Detecting gait abnormalities and predicting future disease risk in elderly individuals in real-world conditions is a complex task that requires a well-designed, multi-modal pipeline—from data acquisition to deployment. Here's a comprehensive technical overview:

To begin with, input modalities must balance accuracy and feasibility. RGB cameras alone often suffer from occlusions, lighting issues, and perspective distortion. A combination of RGB + depth sensors (e.g., Kinect v2, RealSense) or RGB + wearable IMUs can improve robustness. Public datasets (such as Gait in Aging, TUG, or UP-Fall) are useful for pretraining or benchmarking, but custom data collection is usually necessary to reflect the variability of real-world gait (e.g., different walking aids, environments, and physical limitations).

In terms of pose estimation, 3D pose estimation is preferable for clinical relevance, as it allows accurate modeling of balance, stability, and joint range of motion. Models like VIBE, VideoPose3D, or METRO can provide reliable joint localization over time. For lightweight setups, 2D models such as OpenPose or MediaPipe may suffice, but lack sufficient detail for tasks like COM analysis or fall-risk estimation. Consistent joint tracking across frames can be achieved with Kalman filters, optical flow-based linking, or multi-view fusion.

Once joint trajectories are available, feature extraction focuses on spatiotemporal parameters such as stride length, cadence, gait symmetry, and double support time. Balance-related features like center of mass sway, trunk rotation, and step width variability are also key indicators. These features are best extracted as time series, not static snapshots, and should be normalized for individual height, gait speed, and walking conditions.

The machine learning approach depends on the goal. If the objective is binary classification ("normal" vs. "abnormal"), models like Random Forests or SVMs can work with handcrafted features. However, for more granular or longitudinal predictions (e.g., fall risk score, Parkinsonian gait index), deep learning models such as CNN-LSTMs, GRUs, or Graph Neural Networks (with joints as nodes) are better suited. Clinical conditions often require multi-label classification and integration with demographic or medical history data (e.g., age, BMI, comorbidities). Model performance should be evaluated using ROC-AUC, F1-score, and clinical interpretability, not just accuracy.

For disease risk prediction, there's solid clinical literature linking specific gait deviations to disorders like Parkinson’s disease, dementia, arthritis, and stroke recovery. Combining gait features with contextual metadata (medication, prior fall history, cognitive tests) significantly improves predictive performance.

Regarding data handling, all raw video and extracted features must be securely stored, ideally using encrypted cloud platforms or local edge devices with anonymization layers. Real-time analysis is useful for clinical environments (e.g., hallway cameras), while batch processing is acceptable for home-monitoring or research applications.

For deployment, the system should offer an intuitive interface for clinicians or caregivers, with visualizations of gait cycles, risk levels, and anomaly heatmaps. Offline functionality may be needed in rural or low-resource settings. The design must account for usability by non-technical users.

Finally, validation must include clinical expert review, ideally using standardized gait assessment tools (e.g., TUG test) and external dataset generalization. Ethically, informed consent, privacy-preserving design, and clear classification of the system (assistive vs diagnostic) are critical for legal compliance and clinical acceptance. Explainable AI models are recommended when used in diagnostic decision-making.

Overall, the most effective systems combine multi-modal sensing, accurate 3D pose estimation, time-aware modeling, and clinically grounded evaluation—while maintaining respect for patient privacy and usability in real-world conditions.