Background. Peripheral neuropathy is regarded as one of the leading causes of fatal and nonfatal falls. Wearable sensors, due to their increasing availability and flexibility of setting and space, are used widely to obtain wearer's kinematic data to analyze one's balance capacities for the evaluation of risk of fall. There is yet to have a review study focusing on the application of wearable sensors in the scope of fall risk for patients with peripheral neuropathy. Objective. To investigate the methods by which researchers adopt to assess risk of fall in peripheral neuropathy patients and potentially shed light on future researches. Methods. A systematic review design was used to identify articles on fall risk assessment and balance training using wearable sensors in patients with peripheral neuropathy. The study is aimed at extracting the following information: the type of sensors, the type of signal and data processing employed, the scales and tests used in the study, and the type of application. Results. We identified 351 studies, from which 8 were included. An average sample size of 35.6 patients enrolled the studies. The accelerometer was the most common wearable sensor used. 10-meter walk test was the preferable procedure for assessing risk of fall. Conclusion. This review examined several key components in studies on assessing and improving the risk of fall using wearable sensors. We identified the preferred functional test (10-meter walk test), sensor technology (accelerometer), locations (torso and lower legs), and fall risk improvement methods (prostheses). However, due to the limited number of articles specializing in this field of research, a consensus on patient sample size and procedures is not reached. We would recommend future researches to examine more parameters and adopt a fusion sensor setup.
CITATION STYLE
Pan, Q., Chen, Y., Ma, X., Wang, C., & Chen, W. (2023). Application of Wearable Technologies in Fall Risk Assessment and Improvement in Patients with Peripheral Neuropathy: A Systematic Review. Journal of Sensors. Hindawi Limited. https://doi.org/10.1155/2023/1746536
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