Abstract
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.
Highlights
D ESPITE the recent concerns of automation, the significance of human beings as an integral part of the future socio-technical workforce is being validated through several studies [1] [2]
An interesting open problem we consider in this work is to estimate the external interaction wrench experienced at human extremities such as hands while manipulating a payload
An element in the configuration space is denoted by q = ∈ Q, which consists of pose of the base frame qB = (I pB, I RB) ∈ R3 ×SO(3) where I pB ∈ R3 denotes the position of the base frame with respect to the inertial frame; IRB ∈ SO(3) denotes the rotation matrix representing the orientation of the base frame with respect to the inertial frame; and the joint positions vector s ∈ Rn which captures the topology, i.e., the internal configuration of the system
Summary
D ESPITE the recent concerns of automation, the significance of human beings as an integral part of the future socio-technical workforce is being validated through several studies [1] [2]. In this work we assume the contact locations to be known, and propose a generalized approach to estimate the external wrench at these known contact locations that are not equipped with any sensors to get direct force-torque measurements. We present the problem formulation as an extension of the stochastic human inverse dynamics method [24] through a two-step approach where the estimation of external wrenches on the links is decoupled from the estimation of internal wrenches exchanged through the joints of the human that results in articular stress. Systematic description of stochastic whole-body inverse dynamics problem formulation and its limitation for human articular stress estimation under payloads.
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