FOURTH GENERATION WIRELESS REFLEX QUANTIFICATION SYSTEM FOR ACQUIRING TENDON REFLEX RESPONSE AND LATENCY

Abstract

An intrinsic aspect of the standard neurological examination is the deep tendon reflex. A clinician is tasked with qualitatively evaluating reflex parameters, such as reflex response and latency. The tendon reflex is capable of providing preliminary insight with respect to dysfunction of the central and peripheral nervous systems. The qualitative assessment of the tendon reflex can be classified through the implementation of an ordinal scale, such as the NINDS scale which spans five ordinal components from 0 to 4. The reliability and accuracy of the ordinal-scale method for classifying reflex characteristics have been demonstrated to be an issue of controversy. Ordinal scales lack the capacity to properly classify the temporal features of the tendon reflex. Electrodiagnostic testing traditionally provides higher fidelity evaluation of peripheral neuropathy; however, a study by Cocito et al., has discovered 28% of the prescriptions were inappropriate. The fourth-generation wireless reflex quantification system provides a less resource intensive, highly accurate, reliable, and reproducible alternative. The patellar tendon reflex is evoked through a predetermined potential energy derived swing arm attached to a standard reflex hammer. Tandem wireless 3D MEMS accelerometers quantify reflex response and latency. The reflex response maximum and minimum are acquired from the wireless 3D MEMS accelerometer positioned above the ankle joint. The latencies derived from the maximum and minimum of the reflex responses are derived from the temporal disparity relative to the acceleration waveforms of the reflex response and swing arm evoking the tendon reflex. The fourth-generation wireless reflex quantification system has been evolved with a more user-convenient wirelessly activated datalogger mode, which is subsequently downloaded to a local PC wirelessly. The wireless datalogger mode enables sampling at a greater rate relative to the real-time streaming data mode. An automated MATLAB software program is implemented for acquiring reflex parameters. Enclosed is the longitudinal study of the fourth-generation wireless reflex quantification system that demonstrates considerable precision for accuracy, reliability, and reproducibility. As a supplement to the research, a brief reflex modulation study is amended to the longitudinal study.