Conference Agenda

INTRODUCTION

Shoulder injuries, particularly those affecting the rotator cuff (RC), are common and result from repetitive movements, excessive strain, or trauma, leading to pain, muscle weakness, and reduced joint mobility. Rehabilitation, especially post-surgical, is essential to restore the range of motion (ROM) and prevent complications. However, recovery assessment is often based on subjective medical evaluations, limiting accuracy. While motion capture (MOCAP) systems offer more objective assessments, they are expensive and not easily accessible for routine clinical monitoring. An accessible system is needed to track shoulder movements during rehabilitation, enhancing therapy programs with real-time feedback.

METHODS

This study was funded by the European Union - Next Generation EU - PNRR M6C2 - Investment 2.1 Enhancement and strengthening of biomedical research in the National Health Service (Project No. PNRR-MAD-2022-12376080 - CUP: F83C22002450001). The study developed a wearable sensor based on fiber Bragg gratings (FBG) and 3D printing to monitor shoulder movements objectively and continuously. Made of thermoplastic polyurethane (TPU), known for its flexibility and durability, the sensor incorporates an embedded FBG sensitive to strain and temperature variations. The TPU substrate design, shaped like a "dog bone," optimizes strain transmission in the sensor's central section.

Two stretchable anchoring bands, integrated during printing, ensure stability and ease of use.

Tests evaluated strain sensitivity and hysteresis error using a tensile testing machine and temperature sensitivity through a laboratory oven. A preliminary test on a healthy volunteer assessed sensor performance in monitoring shoulder movements at different angles (0-30°; 0°-60°; 0°-90°) and speeds (0°-90° in 3s and 6s).

RESULTS

The sensor exhibited an average strain sensitivity (S_ε) of 1.45 nm/mε and a temperature sensitivity (S_T) of 0.02 nm/°C, slightly higher than a bare FBG due to TPU's thermal expansion. However, the hysteresis error was high (51%), indicating a nonlinear response under dynamic conditions.

During preliminary tests on a healthy volunteer, the sensor detected shoulder movements in the sagittal plane, with output variations (Δλ_B) proportional to ROM (0.22 nm at 30°, 0.46 nm at 60°, 0.77 nm at 90°) and speed (6-second cycles: mean Δλ_B of 0.784 nm ± 0.028 nm; 3-second cycles: mean Δλ_B of 0.762 nm ± 0.029 nm). These findings demonstrate the sensor's potential for real-time monitoring and clinical applications.

CONCLUSIONS

The developed sensor combines FBG and 3D printing to monitor shoulder movements during rehabilitation, offering high sensitivity and an ergonomic design. Preliminary results are promising but highlight limitations, such as high hysteresis error and TPU's thermal influence.

Future developments aim to improve linearity, reduce thermal effects, and lower hysteresis, as well as test the device on a larger sample. Additionally, sensor evaluations will be extended to shoulder movements in the frontal and scapular planes to provide a more comprehensive analysis in real-world conditions.

This sensor represents an affordable, portable, and non-invasive solution with the potential to revolutionize rotator cuff rehabilitation and other medical and sports applications, providing objective data to optimize therapy pathways and improve functional outcomes.

INTRODUCTION: Telerehabilitation represents an innovative solution for remotely monitoring and managing physical rehabilitation, enabling the execution and supervision of exercises in home or non-clinical settings. This approach has the potential to enhance the overall quality of rehabilitation care, promoting an inclusive strategy that addresses challenges related to accessibility and the sustainability of healthcare services. Among the various technologies explored, thermal cameras, when integrated with computer vision and artificial intelligence (AI) algorithms, offer significant advantages. They enable non-invasive monitoring and the detection of thermal variations associated with muscle movement and physical activity while also addressing privacy concerns, an essential aspect for home-based applications.
This study aims to evaluate the performance of an integrated system based on AI algorithms and thermal cameras for the automatic recognition of upper limb rehabilitation movements.

METHODS: This work was funded by the European Union - Next Generation EU - PNRR M6C2 - Investment 2.1 Enhancement and Strengthening of Biomedical Research in the National Health Service (Project No. PNRR-MAD-2022-12376080 - CUP: F83C22002450001). A total of 20 healthy volunteers were recruited. Data were acquired using a thermal camera (SEEK THERMAL Compact Pro) connected to an Android smartphone, positioned at a fixed distance from the subjects. The experimental protocol included ten shoulder rehabilitation exercises, each repeated an average of six times in three different scenarios: (1) Flexion/Extension, (2) Abduction/Adduction, (3) Scapular plane elevation, (4) External rotation, (5) External rotation with shoulder at 90° abduction, (6) Military press in standing position, (7) Wall slide in standing position, (8) Towel slide, (9) Forward bow, and (10) Pendulum. After a preprocessing phase, the recorded videos were processed through a pipeline that included frame extraction and the identification of 17 anatomical key points. These data were subsequently used to train (70% of the dataset) and validate (30% of the dataset) different AI models, including both machine learning (ML) and deep learning (DL) ones, for the automatic classification of rehabilitation exercise types.

RESULTS: The proposed computer vision approach demonstrated excellent performance on the test data. The findings show the superior performance of the DL model, achieving 95% accuracy in classifying the different exercise classes and an average Area Under the ROC curve of 0.97. The model was able to autonomously extract both spatial and temporal features from the input data, enabling it to capture more complex patterns and relationships within the data. The classification accuracies for all exercises ranged from 74% to 100%, underscoring the robustness of the model even when subjects performed the exercises in various scenarios.

CONCLUSIONS: The results demonstrate the effectiveness of the model in recognizing shoulder rehabilitation exercises. This study highlights the potential of thermography-based telerehabilitation as an innovative tool for remote monitoring. Such an approach can support both physiotherapists and patients in maintaining rehabilitation programs at home, reducing the need for in-person visits and enabling continuous supervision. Further studies should focus on evaluating the performance of such approach on data collected from patients with musculoskeletal disorders, and incorporating objective assessment of the executed exercises.

Purpose
A recent pilot study (Raso et al., J Exp Orthop, 2024) highlighted the potential of the NAO humanoid robot in guiding shoulder rehabilitation exercises, demonstrating promising results in improving exercise consistency and patient engagement. However, that work relied heavily on an external optical motion‐capture laboratory to track shoulder kinematics, creating barriers to widespread clinical and home‐based adoption. Building on those findings, this new study explores an AI‐driven approach to embed motion‐tracking capability directly within the NAO robot using its integrated camera. Our overarching goal is to remove the reliance on specialized motion‐capture labs and thereby extend the possible reach of robotic rehabilitation—whether in smaller outpatient clinics or in patients’ homes.

Methods
We modified our existing NAO‐based rehabilitation protocol, previously validated with an external motion‐capture system, to incorporate a custom AI module for onboard motion capture. In this updated configuration, NAO uses its camera feed to perform live pose estimation, focusing on key landmarks around the shoulder complex. A convolutional neural network (CNN) processes these video frames in real time, estimating the user’s joint angles and range of motion (ROM) without external sensors. The robot then provides adaptive, step‐by‐step verbal and gestural cues to guide flexion–extension, external rotation, and internal rotation exercises at varying speeds. Preliminary data collection involved a small sample of healthy individuals replicating the earlier lab‐based exercises. By comparing NAO’s internal AI‐generated kinematics to “gold standard” motion‐capture metrics, we qualitatively assessed accuracy, ease of use, and potential for clinical integration.

Results
Preliminary analyses suggest that NAO’s onboard AI system can capture and estimate shoulder ROM with moderate fidelity relative to the optical laboratory standard—particularly within mid‐range movement arcs crucial for early to mid‐stage rehabilitation. While greater deviations arose at the extremes of the ROM, the absolute mean errors observed in pilot sessions remain within clinically acceptable thresholds for guiding exercise performance. Participants reported a high level of confidence in NAO’s real‐time feedback and found the single‐unit robotic system easier to set up than traditional motion‐capture equipment. These early results mirror the positive engagement documented in our earlier study, indicating that the robot‐patient interaction is maintained without needing an external camera system.

Conclusions
By integrating an AI‐powered motion‐tracking module directly into NAO, we build on the success of our prior study and advance toward a more accessible form of robot‐assisted therapy for shoulder rehabilitation. Although further refinements and larger clinical trials are needed—especially to verify accuracy in individuals with shoulder pathologies—our findings underscore the potential of a “one‐system” solution to deliver robust rehabilitation guidance in non‐specialized settings. If ongoing development continues to validate these early results, clinicians could deploy NAO in small outpatient clinics or patients’ homes, reducing the need for fully equipped motion‐analysis laboratories. This shift toward accessible AI‐driven robotics could significantly expand the scope of shoulder rehabilitation, improving patient adherence and long‐term outcomes while decreasing the burden on clinical facilities.