A ground vibration signal-based human body fall detection method and human body fall detection system, the method comprising: three geophones collecting ground vibration signals (S1); performing filtering noise reduction and endpoint segmentation processing on the collected vibration signals (S2); performing an alignment processing on the vibration signals after endpoint segmentation (S3); performing signal feature extraction on the aligned vibration signals (S4); grouping the extracted feature signals into a training set and training to obtain a hidden Markov model (S5); using ground vibration signals during actual use as test data to detect whether the test data are valid vibration signals, and then processing the test data by using a method of processing training data (S6); on the basis of the hidden Markov model, calculating the probability that the test data are fall signals, and if the probability is greater than a threshold, entering a second confirmation stage, otherwise directly determining that the test data are not related to a fall (S7); by means of an EoA positioning algorithm, calculating the displacement of a target within a short period of time after a suspected fall, and if the movement distance is less than a threshold, a fall is confirmed for a second time, otherwise determining that a fall has not occurred (S8). The described detection method uses the hidden Markov model to calculate the probability of vibration signals being related to a fall, and then uses an EoA positioning algorithm to perform second confirmation, thus fall recognition is accurate and the rate of false alarms is low. In addition, a worn device is not required, privacy is protected and user experience is improved.
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