs), focuses exclusively on brain signals. However, this study emphasizes BoMIs that exploit users' residual body movements [5]. By mapping such motions to external devices, BoMIs can expand the functional capabilities of users to compensate for lost sensorimotor functions [6]. Body movements, coming from users' residual active body parts, are the control inputs providing useful information about users' intent, which can be integrated in the control loop with the great advantage of being a non-invasive solution. Despite promising advancements, several challenges and limitations are still present. A key issue is designing interfaces that are both intuitive and adaptable to different levels of impairment. Overcoming this issue is important to enhance the use of assistive devices and to reduce the abandonment rate. BoMIs employ several sensor technologies, including infrared cameras [7], inertial measurement units (IMUs) [8], EMG sensors [9], or hybrid combinations [10], to capture user's measurements effectively. In general, residual movements exploited by BoMIs are voluntary or involuntary controllable motions that remain in individuals with partial or severe motor impairments. When designing a BoMI, it is crucial to consider the ability to discriminate between voluntary and involuntary motions. For instance, [PERSON] et al. [11] explain how the use of electroencephalographic signals (EEGs) in BMIs holds great potential as an indicator of voluntary movements.