Human Pose Estimation for Explainable Corrective Feedbacks in Office Spaces

In working environments where prolonged sitting is ubiquitous, maintaining correct posture is crucial to alleviating musculoskeletal problems and improving general well-being. This research presents an innovative approach for assessing sitting postures using any camera conveniently accessible to the subject, be it integrated into their personal computer or an external device. The main contribution of our system is to detect key posture points and incorrect postures and provide the final user with personalized feedback and explanations to help them correct their postural alignment. Using a simple architecture i.e. the multilayer perceptron, we succeeded in identifying human postures. Moreover, by evaluating the results obtained from our classification model, we are able to obtain an explanation based on a post-hoc analysis. We observed that the most impactful joints for correct posture are, in order of importance, the wrists, elbows, and shoulders. Inspired by counterfactual explanations, we consequently provide personalized feedback for users with incorrect postures. These outcomes, although preliminary, show that the proposed pipeline is encouraging and can be pursued in future work where increasing the variety of data and improving detection approaches will be predominant. The reproducibility code is available here: https://github.com/GaetanoDibenedetto/Explainable-Corrective-Feedbacks