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The Wireless Surface Electromyographic Technique for Supporting the Assessment of Fall Risk in Diabetic Neuropathic Patients

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Mobile Networks for Biometric Data Analysis

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 392))

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Abstract

The Functional Reach (FR) Test is commonly used in the clinical practice to identify elderly subjects at risk of recurrent falls. This study was designed to provide a useful tool for supporting the assessment of fall risk in diabetic neuropathic (DN) subjects: a non-invasive analysis of muscle activation patterns involved in the FR test using wireless surface electromyography (sEMG). Kinematic analysis was also performed to support results achieved by sEMG technique. A population of diabetic non-neuropathic subjects was considered as control group (CTRL). Kinematics was acquired by a 6-camera Elite optoelectronic system and passive markers were placed according to Davis protocol, with an additional marker placed on dominant wrist. sEMG analysis of Sternocleidomastoideus, Rectus Abdominis, Erectores Spinae at L4 level (L4), Rectus Femoris, Hamstrings (Ham), Tibialis Anterior (TA) and Soleus (Sol) was performed. No significant differences (p-value for evaluation of statistical significance was < 0.05) were detected between groups in the computed kinematics parameters. sEMG analysis shows that in CTRL subjects, TA was identified as the first motor, since it activates before the start of FR movement; its action can be attributed to the anticipatory muscular activity necessary to initiate the body forward displacement. Sol and Ham, followed by L4, act mainly as tonic muscles, opposing the movement and preventing falls. Compared to CTRL group, DN subjects show an anticipatory recruitment of TA. The earlier activation of TA in DN group suggests that this anticipation could be a predictor of a greater propensity of the DN subjects to fall compared to non-neuropathic diabetic subjects.

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Maranesi, E. et al. (2016). The Wireless Surface Electromyographic Technique for Supporting the Assessment of Fall Risk in Diabetic Neuropathic Patients. In: Conti, M., Martínez Madrid, N., Seepold, R., Orcioni, S. (eds) Mobile Networks for Biometric Data Analysis. Lecture Notes in Electrical Engineering, vol 392. Springer, Cham. https://doi.org/10.1007/978-3-319-39700-9_22

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  • DOI: https://doi.org/10.1007/978-3-319-39700-9_22

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-319-39700-9

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