This study aims to evaluate the validity and reliability of a novel AI-based physiotherapy evaluation system for measuring oromandibular and neck-shoulder range of motion (ROM). Traditional ROM assessments rely on manual measurements, which may be influenced by rater experience and variability. The proposed AI system uses automated keypoint tracking to provide objective and standardized measurements.

In this cross-sectional study, healthy adult participants will perform standardized ROM tasks. Measurements obtained from the AI system will be compared with those from two independent raters using conventional clinical tools. Repeated measurements will be conducted to assess intra-rater and inter-rater reliability. The agreement between the AI system and human raters will be evaluated to determine the system's clinical applicability.

This study is a cross-sectional measurement study designed to evaluate the reliability and concurrent validity of an AI-based physiotherapy evaluation system for assessing oromandibular and neck-shoulder range of motion (ROM). Participants will be healthy adults aged 20 to 70 years who meet predefined inclusion and exclusion criteria. After providing informed consent, participants will perform standardized movements, including mouth opening and cervical and shoulder ROM tasks.

Each participant will undergo three repeated measurements for each movement. ROM will be assessed using three methods: (1) an AI-based system utilizing real-time keypoint tracking and automated angle calculation, (2) manual measurement by Rater 1, and (3) independent manual measurement by Rater 2 using a goniometer or TheraBite ROM scale.

To minimize measurement bias and fatigue effects, the order of the three assessment methods will be randomized for each participant. Raters will be blinded to each other's measurements and to the AI-generated results.

The primary outcomes include inter-rater reliability and intra-rater reliability of the AI system, as well as agreement between AI-based and manual measurements. Reliability will be assessed using intraclass correlation coefficients (ICC), while agreement will be evaluated using Bland-Altman analysis and mean absolute error (MAE).

This study is expected to provide evidence supporting the clinical applicability of AI-based physiotherapy assessment tools, particularly for standardized and scalable musculoskeletal evaluations.

Inclusion Criteria:

• Healthy adults aged 20 to 70 years
• No trismus
• No history of head, neck, or shoulder injury or surgery
• No history of head and neck cancer-related radiotherapy or chemotherapy

Exclusion Criteria:

• Inability to communicate or follow instructions
• Any condition that may affect movement performance

The study population consists of healthy adult volunteers aged 20 to 70 years recruited through non-probability sampling. Participants without a history of trismus, head and neck cancer, or musculoskeletal conditions affecting the head, neck, or shoulder regions are eligible. All participants are capable of following instructions and performing standardized movement tasks. This population is selected to establish baseline measurement performance and to evaluate the reliability and validity of the AI-based physiotherapy evaluation system under controlled conditions.

Individual participant data (IPD) will not be shared due to privacy considerations and institutional regulations. Although the AI system processes de-identified keypoint data, the dataset may still contain information that could potentially be re-identified. Data sharing may be considered upon reasonable request and subject to institutional review and data protection policies.