{"id":1648,"date":"2020-10-14T11:23:56","date_gmt":"2020-10-14T16:23:56","guid":{"rendered":"https:\/\/sites.bu.edu\/nrl\/?page_id=1648"},"modified":"2022-08-15T20:09:17","modified_gmt":"2022-08-16T00:09:17","slug":"propulsion-diagnostics","status":"publish","type":"page","link":"https:\/\/sites.bu.edu\/nrl\/patient-connect\/propulsion-diagnostics\/","title":{"rendered":""},"content":{"rendered":"

Digital movement phenotypes for precision rehabilitation<\/span><\/strong><\/span><\/h4>\n

Among people with neurological injuries and diseases, <\/span>different impairment patterns converg<\/span>e<\/span> on a characteristically slow, asymmetric, and unstable gait. <\/span>We seek to advance principles of p<\/i><\/span>recision <\/span><\/i>m<\/span><\/i>edicine <\/span><\/i>to the field of gait rehabilitation<\/span>. <\/span>T<\/span>his <\/span>require<\/span>s<\/span> not only the development of <\/span>targeted gait interventions<\/span>, but also the<\/span> <\/span>advance of novel diagnostic methods that can efficiently <\/span>match individual patients to those interventions. <\/span>B<\/span>efore precision medicine is attainable in the field of gait rehabilitation, clinically-accessible classification methods that reflect patients\u2019 underlying gait impairments are required. Indeed, the most common classification approach used post-stroke is based on walking speed, which is not indicative of underlying gait impairments.\u00a0<\/span><\/p>\n

Though clinic-based measurement devices such as the instrumented GaitRite<\/span>TM<\/span><\/sup> <\/span>have allowed the few rehabilitation clinicians with access to these systems to objectively measure some gait quality measures \u2014 such as inter-limb asymmetry in the spatiotemporal determinants of walking speed \u2014 these measurements have had limited utility in guiding prescription and delivery of targeted therapies designed to act on specific gait deficits. More useful measurements of gait quality have historically been limited to motion laboratories, severely limiting the clinical translation of decades of biomechanical insights. <\/span><\/p>\n

I<\/i><\/span>nertial <\/span><\/i>M<\/span><\/i>easurement <\/span><\/i>U<\/span><\/i>nit<\/span><\/i>s<\/span><\/i> (IMUs)<\/span><\/i> offer a promising solution for this measurement gap<\/span><\/i>. <\/span><\/i>IMUs can collect high-fidelity movement data continuously in the background of everyday walking activities and are already widely utilized to track step activity in both <\/span>clinical practice <\/span>and research. <\/span>For this project, <\/span>we <\/span>are developing novel movement estimation algorithms using<\/span> minimal IMU<\/span> sets. Our goal is the advance of IMU-based classification<\/span> of <\/span>walking impairment<\/span> based on <\/span>both walking speed and<\/span> gait quality. Because IMUs are inexpensive and can be used easily across both clinical and free-living settings, IMU-based locomotor phenotyping is highly scalable and has the potential to address the measurement gaps that have hindered progress in the field of gait rehabilitation. \u00a0\u00a0<\/span><\/p>\n

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Project Lead<\/u><\/span><\/h4>\n\t
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