Abhishek Dasgupta

Abhishek Dasgupta, Rahul Sharma, Challenger Mishra et al.

Marker-based Optical Motion Capture (OMC) systems and associated musculoskeletal (MSK) modelling predictions offer non-invasively obtainable insights into in vivo joint and muscle loading, aiding clinical decision-making. However, an OMC system is lab-based, expensive, and requires a line of sight. Inertial Motion Capture (IMC) systems are widely-used alternatives, which are portable, user-friendly, and relatively low-cost, although with lesser accuracy. Irrespective of the choice of motion capture technique, one needs to use an MSK model to obtain the kinematic and kinetic outputs, which is a computationally expensive tool increasingly well approximated by machine learning (ML) methods. Here, we present an ML approach to map experimentally recorded IMC data to the human upper-extremity MSK model outputs computed from ('gold standard') OMC input data. Essentially, we aim to predict higher-quality MSK outputs from the much easier-to-obtain IMC data. We use OMC and IMC data simultaneously collected for the same subjects to train different ML architectures that predict OMC-driven MSK outputs from IMC measurements. In particular, we employed various neural network (NN) architectures, such as Feed-Forward Neural Networks (FFNNs) and Recurrent Neural Networks (RNNs) (vanilla, Long Short-Term Memory, and Gated Recurrent Unit) and searched for the best-fit model through an exhaustive search in the hyperparameters space in both subject-exposed (SE) & subject-naive (SN) settings. We observed a comparable performance for both FFNN & RNN models, which have a high degree of agreement (ravg, SE, FFNN = 0.90+/-0.19, ravg, SE, RNN = 0.89+/-0.17, ravg, SN, FFNN = 0.84+/-0.23, & ravg, SN, RNN = 0.78+/-0.23) with the desired OMC-driven MSK estimates for held-out test data. Mapping IMC inputs to OMC-driven MSK outputs using ML models could be instrumental in transitioning MSK modelling from 'lab to field'.

Ikechukwu Ogbonna, Lesley Davidson, Soumya Banerjee et al.

Millions of people in African countries face barriers to accessing healthcare due to language and literacy gaps. This research tackles this challenge by transforming complex medical documents -- in this case, prosthetic device user manuals -- into accessible formats for underserved populations. This case study in cross-cultural translation is particularly pertinent/relevant for communities that receive donated prosthetic devices but may not receive the accompanying user documentation. Or, if available online, may only be available in formats (e.g., language and readability) that are inaccessible to local populations (e.g., English-language, high resource settings/cultural context). The approach is demonstrated using the widely spoken Pidgin dialect, but our open-source framework has been designed to enable rapid and easy extension to other languages/dialects. This work presents an AI-powered framework designed to process and translate complex medical documents, e.g., user manuals for prosthetic devices, into marginalised languages. The system enables users -- such as healthcare workers or patients -- to upload English-language medical equipment manuals, pose questions in their native language, and receive accurate, localised answers in real time. Technically, the system integrates a Retrieval-Augmented Generation (RAG) pipeline for processing and semantic understanding of the uploaded manuals. It then employs advanced Natural Language Processing (NLP) models for generative question-answering and multilingual translation. Beyond simple translation, it ensures accessibility to device instructions, treatment protocols, and safety information, empowering patients and clinicians to make informed healthcare decisions.

Abhishek Dasgupta, Samson Abramsky

Anytime inference is inference performed incrementally, with the accuracy of the inference being controlled by a tunable parameter, usually time. Such anytime inference algorithms are also usually interruptible, gradually converging to the exact inference value until terminated. While anytime inference algorithms for specific domains like probability potentials exist in the literature, our objective in this article is to obtain an anytime inference algorithm which is sufficiently generic to cover a wide range of domains. For this we utilise the theory of generic inference as a basis for constructing an anytime inference algorithm, and in particular, extending work done on ordered valuation algebras. The novel contribution of this work is the construction of anytime algorithms in a generic framework, which automatically gives us instantiations in various useful domains. We also show how to apply this generic framework for anytime inference in semiring induced valuation algebras, an important subclass of valuation algebras, which includes instances like probability potentials, disjunctive normal forms and distributive lattices. Keywords: Approximation; Anytime algorithms; Resource-bounded computation; Generic inference; Valuation algebras; Local computation; Binary join trees.