Date of Award

5-1-2023

Degree Type

DPT Project

Degree Name

Doctor of Physical Therapy (DPT)

Advisor

Michelle L. Gutierrez

Abstract

Purpose/hypothesis: The purpose of this study is to assess the efficacy of the Smart Wireless Motion Sensors (SWMS) in detecting deviations or abnormalities in gait between normative and traumatic brain injury (TBI) data.

Subjects: We recruited participants from the faculty and students in the University of Texas at El Paso (UTEP) Doctor of Physical Therapy (DPT) program. Inclusion criteria for participation in the study consisted of (a) current faculty and staff in UTEP’s DPT program, (b) 18 years old or older, (c) ambulatory, (d) no history of TBI or any other condition affecting gait and (e) English speaking. Participants were excluded if they (a) were less than 18 years old, (b) non-ambulatory, (c) if they had a history of TBI or any other condition affecting gait, and (d) were non-English speaking.

Materials/methods: We employed a cross-sectional design. Sensors were placed on bilateral upper and lower extremities at upper arm, forearm, thigh, shank, and foot for a total of ten sensors. Patients were instructed to perform four normative gait tasks such as ambulation on level surface, and five pathologic gait tasks such as a diplegic. Data collection software was used to analyze normative and pathologic gait patterns.

Results: Convolutional neural network statistical analysis was utilized to determine accuracy of the wearable technology utilized in this study. Preliminary data suggests that the wearable technology had moderate to high accuracy in identifying variance among the different gait patterns studied. The analysis found the least accuracy (0.70) in comparing a Trendelenburg gait with a right hemiplegic gait. The highest accuracy (1.0) was found in comparing a dual-task gait with a Parkinsonian gait. The remainder of the differentiation couplings had an accuracy of either 0.80 or 0.90.

Conclusions: We conclude that the proprietary wearable technology examined in this study is sufficiently accurate in differentiating between normative and pathologic gait patterns. Further refinement of the technology and associated software is needed in order for this technology to be efficiently utilized in a clinical setting.

Clinical relevance: The results of this research have the potential to determine whether or not these sensors could be useful and efficient in a clinical setting in order to detect gait abnormalities in individuals who have experienced minor TBI. These sensors have the potential to be a more cost-effective and accessible clinical tool to provide early detection of minor TBIs.

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