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Orthopaedic Proceedings
Vol. 105-B, Issue SUPP_16 | Pages 34 - 34
17 Nov 2023
Elliott M Rodrigues R Hamilton R Postans N Metcalfe A Jones R McGregor A Arvanitis T Holt C
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Abstract

Objectives

Biomechanics is an essential form of measurement in the understanding of the development and progression of osteoarthritis (OA). However, the number of participants in biomechanical studies are often small and there is limited ways to share or combine data from across institutions or studies. This is essential for applying modern machine learning methods, where large, complex datasets can be used to identify patterns in the data. Using these data-driven approaches, it could be possible to better predict the optimal interventions for patients at an early stage, potentially avoiding pain and inappropriate surgery or rehabilitation. In this project we developed a prototype database platform for combining and sharing biomechanics datasets. The database includes methods for importing and standardising data and associated variables, to create a seamless, searchable combined dataset of both healthy and knee OA biomechanics.

Methods

Data was curated through calls to members of the OATech Network+ (https://www.oatechnetwork.org/). The requirements were 3D motion capture data from previous studies that related to analysing the biomechanics of knee OA, including participants with OA at any stage of progression plus healthy controls. As a minimum we required kinematic data of the lower limbs, plus associated kinetic data (i.e. ground reaction forces). Any additional, complementary data such as EMG could also be provided. Relevant ethical approvals had to be in place that allowed re-use of the data for other research purposes. The datasets were uploaded to a University hosted cloud platform. The database platform was developed using Javascript and hosted on a Windows server, located and managed within the department.


Orthopaedic Proceedings
Vol. 105-B, Issue SUPP_8 | Pages 20 - 20
11 Apr 2023
Hamilton R Holt C Hamilton D Garcia A Graham C Jones R Shilabeer D Kuiper J Sparkes V Khot S Mason D
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Mechanical loading of joints with osteoarthritis (OA) results in pain-related functional impairment, altered joint mechanics and physiological nociceptor interactions leading to an experience of pain. However, the current tools to measure this are largely patient reported subjective impressions of a nociceptive impact. A direct measure of nociception may offer a more objective indicator. Specifically, movement-induced physiological responses to nociception may offer a useful way to monitor knee OA. In this study, we gathered preliminary data on healthy volunteers to analyse whether integrated biomechanical and physiological sensor datasets could display linked and quantifiable information to a nociceptive stimulus.

Following ethical approval, 15 healthy volunteers completed 5 movement and stationary activities in 2 conditions; a control setting and then repeated with an applied quantified thermal pain stimulus to their right knee. An inertial measurement unit (IMU) and an electromyography (EMG) lower body marker set were tested and integrated with ground reaction force (GRF) data collection. Galvanic skin response electrodes for skin temperature and conductivity and photoplethysmography (PPG) sensors were manually timestamped to the integrated system.

Pilot data showed EMG, GRF and IMU fluctuations within 0.5 seconds of each other in response to a thermal trigger. Preliminary analysis on the 15 participants tested has shown skin conductance, PPG, EMG, GRFs, joint angles and kinematics with varying increases and fluctuations during the thermal condition in comparison to the control condition.

Preliminary results suggest physiological and biomechanical data outputs can be linked and identified in response to a defined nociceptive stimulus. Study data is currently founded on healthy volunteers as a proof-of-concept. Further exploratory statistical and sensor readout pattern analysis, alongside early and late-stage OA patient data collection, can provide the information for potential development of wearable nociceptive sensors to measure disease progression and treatment effectiveness.


Orthopaedic Proceedings
Vol. 103-B, Issue SUPP_16 | Pages 64 - 64
1 Dec 2021
Hamilton R Holt C Hamilton D Jones R Shillabeer D Kuiper JH Sparkes V Mason D
Full Access

Abstract

Objectives

Current tools to measure pain are broadly subjective impressions of the impact of the nociceptive impulse felt by the patient. A direct measure of nociception may offer a more objective indicator. Specifically, movement-induced physiological responses to nociception may offer a useful way to monitor knee OA. In this proof-of-concept study, we evaluated whether integrated biomechanical and physiological sensor datasets could display linked and quantifiable information to a nociceptive stimulus.

Method

Following ethical approval, we applied a quantified thermal pain stimulus to a volunteer during stationary standing in a gait lab setting. An inertial measurement unit (IMU) and an electromyography (EMG) lower body marker set were tested and integrated with ground reaction force (GRF) data collection. Galvanic skin response electrodes and skin thermal sensors were manually timestamp linked to the integrated system.