Trends and Innovations in Wearable Technology for Motor Rehabilitation, Prediction, and Monitoring: A Comprehensive Review

Abstract

(1) Background: Continuous health promotion systems are increasingly important, enabling decentralized patient care, providing comfort, and reducing congestion in healthcare facilities. These systems allow for treatment beyond clinical settings and support preventive monitoring. Wearable systems have become essential tools for health monitoring, but they focus mainly on physiological data, overlooking motor data evaluation. The World Health Organization reports that 1.71 billion people globally suffer from musculoskeletal conditions, marked by pain and limited mobility. (2) Methods: To gain a deeper understanding of wearables for the motor rehabilitation, monitoring, and prediction of the progression and/or degradation of symptoms directly associated with upper-limb pathologies, this study was conducted. Thus, all articles indexed in the Web of Science database containing the terms "wearable", "upper limb", and ("rehabilitation" or "monitor" or "predict") between 2019 and 2023 were flagged for analysis. (3) Results: Out of 391 papers identified, 148 were included and analyzed, exploring pathologies, technologies, and their interrelationships. Technologies were categorized by typology and primary purpose. (4) Conclusions: The study identified essential sensory units and actuators in wearable systems for upper-limb physiotherapy and analyzed them based on treatment methods and targeted pathologies.

Keywords: Parkinson’s; continuous health; physiotherapy; pos-strock; telemedicine; upper limb; wearable.

Figure 1

The data flow of the systematic review.

Figure 2

The figure overview of the technological groups identified throughout our review as well as the relationship between them.

Figure 3

Study characteristics for camera studies [28,29,30,31,32,33,34,35,36,37,38,39,40,41,42].

Figure 4

Study characteristics for complementary technologies studies [43,44,45].

Figure 5

Study characteristics for other studies [46,47,48,49,50].

Figure 6

Identifying hemiparesis using wrist-worn accelerometry, as presented in the work by S. Datta et al. [59].

Figure 7

An illustration of the sensor network used for the reconstruction of upper-limb joints from the work of Meng et al. and the anatomical model of the entire upper limb with the definition of joint axes.

Figure 8

A flowchart of the methodology for estimating task-specific ARAT scores using inertial sensors mounted on the wrist. Synthetic minority over-sampling technique (SMOTE).

Figure 9

A wristband functions as a joystick for controlling a remote-controlled car within a maze; wrist flexion-extension controls the car’s forward and backward movement, while pronation–supination enables on-the-spot turning.

Figure 10

Wearable multimodal rehabilitation utilizing serious games involves extracting kinematic data. Relevant features are identified and input into classification algorithms to predict movements that serve as inputs for the game.

Figure 11

The stimulation system proposed in the work by Ferrari et al [169].

Figure 12

Contributions from the conducted review, including the characterization of the pathologies explored in the studies as well as the main technologies addressed.