tremorsensing

Tremor sensing refers to the detection, measurement, and analysis of tremors—repetitive, involuntary motions or small ground vibrations—using sensors and signal-processing methods. The term covers both geophysical tremors, such as ambient seismic noise and earthquakes, and biological tremors, including motor symptoms observed in movement disorders. In geophysics, tremor sensing relies on seismometers, accelerometers (including MEMS devices), geophones, and GNSS receivers to capture ground motion across wide frequency ranges. Data are processed with time–frequency analyses, spectral estimation, and network-based localization to estimate source characteristics, such as location, depth, and energy, and to characterize soil or site effects through microtremor studies.

In biomedical and clinical contexts, tremor sensing uses inertial sensors, electromyography, and motion capture to quantify limb tremor amplitude, frequency, and variability. These measurements support neurological assessment, monitoring of disease progression, and evaluation of treatment response. In engineering and robotics, tremor sensing informs vibration monitoring, fatigue analysis, and active suppression systems that reduce sensorimotor tremor or counteract unintended motions.

Key challenges include handling noisy, non-stationary signals, differentiating tremor from other motion sources, calibrating sensors across platforms, and integrating multimodal data for robust interpretation. Emerging trends involve low-cost MEMS networks, dense seismic arrays, and machine-learning approaches that improve source discrimination and automatic tremor classification.

Overall, tremor sensing is an interdisciplinary field that supports natural disaster risk reduction, infrastructure resilience, and clinical assessment by transforming subtle motions into actionable information.