Open biomechanical datasets play a critical role in advancing scientific research, particularly with the growing use of machine learning, which requires a large amount of data for training and validation. However, the availability and characteristics of such datasets, particularly in the context of upper limb motion, are still underexplored. This systematic review aims to identify, assess, and categorize existing open datasets related to upper limb motion, focusing on the tasks analyzed, subject demographics, and data collection methods employed. We conducted a comprehensive search across multiple databases, including Scopus, Zenodo, PubMed, Xplore, and Google Dataset Search, selecting publicly accessible datasets that focus on upper limb motion based on predefined inclusion and exclusion criteria. Additional datasets were identified through manual searches. A total of 63 datasets met the criteria for further analysis, and their quality was assessed based on subject types, data collection methods, and sample sizes. The majority of the datasets centered on activities of daily living, with 69.8% of the studies involving healthy subjects. Marker-based motion capture was the most common data collection method, though the use of markerless systems (e.g., inertial sensors, video cameras) is on the rise. Key limitations included the absence of standardized movement protocols and small subject sample sizes across studies. In contrast to lower limb gait analysis, there is no single dominant task for studying upper limb motion. Standardizing movement protocols and incorporating a broader range of daily activities into datasets could enhance the development of assistive technologies and rehabilitation programs. Future research should focus on creating more standardized, diverse datasets to improve the accuracy and generalizability of biomechanical analyses.

Purpose: “Cuban Motion” (CM) is a term often used to label a basic movement of many Latin-American dances, such as salsa and bachata. It is characterized by a cyclical and rhythmic flexion and rotation of the pelvis, accompanied by ample movements of the spine and lower limbs. Since Latin dances are considered both a recreational activity and a kind of healthy gymnastics for young and older subjects, a biomechanical analysis of the CM was performed to quantitatively assess the main features of this movement, from joint kinematics to muscle activations. Method: The study combined experimental acquisitions with a Vicon Motion Capture system and musculoskeletal analyses in OpenSim. Only one subject was considered, an instructor of Latin dances, who was equipped with 55 retro-reflective markers, according to the Full Body CGM2.5 marker set. Data were processed in OpenSim, using the ThoracoLumbar model (111 DoFs and 620 musculo-tendon actuators). Results: The cycle of the CM and its main events were defined. Results showed a wide RoM of many joints, with correlations between hip and knee flexion angles and among list, between hip adduction and shoulder flexion. The main role of the iliopsoas and the ileus costalis muscles was observed. Conclusions: The current study quantified the CM as a physical exercise using a biomechanical approach similar to a gait analysis. The main kinematic and kinetic features were described, also providing an estimation of the muscle activations and joint reactions, derived from simulation activity in OpenSim. The main limitation of this study is the focus on a single subject, but this step was necessary to define a context and guidelines for future investigations, where we are going to compare èlite vs. novices and male vs. female dancers.

(1) Background: Markerless motion capture may enable scalable biomechanical assessment outside laboratory environments, yet its applicability to tasks involving elevated foot contacts and rapid loading transitions remains insufficiently characterized. This feasibility study evaluated OpenCap, an open-source smartphone-based markerless system, for estimating lower-limb kinematics and ground reaction forces during stair ascent, stair descent, squat, sit-to-stand, and forward lunge. (2) Methods: One healthy adult completed three repetitions of each task while data were acquired simultaneously with three smartphones, an eight-camera Vicon system, and two force plates. OpenCap/OpenSim outputs were adapted for stair and lunge kinetics and compared with marker-based kinematics and force-plate measurements using root mean square error, Pearson correlation, and cosine similarity. (3) Results: Agreement was strongest for sagittal-plane lower-limb kinematics, particularly knee flexion-extension (RMSE: 1.4–10.5 deg; Pearson: 0.94–0.999). Larger discrepancies occurred for hip internal-external rotation, pelvis motion during sit-to-stand, and ankle flexion-extension. Resultant GRF waveforms showed high similarity to force-plate data (cosine similarity ≥ 0.988; RMSE: 0.069–0.167 BW). Stair descent produced the largest deviations and variability, particularly for single-limb forces. (4) Conclusions: In this feasibility study, smartphone-based markerless analysis agreed best with laboratory measurements for sagittal lower-limb kinematics and resultant GRF waveforms but showed larger discrepancies in multiplanar kinematics and limb-specific loading. The adapted pipeline is technically feasible for exploratory movement analysis, although larger, repeated-session studies are needed before clinical use.

(1) Background: Markerless motion capture may enable scalable biomechanical assessment outside laboratory environments, yet its applicability to tasks involving elevated foot contacts and rapid loading transitions remains insufficiently characterized. This feasibility study evaluated OpenCap, an open-source smartphone-based markerless system, for estimating lower-limb kinematics and ground reaction forces during stair ascent, stair descent, squat, sit-to-stand, and forward lunge. (2) Methods: One healthy adult completed three repetitions of each task while data were acquired simultaneously with three smartphones, an eight-camera Vicon system, and two force plates. OpenCap/OpenSim outputs were adapted for stair and lunge kinetics and compared with marker-based kinematics and force-plate measurements using root mean square error, Pearson correlation, and cosine similarity. (3) Results Agreement was strongest for sagittal-plane lower-limb kinematics, particularly knee flexion-extension (RMSE: 1.4-10.5 deg; Pearson: 0.94-0.999). Larger discrepancies occurred for hip internal-external rotation, pelvis motion during sit-to-stand, and ankle flexion-extension. Resultant GRF waveforms showed high similarity to force-plate data (cosine similarity >=0.988; RMSE: 0.069-0.167 body weight). Stair descent produced the largest deviations and variability, particularly for single-limb forces. (4) Conclusions These findings support the technical feasibility of smartphone-based markerless functional biomechanics while highlighting the need for improved multiplanar pose estimation, contact modeling, and larger validation studies.