Introduction

Cerebral apoplexy, commonly referred to as stroke, is one of the principal aetiologies of enduring disabilities worldwide1. Statistical analyses indicated that approximately 70% of individuals who recovered from stroke experienced motor dysfunctions after discharge from medical facilities, with upper limb hemiparesis being the most prevalent complication. This impairment significantly hampers the ability of stroke survivors to perform activities of daily living (ADL)2,3, thereby imposing profound psychological and financial distress upon patients and their kin.

Empirical evidence suggests that prolonged and consistent rehabilitation training can significantly ameliorate motor functional impairments resulting from stroke4,5. However, the vast majority of stroke survivors lack the resources necessary for sustained clinical rehabilitation, and the rehabilitation process is hampered by factors such as time, distance, costs, the scarcity of specialists, and limited clinical facilities6,7,8,9. Studies have indicated that home-based rehabilitation represents a more sustainable model of recovery, facilitating more effective promotion of long-term rehabilitation training for patients and transitioning care services towards a decentralized model10,11,12, which is divergent from traditional clinical therapeutic approaches13,14. Compared with hospital-based rehabilitation, home rehabilitation offers greater flexibility and autonomy, affording patients substantial amounts of time for self-directed training, and home-based recovery has been shown to reduce rehabilitation healthcare costs by up to 15%15. Nonetheless, for patients receiving rehabilitation therapy within the home environment, accurately assessing the quality and quantity of rehabilitation movements poses a significant challenge. Research has documented patients’ difficulties in precisely reporting the quantity and quality of exercises performed16,17,18, as well as their struggles to adhere to rehabilitation training over extended periods16. Consequently, the classification of rehabilitation movements has emerged as a pressing issue that needs addressing in the context of home-based rehabilitation.

Rapid advancements in artificial intelligence (AI) technologies are pivotal in supporting decentralized care models. Specifically, the development of unobtrusive motion capture technologies to establish simple, safe, effective, and objective systems for movement classification has become a strategic application in supporting remote home-based rehabilitation models4,19. Traditional systems for movement classification include (1) camera-based systems20,21,22, among which depth cameras, exemplified by Kinect23, have achieved significant milestones in the domain of rehabilitation motion recognition24,25,26,27. However, camera-based solutions necessitate ample, unobstructed observational space to monitor participants, a requirement that is challenging to meet outside laboratory environments28,29. Moreover, the deployment of visual recognition technologies within domestic settings may trigger privacy invasion concerns30,31. (2) Wearable-based inertiiial sensor systems, which employ either single or multiple inetial measurement units (IMUs), facilitate the determination of each limb’s position by resolving the corresponding kinematic equations32,33,34.

Inertial motion analysis has attracted increasing interest in recent decades because of its advantages over classical optical systems35. Owing to their integrative capabilities, low cost, and ease of implementation, IMUs are extensively employed for monitoring rehabilitation exercises within home settings36,37,38,39. Table 1 provides a detailed presentation of the relevant works associated with this study, including achievements in movement classification using either a single or multiple sensors. For example, Zinnen et al.40 reported a classification accuracy of 93% for 20 movements via a five-device system and an accuracy of 86% with two wrist devices, covering actions such as manipulating various car doors and a writing task. Zhang et al.41 introduced a two-device framework that achieved 97.2% accuracy in classifying four arm movements. Lui and Menon33, Wang et al.42, Alessandrini et al.43, and Li et al.44 all rely on multiple sensors for classification. Similarly, studies by Basterretxea et al.45, Xu and Yuan46, and Choudhury et al.47 also demonstrate high accuracy, with substantial diversity in the classified movements. However, as shown in the relevant studies listed in Table 1, few studies have achieved high-precision motion classification using a single sensor. Multi-device systems are generally more complex than single-device setups are, particularly when devices are affixed to multiple limbs or when interdevice calibration is needed. Whenever possible, a single-device system is preferred due to its ease of wearability and configuration33. The rapid development of machine learning algorithms has made it possible to achieve rehabilitation movement classification using a single inertial sensor. For example, Zhang et al.48 achieved an accuracy of 96.1% for nine activities among healthy participants, including actions such as walking, running, sitting, and standing. In another study, Khan et al.49 achieved a precision of 97.9% using chest-mounted devices for 15 movements across various activities (e.g., lying, sitting, standing, and transitions among these postures). The rapid advancement of machine learning algorithms has facilitated the implementation of rehabilitation movement classification via a single inertial sensor. Tseng et al.50 reported a single-device system that achieved a classification accuracy of 93.3% for three movements associated with door opening in a study involving five healthy participants. Zhang et al.51 presented a system employing a solitary IMU for discerning six-arm manoeuvres among fourteen stroke subjects, with classification accuracies of 99.4% with an innovative algorithm and 98.8% when an SVM framework was used. These manoeuvres, which encapsulate four of the human arms’ seven degrees of freedom, include flexion and extension at the shoulder joint, signifying the arm’s forward and backwards motion; abduction and adduction at the same joint, indicating lateral movement away from and towards the torso; pronation and supination of the forearm, involving rotational movement to orient the palm downwards and upwards, respectively; and flexion and extension at the elbow joint, denoting the arm’s bending and straightening. This delineation underscores the integral relationship of movements with specific joint actions, which are crucial for comprehensive upper limb rehabilitation in stroke recovery.

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