Fall-down detection and alarm system based on Naive Bayes algorithm and method thereof
A Bayesian algorithm and alarm system technology, applied in the field of electronic detection, can solve the problems of high false alarm rate, prone to false alarms, single detection method, etc., and achieve the effects of high accuracy, error avoidance, and low false alarm rate
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Embodiment 1
[0062] Embodiment 1: as Figure 1-2 , 5, the present invention provides a fall detection and alarm system based on naive Bayesian algorithm, comprising: acquisition module 1, processing module 2, transmission module 3, identification module 4, judgment module 5 and notification module 6, wherein: Identification module 4, judgment module 5 and notification module 6 constitute monitoring terminal, identification module 4, judgment module 5, notification module 6 are connected successively; The short message of the user's location information is used for alarm function, and the monitoring terminal can be a smart phone.
[0063] Acquisition module 1 includes a three-axis acceleration sensor and a three-axis gyroscope, the three-axis acceleration sensor and the three-axis gyroscope are installed on the upper torso of the human body, and the three-axis acceleration sensor and the three-axis gyroscope are respectively Real-time acquisition of the three-dimensional acceleration a of ...
Embodiment 2
[0088] Embodiment 2: as Figure 3-5 As shown, the present invention also provides a fall detection and alarm method based on naive Bayesian algorithm, including:
[0089] Step 1. The three-axis acceleration sensor and the three-axis gyroscope collect the three-dimensional acceleration a of the upper torso in human activities in real time at a sampling frequency of 100 times per second. x 、a y 、a z Data and three-dimensional angular velocity ω x , ω y , ω z data; where: a x is the acceleration along the x-axis direction, a y is the acceleration along the y-axis direction, a z is the acceleration along the z-axis direction, ω x is the angular velocity along the x-axis, ω y is the angular velocity along the y-axis direction, ω z is the angular velocity along the z-axis, such as Figure 5 shown.
[0090] Step 2, the microprocessor calculates combined acceleration a and combined angular velocity ω, wherein:
[0091] Step 3, the bluetooth device transmits the combin...
Embodiment 3
[0110] Embodiment 3: The specific principle of the fall detection and alarm system and method based on the naive Bayesian algorithm disclosed by the present invention is: since the actions of the human body such as falling, squatting, and sitting generally do not exceed 2 seconds from the beginning to the end, the selected The waveform of the combined acceleration and combined angular velocity within 2 seconds of the human body falling, squatting, sitting down, etc. constitutes a training sample (that is, each training sample contains two waveforms of the combined acceleration and combined angular velocity). Data waveforms are compared for fall detection.
[0111] The significance of the sliding window in the present invention is that the computer cannot process infinite and continuous data, so it is necessary to collect waveforms within 2 seconds for detection according to the sampling frequency. Since the sampling time is fixed at 2 seconds, the length of the sliding window ...
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