Optimising outcomes after shoulder interventions requires objective shoulder range of motion (ROM) assessments. This narrative review examines video-based pose technologies and markerless motion capture, focusing on their clinical application for shoulder ROM assessment. Camera pose-based methods offer objective ROM measurements, though the accuracy varies due to the differences in gold standards, anatomical definitions, and deep learning techniques. Despite some biases, the studies report a high consistency, emphasising that methods should not be used interchangeably if they do not agree with each other. Smartphone cameras perform well in capturing 2D planar movements but struggle with that of rotational movements and forward flexion, particularly when thoracic compensations are involved. Proper camera positioning, orientation, and distance are key, highlighting the importance of standardised protocols in mobile phone-based ROM evaluations. Although 3D motion capture, per the International Society of Biomechanics recommendations, remains the gold standard, advancements in LiDAR/depth sensing, smartphone cameras, and deep learning show promise for reliable ROM assessments in clinical settings.

Background: The ability to discern exercise modalities, defined here as intensity and pattern, holds significant importance in various contexts, particularly in conditions where treatment management is heavily influenced by daily physical activity. The objectives of this study were thus to evaluate the predictive capabilities of heart rate variability (HRV) components for identifying exercise modalities, and to examine whether HRV during exercise exhibits distinctive characteristics depending on the intensity and continuous or intermittent nature of the activity. Methods: We conducted HRV analysis across different exercises varying in intensity and pattern [two constant-load sessions at 40% (C40) and 60% (C60) of maximal aerobic power, and one intermittent session (INT)], using cycle ergometers with 26 volunteer subjects. HRV was analyzed using time-domain, frequency-domain, and nonlinear measures from data recorded with a Polar H10 Heart Rate Sensor chest strap and processed with Kubios HRV software. Gas exchanges and lactate levels were also measured. HRV data from the final 4 minutes of each exercise session were analyzed using penalized multinomial logistic regression to predict specific exercise modalities performed. Additionally, repeated measures ANOVA were conducted followed by post-hoc tests when appropriate, to assess differences across conditions. Results: Multinomial logistic regression model demonstrated robust predictive performance, achieving sensitivities and specificities ranging from 88 to 98%. Time-domain (meanRR, SDNN) and nonlinear (DFA-ɑ1) emerged as the retained predictors. ANOVA tests revealed distinct impacts of exercise modality on HRV components: time-domain measures, LF and HF spectral powers, and the nonlinear index DFA-ɑ1 were significantly reduced in C60 compared with C40, while time-domain variability indices, LF and HF spectral powers, and DFA-ɑ1 remained higher during INT exercise than in both other conditions. Conclusion: HRV emerges as a promising tool for distinguishing between exercise modalities with various intensity and/or continuous or intermittent pattern.

Conventional clinical assessment of upper limb kinematics relies on goniometry or visual estimation, methods that provide limited information on multi-segment 3D kinematics and can have poor inter-rater reliability. Inertial measurement units (IMUs) represent a viable alternative; however, extracting anatomically meaningful joint angles from skin-mounted sensors remains challenging owing to sensor-to-segment misalignment, soft-tissue artefact, and suboptimal orientation estimation under dynamic conditions. This study developed and validated a multi-segment IMU-based framework for upper limb kinematic assessment, addressing three stages of the measurement pipeline: (i) sensor fusion gain optimisation as a function of task mean segment angular velocity; (ii) accuracy of an anatomical landmark registration-based alignment method; and (iii) comparative evaluation of landmark-based alignment against manual and neutral-pose alignment approaches. Twenty participants with typical shoulder function performed clinical and functional upper limb tasks, recorded simultaneously with an optoelectronic motion capture system and six IMUs tracking the thorax, clavicle, scapula, humerus, forearm, and hand. Orientation estimation used a gradient-descent complementary filter with velocity-dependent gain modulation. Sensor-to-segment alignment was achieved via manual, neutral-pose, and landmark registration methods. Mean sensor orientation errors ranged from 1.5° to 3° across segments/tasks and increased with angular velocity. Landmark registration produced substantially lower joint angle errors than manual and neutral-pose alignment across all joints (root mean square error range: 3.7°–7.8°; manual: 8.3°–24.2°; neutral: 15.5°−35.5°). These results indicate that IMU-derived kinematic accuracy is context-dependent, varying with joint and movement type, suggesting that accuracy should be established for the specific movements and joints relevant to each clinical application.