Department of Health Sciences

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Browsing Department of Health Sciences by Author "Anwar, Sohel"

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    Modeling and Simulation of Robotic Palpation to Detect Subsurface Soft Tissue Anomaly for Presurgical Assessment
    (ASME, 2024-08) Bhattacharjee, Abhinaba; Loghmani, M. Terry; Anwar, Sohel; Health Sciences, School of Health and Human Sciences
    Surgical Haptics is an emergent field of research to integrate and advance the sense of robotic touch in laparoscopic tools in robot-assisted minimally invasive surgery. Haptic feedback from the tooltip and soft tissue surface interaction during robotic palpation can be leveraged to detect the texture and contour of subsurface geometry. However, precise force modulation of the robotic palpating probe is necessary to determine stiff inclusions of the anatomy and maneuver successive manipulation tasks during surgery. This paper focuses on investigating the layered deformations associated with different force profiles involved in manipulating the superficial anatomy of soft tissues during dynamic robotic palpation to determine the underlying anomaly. A realistic three-dimensional (3D) cross-sectional soft tissue phantom with anatomical layers and tumor, as an anomaly, is designed, modeled, and analyzed to examine the effects of oriented palpating forces (0–5 N) of a 7 DOF robot arm equipped with a contoured palpation probe. Finite element static structural analysis of oriented robotic palpation on the developed 3D soft tissue phantoms (with and without anomaly) reveals the soft tissue layer deformations and associated strains needed to identify presence of stiffer inclusions or anomaly during Robotic palpation. The finite element analysis study shows that the difference in deformations of soft tissue layers (e.g., underlying myofascial layers) under stiffer inclusions at different force levels can facilitate haptic feedback to acquire information about subsurface tumors. The deformation variations are further compared to assess better palpation orientations for subsurface anomaly detection.
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    Multimodal Sequence Classification of force-based instrumented hand manipulation motions using LSTM-RNN deep learning models
    (IEEE, 2023-10) Bhattacharjee, Abhinaba; Anwar, Sohel; Whitinger, Lexi; Loghmani, M. Terry; Health Sciences, School of Health and Human Sciences
    The advent of mobile ubiquitous computing enabled sensor informatics of human movements to be used in modeling and building deep learning classifiers for cognitive AI. Expanding deep learning approaches for classifying instrumented hand manipulation tasks, especially the art of manual therapy and soft tissue manipulation, can potentially augment practitioner’s performance and enhance fidelity with computer assisted guidelines. This paper introduces a dataset of 3D force profiles and manipulation motion sequences of controlled soft tissue manipulation stroke pattern applications in thoracolumbar, upper thigh and calf regions of a single human subject performed by five experienced manual therapists. The multimodal 3D force, 3D accelerometer and resultant gyro raw data were preprocessed and experimentally fed into a multilayered Long Short-Term Memory (LSTM) based Recurrent Neural Network (RNN) deep learning model to observe sequence classifications of two manipulation motion techniques (Linear "Strumming" motion and curvilinear "J-Stroke" arched motion) of manual therapy performed using a handheld, localizing Quantifiable Soft Tissue Manipulation (QSTM) medical tool. Each of these motion sequences were further labeled with corresponding best practice technique from validated video tapes and reclassified into "Correct" and "Incorrect" practice based on defined criteria. The deep learning model resulted in 90-95% classification accuracy for individual intra-therapist reduced dataset. The classification accuracy varied between 78%-93% range, when trained with multivariate characteristic feature set combinations for the complete spectrum of inter-therapist dataset.Clinical Relevance — AI informed online therapeutic guidelines can be leveraged to minimize practice inconsistencies, optimize educational training of therapy using data informed protocols, and study progression of pain and healing towards advancing manual therapy.