Improving the quality of work for human beings is receiving a lot of attention from multiple research communities. In particular, digital transformation in human factors and ergonomics is going to empower the next generation of the socio-technical workforce. The use of wearable sensors, collaborative robots, and exoskeletons, coupled with novel technologies for the real-time assessment of human ergonomy forms the crux of this digital transformation. In this direction, this paper focuses on the open problem of estimating the interaction wrench experienced at the human extremities (such as hands), where the feasibility of direct sensor measurements is not practical. We refer to our approach as non-collocated wrench estimation, as we aim to estimate the wrench at known contact locations but without using any direct force-torque sensor measurements at these known locations. We achieve this by extending the formulation of stochastic inverse dynamics for humans by considering a centroidal dynamics constraint to perform a reliable non-collocated estimation of interaction wrench and the joint torques (articular stress) experienced as a direct consequence of the interaction. Our approach of non-collocated estimation is thoroughly validated in terms of payload estimation and articular stress estimation through validation and experimental scenarios involving dynamic human motions like walking.