Design of a Normative sEMG Database for Biometric Comparison in Rehabilitation Research

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Abstract
Electromyography (EMG) is used in a wide range of research fields, such as physiotherapy, ergonomics, and neurorehabilitation. Normative EMG databases play a crucial and significant role in the efficient diagnosis and treatment of neuromuscular disorders. They can rapidly provide information that, although not necessarily diagnostic, can efficiently and effectively guide further diagnostic studies. Quantitative EMG (QEMG) in the upper extremities is an effective diagnostic tool, but there are currently few normative databases available. The absence of fundamental guidelines and established methods for creating normative databases contributes to a significant obstacle in the field of rehabilitation research. This study aims to bridge this gap by designing a dynamic, scalable, consistent, available, and partition-tolerant NoSQL database (DB), in alignment with the Consistency, Availability, and Partition Tolerance (CAP) theorem, to house normative surface EMG (sEMG) values for upper body muscles, primarily for biometric comparison in rehabilitation. The DB encompasses diverse EMG features, both in the time and frequency domains, as well as anthropometric variables, extracted by healthy participants and post-stroke or spinal cord injury patients. The participant selection is based on Greece's average demographic statistics and specific inclusion and exclusion criteria from existing clinical trials. The proposed DB is particularly designed to be continuously updated offering real-time insights, allowing the DB to be an even more valuable resource for researchers and practitioners working in the field.

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