In-sensor impact strength estimation

Abstract

Embodiments of the present disclosure relate to in-sensor impact strength estimation. In accordance with an embodiment, there is provided a sensor for detecting an impact event, the sensor including a machine learning core (MLC) and a finite state machine (FSM) circuit. The MLC continuously calculates a value based on a change in velocity. The FSM circuit compares the value to a first threshold and generates a first interrupt if the value is greater than the first threshold. The FSM circuit then compares the value to a second threshold that is less than the first threshold and, after the first interrupt, generates a second interrupt if the value is less than or equal to the second threshold. The MLC calculates and stores a maximum value between the first interrupt and the second interrupt in a register, the maximum value being read after an application processor of the host device receives the second interrupt. The maximum acceleration norm value is reset after a delay after the second interrupt is generated.

Description

Technical Field

[0001] The present disclosure relates generally to sensors and, in particular embodiments, to shock intensity estimation. Background Art

[0002] An accelerometer is a sensor that measures acceleration forces along multiple axes. It can detect changes in velocity and movement in any direction relative to its orientation. Microelectromechanical systems (MEMS) accelerometers are commonly used in portable electronic devices due to their small size and low power consumption.

[0003] Accelerometers can detect shock measurements (i.e., assess impact) by measuring sudden acceleration forces acting on the electronic device housing the accelerometer. These forces are converted into electrical signals that are typically processed and analyzed by a processor external to the sensor or an onboard computer. A threshold or trigger level is set to differentiate between normal movement and impact events. If the measured acceleration exceeds this threshold, a significant impact or shock event can be indicated. The threshold level can be adjusted based on the desired sensitivity and application requirements.

[0004] Once an impact event is detected, the occurrence of the impact and associated data, such as the magnitude and duration of the impact, can be analyzed. Depending on the function of the electronic device, it can trigger a specific response, such as activating a protection mechanism, signaling an alarm, displaying a warning, or notifying the user. Advanced algorithms can analyze the recorded impact data to assess the severity or type of impact. By analyzing the acceleration pattern and duration, different types of impacts can be distinguished, such as the impact of a device after a fall, the impact of a human body on the ground, or a collision.

[0005] For example, in a smartwatch equipped with a fall detection feature, an accelerometer can identify when a user falls. If a significant impact is detected, the smartwatch can initiate an alarm to notify emergency contacts or trigger an automated emergency response.

[0006] Conventionally, the processor of the host device must be always on to continuously monitor for the occurrence of shock events. It would be advantageous to reduce the workload on the host microcontroller, minimize system power consumption, and reduce the latency in measuring shock intensity. Summary of the invention

[0007] Technical advantages are generally achieved by embodiments of the present disclosure, which describe methods, circuits, and systems for shock intensity estimation.

[0008] A first aspect relates to a sensor for detecting an impact event. The sensor includes a machine learning core configured to continuously calculate an acceleration norm value based on a change in velocity of an accelerometer along one or more axes. The sensor additionally includes a finite state machine circuit configured to compare the continuously calculated acceleration norm value with a first threshold value, and in response to the continuously calculated acceleration norm value being greater than the first threshold value, generate a first interrupt signal. The finite state machine circuit is also configured to compare the continuously calculated acceleration norm value with a second threshold value, and in response to the continuously calculated acceleration norm value being less than or equal to the second threshold value after the first interrupt signal is generated, generate a second interrupt signal, the second threshold value being less than the first threshold value. The machine learning core is also configured to calculate a maximum acceleration norm value between the first interrupt signal and the second interrupt signal, and store the maximum acceleration norm value in a register. An application processor of a host device reads the maximum acceleration norm value after receiving the second interrupt signal from the sensor. The maximum acceleration norm value is reset after a delay after the second interrupt signal is generated.

[0009] A second aspect relates to a system including a sensor. The sensor includes a machine learning core configured to continuously calculate an acceleration norm value based on a change in velocity of an accelerometer along one or more axes. The sensor additionally includes a finite state machine circuit configured to compare the continuously calculated acceleration norm value with a first threshold value, and in response to the continuously calculated acceleration norm value being greater than the first threshold value, generate a first interrupt signal. The finite state machine circuit is also configured to compare the continuously calculated acceleration norm value with a second threshold value, and in response to the continuously calculated acceleration norm value being less than or equal to the second threshold value after the first interrupt signal is generated, generate a second interrupt signal, the second threshold value being less than the first threshold value. The machine learning core is also configured to calculate a maximum acceleration norm value between the first interrupt signal and the second interrupt signal, and store the maximum acceleration norm value in a register. An application processor of the host device reads the maximum acceleration norm value after receiving the second interrupt signal from the sensor. The maximum acceleration norm value is reset after a delay after the second interrupt signal is generated.

[0010] The third aspect relates to a method for detecting an impact event. The method includes: a sensor in a host device continuously measures acceleration values ​​along multiple directions; the sensor continuously calculates acceleration norm values ​​based on the acceleration values; the sensor compares the continuously calculated acceleration norm value with a first threshold value, and generates a first interrupt signal in response to the continuously calculated acceleration norm value being greater than the first threshold value; the sensor compares the continuously calculated acceleration norm value with a second threshold value, and generates a second interrupt signal in response to the continuously calculated acceleration norm value being less than or equal to the second threshold value after the first interrupt signal is generated, and the second threshold value is less than the first threshold value; the sensor calculates a maximum acceleration norm value between the first interrupt signal and the second interrupt signal; the maximum acceleration norm value is stored in a register of the sensor, wherein an application processor of the host device reads the maximum acceleration norm value after receiving the second interrupt signal from the sensor; and after a delay after the second interrupt signal is generated, the maximum acceleration norm value is reset.

[0011] Embodiments may be implemented in hardware, software, or any combination thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] For a more complete understanding of the present disclosure and its advantages, reference is now made to the following description taken in conjunction with the accompanying drawings, in which:

[0013] Figure 1 is a block diagram of an embodiment system;

[0014] Figure 2 Yes, you can Figure 1 A block diagram of an embodiment sensor implemented in a system of;

[0015] Figure 3 is a flow chart of an embodiment method for operating a sensor;

[0016] Figure 4 shows waveforms of example signals; and

[0017] Figure 5 is a state transition diagram of a finite state machine circuit of an embodiment. The finite state machine circuit of an embodiment can be implemented as Figure 1 FSM circuit in the sensor. DETAILED DESCRIPTION

[0018] The present disclosure provides many applicable creative concepts that can be embodied in various specific contexts. The specific embodiments only illustrate specific configurations and do not limit the scope of the claimed embodiments. Unless otherwise stated, features from different embodiments can be combined to form additional embodiments.

[0019] The variation or modification described in one embodiment of the embodiment may also be applied to other embodiments. In addition, various changes, substitutions and modifications may be made without departing from the spirit and scope of the present disclosure as defined by the appended claims.

[0020] Although the inventive aspects are primarily described in the context of sensors in consumer portable electronic devices (such as smartphones or smart watches), it should also be understood that these inventive aspects may also be applicable to sensors housed in any electronic device that would benefit from a low-power, small-footprint impact estimation sensor (such as drones, game controllers, navigation systems). In particular, aspects of the present disclosure may be similarly applicable to, for example, various industrial and vehicular applications.

[0021] Conventionally, shock intensity estimation relies on software calculations by an application processor (or microcontroller) in a host device that is external to the sensor used to measure the shock event. The application processor is typically configured to continuously read data from the accelerometer, calculate the corresponding acceleration norm value, and determine whether a shock event has occurred by calculating the maximum peak value within the shock event. The continuous reading is typically done at a very high sampling rate (e.g., hundreds of hertz) to achieve accurate shock detection. In conventional configurations, system efficiency is reduced as the accelerometer continuously wakes up the application processor.

[0022] Embodiments of the present disclosure provide methods, circuits, and systems for minimizing the activity of a host device by offloading typical processing done by an application processor to a sensor operating in a low power domain.

[0023] Furthermore, embodiments of the present disclosure minimize data latency because the core features related to impact detection measurement and processing are performed within the sensor and do not require round-trip communications between the application processor and the sensor as in conventional systems.

[0024] Various aspects of the present disclosure provide interrupt lines and registers in the sensor that notify an impact event that triggers an application processor. The application processor, in turn, upon receiving the trigger, can access / read the registers to collect impact event data and, if necessary, further process the data (e.g., using a neural network, decision tree, support vector machine, random forest, etc.) to determine whether to trigger a response, such as contacting emergency services in response to a vehicle collision detection.

[0025] Figure 1 1 illustrates a block diagram of an embodiment system 100. The system 100 includes a processor 102, a memory 104, a sensor 106, a power supply unit (PSU) 108, and an interface 110, which may (or may not) be arranged as shown. Figure 1100 is shown in one of each (i.e., processor 102, memory 104, sensor 106, power supply unit 108, and interface 110), but the number of components is not limited, and a larger number is similarly contemplated in other embodiments. System 100 may include additional components not described, such as long-term storage devices (e.g., non-volatile memory, etc.), power management circuit devices, security and encryption modules (e.g., trusted platform module (TPM)), etc. System 100 may be an electronic device, such as a smartphone, a tablet computer, a notebook computer, a smart watch, a vehicle, or any system or subsystem capable of hosting sensor 106.

[0026] In an embodiment, each component can communicate with any other component inside or outside the system 100. For example, each component can use I 2 C (alternatively referred to as I2C or IIC) communication protocol, Serial Peripheral Interface (SPI) specification, etc.

[0027] The processor 102 may be any component or collection of components adapted to perform computations or other processing related tasks. In an embodiment, the processor 102 is an application processor or a microcontroller. Embodiments of the present disclosure provide a solution whereby continuous processing for monitoring impact events is delegated from the processor 102 to the sensor 106. Typically, the processor 102 or certain cores of the processor 102 run continuously to monitor impact events. Therefore, embodiments of the present disclosure advantageously reduce power consumption within the system 100 by transferring the workload from the processor 102 to the low power sensor 106, which operates with high efficiency and low power.

[0028] In an embodiment, the processor 102 receives meaningful data from the sensor 106, such as impact intensity and duration, which may be further processed by the processor 102 to detect, for example, car accidents, fall detection events, or for warranty and insurance purposes.

[0029] The memory 104 may be any component or collection of components suitable for storing programming or instructions for execution by the processor 102. In an embodiment, the memory 104 includes a non-transitory computer-readable medium.

[0030] like Figure 2The sensor 106, further described in , measures the intensity of shocks corresponding to shock events on the system 100. In an embodiment, the sensor 106 continuously monitors for shock events with little or no intervention by the processor 102. In an embodiment, the sensor 106 is configured to detect shock start events and shock end events by comparing values ​​from an accelerometer of the sensor 106 to a threshold, storing a maximum value of an acceleration norm between the shock start event and the shock end event, and disabling shock detection for a period of time before being re-enabled.

[0031] The interface 110 may be any component or collection of components that allows the processor 102 to communicate with other devices / components or a user. For example, the interface 110 may be adapted to allow a user or sensor 106 to interact / communicate with the system 100. In addition, the interface 110 may include circuitry that allows the system 100 to transmit a signal to notify an emergency contact or trigger an automated emergency response.

[0032] Figure 2 A block diagram of an embodiment sensor 200 is illustrated, which can be implemented as sensor 106 in system 100. Sensor 200 includes a finite state machine (FSM) circuit 202, an accelerometer 204, a gyroscope 206, a machine learning core (MLC) 208, a memory 210, and an optional first-in, first-out (FIFO) circuit 212, which may (or may not) be arranged as shown. Sensor 200 may include additional components and sensors not shown, such as an analog-to-digital converter (ADC), a barometer (for measuring atmospheric pressure), or a magnetometer (for measuring magnetic fields). In an embodiment, sensor 200 is a single packaged component within a host device.

[0033] FSM circuit 202 is a digital circuit that implements a finite state machine, which is a mathematical model used to describe the behavior of FSM circuit 202 that can exist in a finite number of states. FSM circuit 202 may include one or more state registers, state transition logic, and output logic, and is based on a state transition table or diagram.

[0034] The FSM circuit 202 is designed to respond to input signals received from the accelerometer 204 and the optional gyroscope 206 and to transition between different states based on predefined rules or conditions. In the FSM circuit 202, the behavior of the circuit is divided into a set of discrete states, and transitions between these states occur in response to inputs. The current state of the FSM (e.g., stored in a state register implemented as a flip-flop or other type of sequential logic element) is determined by the value stored in a memory element (such as a flip-flop or register) within the FSM circuit 202. The input signal and the current state together determine the state of the FSM circuit 202 as described below. Figure 5A state transition table or state transition diagram further described defines the next state of the system (eg, using state transition logic).

[0035] In an embodiment, the FSM circuit 202 is configured to perform a sequence of threshold or timing checks on the data collected from the accelerometer 204 or gyroscope 206 in each state to detect a sequence of movements.

[0036] The accelerometer 204 is a sensor that detects changes in velocity (i.e., acceleration) along multiple axes. In an embodiment, the accelerometer 204 can detect movement in three dimensions: X-axis, Y-axis, and Z-axis, corresponding to forward / backward motion, lateral motion, and vertical motion. The design of the accelerometer 204 can be based on piezoelectric effect, piezoresistance, capacitive sensing, etc. Most accelerometers are tiny, and they are often referred to as micro-electromechanical systems (MEMS) accelerometers.

[0037] In an embodiment, the accelerometer 204 is configured to detect shock or impact at a high sampling rate of 200 Hz, 400 Hz, or 800 Hz (not limited). In an embodiment, the accelerometer 204 may include more than one accelerometer in the sensor 200. For example, the accelerometer 204 may include a first accelerometer and a second accelerometer, the first accelerometer being configured to have dedicated channels for ±2g, ±4g, ±8g, and ±16g full scales, and the second accelerometer being configured to have dedicated channels for ±32g, ±64g, ±128g, and ±256g full scales. In an embodiment, the ±16g channel may detect shock intensity based on an acceleration norm of up to ±27.71g. In an embodiment, the ±256g channel may detect shock intensity based on an acceleration norm of up to ±443.4g. In an embodiment, the accelerometer 204 or a second one of the accelerometers 204 is configured to have a maximum full scale of ±300 g, and can detect the impact intensity based on an acceleration norm up to ±519.6 g.

[0038] Gyroscope 206 is a sensor that measures the rate of rotation or angular velocity around a specific axis. Gyroscopes are generally based on the principles of angular momentum and gyroscopic effect. In an embodiment, gyroscope 206 measures rotational motion along three axes: pitch (tilt up and down), roll (tilt left and right), and yaw (rotation around the vertical axis).

[0039] In an embodiment, the accelerometer 204 and the gyroscope 206 provide analog signals that are converted to digital signals (e.g., using an analog-to-digital converter) for processing to detect, for example, device orientation, trigger safety features such as vehicle airbags, fall detection, or measure impact to a host device that houses the sensor 200.

[0040] In an embodiment, the accelerometer 204 can be used to detect the occurrence of an impact, and the data collected using the accelerometer 204 and the gyroscope 206 can be used alone or in combination to provide a more accurate and precise estimate of the context in which the impact occurred or the characteristics of the impact. For example, accelerometers are prone to errors due to gravity, and gyroscopes drift over time. Combining their outputs can help to compensate for these limitations. The complementary nature of these two sensors allows detection of both linear and rotational movement, providing a more comprehensive understanding of the motion of the host device at the time of impact. This sensor fusion technology is typically used in an inertial measurement unit (IMU) or sensor array to achieve robust motion tracking in various applications.

[0041] The MLC 208 can be used for signal processing, such as filtering raw accelerometer data, which may contain noise and unwanted vibrations. Typically, the signals from the accelerometer 204 and the gyroscope 206 are filtered and processed to obtain accurate measurements. Filtering techniques such as low-pass or high-pass filters can eliminate noise or bias and isolate relevant data. In addition, the MLC 208 can be used for various calculation functions within the sensor 200. For example, the MLC 208 can be used to calculate the maximum value of the norm signal from the accelerometer 204, as described in detail below. In an embodiment, the functions performed by the MLC 208 are implemented in a general-purpose processor or a special-purpose processor (such as a signal processor or a microcontroller).

[0042] In an embodiment, the memory 210 is used to store values ​​used by the sensor 200 to provide data to the processor 102 in the system 100. In an embodiment, the memory 210 may include an element such as a trigger, a register, or other storage element. The memory 210 may store a maximum acceleration norm value corresponding to an impact event. In some embodiments, the memory 210 may include a lookup table or function lookup that converts the acceleration norm value to a calibrated impact intensity value. In some embodiments, the memory 210 is configured to store acceleration values ​​(e.g., x-axis, y-axis, z-axis) used to calculate the maximum acceleration norm value.

[0043] The FIFO circuit 212 is designed to efficiently manage sequential data flow, where the first data element entering the FIFO circuit 212 is first retrieved or processed by, for example, the processor 102 in the system 100. The FIFO circuit 212 can be implemented as known in the art. In an embodiment, the FIFO circuit 212 can be replaced by, for example, a dedicated register within the memory 210 of the sensor 200.

[0044] In an embodiment, the sensor 200 operates autonomously with respect to the system 100 and without any intervention of the processor 102 (i.e., the sensor 200 components do not need to be programmed). In such an embodiment, the processing logic for operating and initializing the components of the sensor 200 (e.g., MLC 208, FSM circuit 202) can be implemented as dedicated hardware components.

[0045] In an embodiment, sensor 200 includes one or more external pads to allow for communication of signals with processor 102 (or any other component within system 100 ). In an embodiment, processor 102 is configured to initialize sensor 200 .

[0046] In an embodiment, the processor 102 operates as a master device and the sensor 200 acts as a secondary device for information transfer. In an embodiment, after the sensor 200 is activated, the processor 102 configures the sensor 200 to, for example, generate data at a specific sampling rate, generate interrupts embedded in the sensor 200, configure additional functions and registers suggested herein by enabling logic (e.g., for impact event detection, intensity calculation, etc.), set thresholds, etc.

[0047] In an embodiment, after the processor 102 initially configures the sensor 200 , communication between the processor 102 and the sensor 200 is limited to responding to triggers initiated by the sensor 200 using interrupt signals from the sensor 200 to the processor 102 .

[0048] In an embodiment, after the sensor 200 reports an interrupt to the processor 102 using an interrupt signal, the processor 102 may access (e.g., read) one or more registers in the sensor 200 to collect data related to the impact event (e.g., gyroscope values, maximum acceleration norm values, impact intensity duration, etc.), and the data related to the impact event is calculated or stored, for example, by the MLC 208.

[0049] In an embodiment, the sensor 200 is configured to generate and transmit to the processor 102 a data packet having various information collected during the detection of an impact event.

[0050] Figure 3 A flow chart of an embodiment method 300 for operating sensor 200 is illustrated. Figure 4 Waveforms of example signals corresponding to method 300 , system 100 , and sensor 200 are illustrated. Figure 5 is a state transition diagram of an embodiment finite state machine circuit 500 , which may be implemented as FSM circuit 202 in sensor 200 .

[0051] The operation of the sensor 200 implemented as the sensor 106 of the system 100 uses Figure 3 Method 300, Figure 4 The waveform and Figure 5 The state transition diagram in is described. Figure 4 Included is a first signal 402 corresponding to the acceleration norm value calculation and an interrupt signal 404 corresponding to an interrupt signal generated by, for example, the FSM circuit 202 and provided to the processor 102 .

[0052] At step 302, the MLC 208 continuously calculates an acceleration norm value based on the data collected from the accelerometer 204. Acceleration norm value calculation refers to determining the magnitude of the acceleration vector based on the data collected by the accelerometer 204. Since acceleration is a vector describing the rate of change of velocity over time, it has both a magnitude (the amount of acceleration) and a direction. The acceleration norm value calculation provides a single value representing the magnitude of the acceleration measurement regardless of direction.

[0053] The acceleration norm can be calculated using the Euclidean norm, also known as L 2 Norm or magnitude of a vector. For a three-dimensional acceleration vector (x, y, z), the Euclidean norm (n) value can be calculated as follows: This involves taking the square of each component of the acceleration vector, summing them, and then taking the square root of the result. In an embodiment, at step 302, the MLC 208 calculates the acceleration norm from three dimensions (x, y, and z) at each time instant (digitized based on the sensor circuitry).

[0054] In some embodiments, the acceleration norm may be calculated using only one or two directions. For example, the accelerometer 204 may be configured to measure acceleration only in the forward / backward directions and the lateral direction. In such an example, the acceleration norm (n) may be calculated by the MLC 208 using the equation to calculate.

[0055] In other embodiments, a software or hardware configurable weight multiplier (ie, coefficient) may emphasize the hierarchical structure in one of the directions used to calculate the modified acceleration norm value. For example, the modified acceleration norm value (n') may be calculated as follows: Where α corresponds to the weight factor for the z direction, β corresponds to the weight factor for the y direction, and γ corresponds to the weight factor for the z direction. Therefore, assigning different values ​​to the weight factors can highlight the measurements in a specific direction to meet the needs of the application. Of course, embodiments in which equal weight factors or zero-valued weight factors have been assigned are also considered.

[0056] In an embodiment, the MLC 208 uses a recursive (i.e., iterative update) feature (i.e., recursive attribute, recursive variable) to calculate the maximum acceleration norm value. In such an embodiment, the maximum acceleration norm value is calculated based on the previous value (i.e., sample by sample); therefore, no attenuation factor is applied to avoid releasing the value and retaining the maximum value.

[0057] Recursive features are often used in time series analysis and sequential data modeling, where the current value of a variable is affected by its past values. These features capture temporal correlations and provide a method for incorporating historical information into the analysis. In an embodiment, the decay factor (i.e., a configurable coefficient) is set by the processor 102 during a startup sequence while configuring the sensor 200.

[0058] In these embodiments, the MLC 208 calculates a maximum value, a minimum value, a peak-to-peak recursive value, or a combination thereof. The maximum recursive value and the minimum recursive value are set equal to the input value when they are above or below the current value. Otherwise, the maximum and minimum values ​​are decayed with a configurable coefficient.

[0059] For example, to calculate the maximum acceleration norm value, if the currently calculated acceleration norm value (in i ) is greater than the previous maximum acceleration norm value (Max i -1), then the current maximum acceleration norm value (Max i ) is equal to the currently calculated acceleration norm value (in i )(i.e., Max i =in i ), and the maximum coefficient (C max ) is set as the initial attenuation coefficient (i.e., C max =c decay ).

[0060] Otherwise, if the currently calculated acceleration norm value (in i ) is less than or equal to the previous maximum acceleration norm value (Max i-1 ), then the current maximum acceleration norm value (Max i ) is equal to the previous maximum acceleration norm value (Max i-1 ) multiplied by the current attenuation coefficient (c max )(i.e., Max i =Max i-1 ×c max , where c max =c decay ×c max ).

[0061] As another example, to calculate the minimum acceleration norm value, if the currently calculated acceleration norm value (in i) is less than the previous minimum acceleration norm value (Min i-1 ), then the current minimum acceleration norm value (Min i ) is equal to the currently calculated acceleration norm value (in i )(i.e., Min i =in i ), and the minimum coefficient (c min ) is set as the initial attenuation coefficient (i.e., C min =c decay ).

[0062] Otherwise, if the currently calculated acceleration norm value (in i ) is greater than or equal to the previous minimum acceleration norm value (Min i-1 ), then the current minimum acceleration norm value (Min i ) is equal to the previous minimum acceleration norm value (Min i-1 ) multiplied by the current attenuation coefficient (c min )(i.e., Min i =Min i-1 ×C min , where c min =c decay ×C min ).

[0063] Peak-to-peak value (Pk2pk i ) can be calculated as the current maximum acceleration norm value (Max i ) and the current minimum acceleration norm value (Min i ) (i.e., Pk2pk i =Max i -Min i ).

[0064] Therefore, it should be understood that the method of calculating the acceleration norm value is not limiting and various methods may be considered based on the application.

[0065] In an embodiment, in addition to or in lieu of the maximum acceleration norm value, other signal features are continuously calculated or extracted from the sensor 200. For example, signal features such as the mean, variance, energy, maximum, minimum, peak-to-peak value of any accelerometer or gyroscope axis or accelerometer or gyroscope norm are continuously calculated or extracted from the sensor 200. In an embodiment, one or more of these values ​​are stored in, for example, a register or memory storage device of the sensor accessible to the processor 102.

[0066] In an embodiment, the MLC 208 filters out noise or bias and isolates the relevant acceleration data for each axis before calculating the acceleration norm value. In some embodiments, the MLC 208 filters out noise or bias and isolates the relevant acceleration data after calculating the acceleration norm value.

[0067] In an embodiment, at step 304, after each acceleration norm value is calculated, the calculated value is compared with the value stored in the memory 210. In an embodiment, in response to the acceleration norm value calculated at step 302 being greater than the value stored in the memory 210, the value in the memory 210 is updated to reflect the maximum acceleration norm value. In an embodiment, the corresponding acceleration value (e.g., x-axis, y-axis, z-axis) for the updated maximum acceleration norm value is stored in the memory 210.

[0068] At step 306, the FSM circuit 202 operates in the first state 502. Between time t0 and t3 or between t5 and t8, the FSM circuit 202 sets the first threshold value (ie, THS HIGH ) is calculated and compared with each acceleration norm value to determine when the acceleration norm exceeds the first threshold. At times t1 and t6, in response to the FSM circuit 202 determining that the acceleration norm value is greater than the first threshold, the FSM circuit 202 asserts a first interrupt by transitioning the value of the interrupt signal 404 from a logic low state to a logic high state within a set amount of time. In addition, the FSM circuit 202 transitions from the first state 502 to the second state 504.

[0069] In an embodiment, a first value (e.g., 0x02) is recorded in a register of the sensor 200 just before the first interrupt is generated. In response to the processor 102 receiving the first interrupt, the processor 102 accesses the register, which includes the first value, indicating to the processor 102 that the trigger from the interrupt signal 404 indicates an impact start event.

[0070] In an embodiment, the values ​​stored in the memory 210 are not updated until a first interrupt is generated at the interrupt signal 404. In such an embodiment, each calculated acceleration norm value is compared to a first threshold value, and only after the calculated value is greater than the first threshold value is the register in the memory 210 updated to reflect the maximum acceleration norm value.

[0071] At step 308, between t1 and t3 or between t6 and t8, the FSM circuit 202 sets the second threshold value (ie, THS LOW ) is compared with each acceleration norm value to determine when the acceleration norm is equal to or less than a second threshold value. The second threshold value is less than the first threshold value.

[0072] In an embodiment, the first threshold and the second threshold are set by processor 102 during a startup sequence while configuring sensor 200. In an embodiment, the first threshold is equal to 20 g, and the second threshold is equal to 19 g.

[0073] In addition, the maximum acceleration norm value stored in the memory 210 continues to be updated after each acceleration norm value calculation when the calculated value exceeds the value stored in the memory 210 (see step 304). Since the maximum acceleration norm value for the first signal 402 occurs at time t2 for a duration between t0 and t3, and occurs at time t7 for a duration between t5 and time t7, the value stored in the memory 210 reflects the acceleration norm value calculated at time t2 and t7, respectively, at each impact event.

[0074] At times t3 and t8, in response to the FSM circuit 202 determining that the acceleration norm value is less than or equal to the second threshold, the FSM circuit 202 asserts a second interrupt by transitioning the value of the interrupt signal 404 from a logic low state to a logic high state within a set amount of time. In addition, the FSM circuit 202 transitions from the second state 504 to the third state 506. In an embodiment, after the second interrupt is generated at the interrupt signal 404, the value stored in the memory 210 is blocked from updating.

[0075] In an embodiment, a second value (e.g., 0x01) is recorded in a register of the sensor 200 that previously stored the first value just before the second interrupt was generated. In response to the processor 102 receiving the second interrupt, the processor 102 accesses the same register, which now includes the second value, indicating to the processor 102 that the trigger from the interrupt signal 404 indicates an end-of-impact event and enables the maximum acceleration norm value to be stored in the memory element of the FIFO circuit 212, as described in detail in step 310.

[0076] In an embodiment, the FSM circuit 202 uses an internal counter (i.e., hardware) to calculate the impact duration corresponding to the duration between the first interrupt and the second interrupt (e.g., the time between t1 and t3 or t6 and t8). In other embodiments, the processor 102 can calculate the duration based on receiving the difference between the first interrupt and the second interrupt in the interrupt signal 404.

[0077] In an embodiment, other signal features are calculated or extracted from the sensor 200 between the generation of the first interrupt and the generation of the second interrupt. For example, signal features such as the mean, variance, energy, maximum, minimum, peak-to-peak value of any accelerometer or gyroscope axis or accelerometer or gyroscope norm are calculated or extracted from the sensor 200 during the period between the first interrupt and the second interrupt.

[0078] At step 310 , in response to receiving the second interrupt signal, the processor 102 enables storing of the maximum acceleration norm value into the memory element of the FIFO circuit 212 , which results in copying the maximum acceleration norm value from the memory 210 to the memory element of the FIFO circuit 212 .

[0079] In an embodiment, additional data is copied from the memory 210 to the memory element of the FIFO circuit 212, such as acceleration values ​​(e.g., x-values, y-values, z-values), gyroscope values, etc., the additional data corresponding to the maximum acceleration norm value. In an embodiment, calculated or extracted signal features, such as the mean, variance, energy, maximum value, minimum value, peak-to-peak value, etc. of any accelerometer or gyroscope axis or accelerometer or gyroscope norm during the time period between the first interrupt and the second interrupt, are copied from the memory 210 to the memory element of the FIFO circuit 212.

[0080] In an embodiment, the calculated impact duration value is stored in the same FIFO entry as the maximum acceleration norm value of the FIFO circuit 212. In an embodiment, the values ​​stored in the memory element of the FIFO circuit 212 or in a register accessible by the processor 102 are stored in an integer format (e.g., two's complement, sign magnitude, etc.), half precision (i.e., 16-bit), single precision (i.e., 32-bit), or double precision (i.e., 64-bit) floating point format.

[0081] At step 312, the FSM circuit 202 in the third state 506 asserts a third interrupt at time t4 after a first period of time (e.g., a delay) by transitioning the value of the interrupt signal 404 from a logic low state to a logic high state within a set amount of time. In an embodiment, the first period of time is equal to a set number of samples (e.g., 1 to 5), or a time interval (e.g., 2 to 10 milliseconds). In an embodiment, the delay value may be configured by the processor 102 during a sensor startup sequence while configuring the sensor 200.

[0082] The third interrupt indicates to the processor 102 that the maximum acceleration norm value is copied into the memory element of the FIFO circuit 212. In addition, the FSM circuit 202 transitions from the third state 506 to the fourth state 508.

[0083] In an embodiment, the third value (e.g., 0x00) is recorded in a register of the sensor 200 that previously stored the first value and the second value just before the third interrupt was generated. In response to the processor 102 receiving the third interrupt, the processor 102 accesses the same register, which now includes the third value, indicating to the processor 102 that the trigger from the interrupt signal 404 indicates to the processor 102 a signal to disable the maximum acceleration norm value from being stored in the memory element of the FIFO circuit 212, as described in detail in step 316.

[0084] At step 316, the processor 102, in response to receiving the third interrupt, disables storing the maximum acceleration norm value in the memory element of the FIFO circuit 212. In an embodiment, at step 314, the processor 102 reads the value stored in the FIFO circuit 212 after receiving the third interrupt. In other embodiments, at step 314, the processor 102 reads the value stored in the FIFO circuit 212 after the processor 102 disables storing the maximum acceleration norm value in the memory element of the FIFO circuit 212.

[0085] In an embodiment, at step 316, the processor 102 reads a maximum acceleration norm value; an impact duration value; an acceleration value; a gyroscope value; a mean, variance, energy, maximum value, minimum value, peak-to-peak value, or any combination thereof of any accelerometer or gyroscope axis or accelerometer or gyroscope norm during a period between the first interrupt and the second interrupt stored in the FIFO circuit 212.

[0086] In an embodiment, the enabling and disabling of the memory element of the FIFO circuit 212 that allows the maximum acceleration norm value (and other data) to be transferred to the FIFO circuit 212 is performed internally (i.e., autonomously) within the sensor 200 and without intervention by the processor 102. In such an embodiment, the processor 102 will access the sensor 200 to access the data (e.g., the maximum acceleration norm value, the impact duration, the accelerometer data, the gyroscope data, etc.) based on a trigger event generated by the FIFO circuit 212 (e.g., a new sample is available in the memory element of the FIFO circuit, the number of samples stored in the memory element of the FIFO circuit reaches a selected user threshold, the memory element of the FIFO circuit is full, etc.).

[0087] In an embodiment where the enabling and disabling of the memory element of the FIFO circuit 212 that allows the maximum acceleration norm value to be transmitted to the FIFO circuit 212 is performed internally within the sensor 200 or a dedicated register replaces the FIFO circuit 212, steps 310, 312, and 316 become optional. One or more values ​​stored in the memory 210 (e.g., the maximum acceleration norm value) are retained or transferred to another register accessible to the processor 102. After the second interrupt is generated, the accessible stored one or more values ​​(e.g., the maximum acceleration norm value) are not modifiable. After the fourth interrupt is generated at step 320, the one or more values ​​are reset and modified.

[0088] At step 318, the FSM circuit 202 in the fourth state 508 asserts a fourth interrupt at time t5 by transitioning the value of the interrupt signal 404 from a logic low state to a logic high state within a set amount of time after a second period of time (e.g., a delay). In addition, the FSM circuit 202 transitions from the fourth state 508 to the fifth state 510. In an embodiment, the second period of time is equal to a set number of samples (e.g., 1500 samples), or a time interval (e.g., three seconds). In an embodiment, the second delay value may be configured by the processor 102 during a sensor startup sequence while configuring the sensor 200.

[0089] In an embodiment, the fourth value (e.g., 0x03) is recorded in a register of the sensor 200 that previously stored the first value, the second value, and the third value just before the fourth interrupt was generated. In response to the processor 102 receiving the fourth interrupt, the processor 102 accesses the same register, which now includes the fourth value, indicating to the processor 102 that the trigger from the interrupt signal 404 indicates the start of the next impact event detection (i.e., the reset of the maximum acceleration norm value and the restart of the process), as described in detail in step 320.

[0090] At step 320, the FSM circuit 202 in the fifth state 510 resets the maximum acceleration norm value stored in the memory 210 and transitions to the first state 502. The fourth interrupt indicates to the reset processor 102 that detection for the next impact has started.

[0091] In an embodiment, the first delay and the second delay are set so that the duration between time t3 and time t5 is equal to three seconds. The configurable delay value allows the processor to have enough time to enable the FIFO circuit device 212 and wait for the shock intensity value to be copied to the FIFO circuit device 212 memory before disabling the FIFO circuit device 212, and allows the processor to obtain sensor data (e.g., maximum acceleration norm value, shock duration, accelerometer data, gyroscope data, etc.) within the desired time for further calculation.

[0092] Note that Figure 4 , the maximum acceleration norm value within times t3 and t5 is greater than the maximum acceleration norm value between times t1 and t3 or between times t6 and t8, however, the value corresponding to this acceleration norm calculation is discarded because it occurs during the delay between the second interrupt and the fourth interrupt.

[0093] Embodiments of the present disclosure provide a low-power, autonomously operated sensor that can detect impact events and signal the impact event to an application processor of a host device. Advantageously, the application processor no longer needs to read and process accelerometer data to calculate the maximum acceleration norm value or the duration of the impact event. Therefore, communication between the application processor and the sensor is kept to a minimum, which allows the application processor to focus on other tasks or enter a sleep mode (i.e., a low-power setting). Since the impact measurements are calculated in a low-power domain (e.g., running entirely in hardware) and independently of the application processor in the host device, system efficiency is improved. In addition, since the impact data (e.g., impact duration, maximum impact intensity, etc.) is available to the application processor, the host device can perform additional calculations as needed.

[0094] Note that all steps outlined in the flowchart of the method disclosed herein are not necessarily required and may be optional. In addition, changes to the arrangement of steps, deletion of one or more steps and path connections, and addition of steps and path connections are similarly considered.

[0095] A first aspect relates to a sensor for detecting an impact event. The sensor includes a machine learning core configured to continuously calculate an acceleration norm value based on a change in velocity of an accelerometer along one or more axes. The sensor additionally includes a finite state machine circuit configured to compare the continuously calculated acceleration norm value with a first threshold value, and in response to the continuously calculated acceleration norm value being greater than the first threshold value, generate a first interrupt signal. The finite state machine circuit is also configured to compare the continuously calculated acceleration norm value with a second threshold value, and in response to the continuously calculated acceleration norm value being less than or equal to the second threshold value after the first interrupt signal is generated, generate a second interrupt signal, the second threshold value being less than the first threshold value. The machine learning core is also configured to calculate a maximum acceleration norm value between the first interrupt signal and the second interrupt signal, and store the maximum acceleration norm value in a register. An application processor of a host device reads the maximum acceleration norm value after receiving the second interrupt signal from the sensor. The maximum acceleration norm value is reset after a delay after the second interrupt signal is generated.

[0096] In a first implementation form of the sensor according to the first aspect itself, the finite state machine circuit is further configured to calculate a shock event duration corresponding to the generation of the first interrupt signal and the generation of the second interrupt signal, and store the shock event duration in a register.

[0097] In a second implementation form of the sensor according to the first aspect per se or any previous implementation form of the first aspect, the application processor reads the maximum acceleration norm value and the impact event duration from the sensor.

[0098] In a third implementation form of the sensor according to the first aspect per se or any previous implementation form of the first aspect, the acceleration norm value is calculated using a Euclidean norm.The maximum acceleration norm value is calculated using a recursive function.

[0099] In a fourth implementation form of the sensor according to the first aspect itself or any previous implementation form of the first aspect, the sensor further comprises a first-in-first-out (FIFO) circuit. The maximum acceleration norm value is copied to a memory element of the FIFO circuit in response to the second interrupt signal being generated. The application processor accesses the memory element of the FIFO circuit to read the maximum acceleration norm value.

[0100] In a fifth implementation form of the sensor according to the first aspect itself or any previous implementation form of the first aspect, the delay is a first delay. The finite state machine circuit is further configured to generate a third interrupt signal after a second delay, the second delay being less than the first delay. The third interrupt signal indicates to the application processor that the maximum acceleration norm value is copied to the memory element of the FIFO circuit.

[0101] In a sixth implementation form of the sensor according to the first aspect itself or any previous implementation form of the first aspect, the finite state machine circuit is also configured to generate a first delay after the second interrupt signal is generated, and after the first delay, generate a fourth interrupt signal to indicate that the maximum acceleration norm value is reset and the sensor is monitoring the next impact event.

[0102] A second aspect relates to a system including a sensor. The sensor includes a machine learning core configured to continuously calculate an acceleration norm value based on a change in velocity of an accelerometer along one or more axes. The sensor additionally includes a finite state machine circuit configured to compare the continuously calculated acceleration norm value with a first threshold value, and in response to the continuously calculated acceleration norm value being greater than the first threshold value, generate a first interrupt signal. The finite state machine circuit is also configured to compare the continuously calculated acceleration norm value with a second threshold value, and in response to the continuously calculated acceleration norm value being less than or equal to the second threshold value after the first interrupt signal is generated, generate a second interrupt signal, the second threshold value being less than the first threshold value. The machine learning core is also configured to calculate a maximum acceleration norm value between the first interrupt signal and the second interrupt signal, and store the maximum acceleration norm value in a register. An application processor of the host device reads the maximum acceleration norm value after receiving the second interrupt signal from the sensor. The maximum acceleration norm value is reset after a delay after the second interrupt signal is generated.

[0103] In a first implementation form of the system according to the second aspect per se, the sensor operates autonomously with respect to the application processor.

[0104] In a second implementation form of the system according to the second aspect per se or any previous implementation form of the second aspect, the application processor is configured to calculate an impact event duration corresponding to a duration between receipt of the first interrupt signal and receipt of the second interrupt signal.

[0105] In a third implementation form of the system according to the second aspect per se or any previous implementation form of the second aspect, the acceleration norm value (n) is calculated using the following equation: where x, y and z are different components of the acceleration vector, where α corresponds to a weight factor for the x direction, β corresponds to a weight factor for the y direction, and γ corresponds to a weight factor for the z direction. The weight factors are configurable.

[0106] In a fourth implementation form of the system according to the second aspect itself or any previous implementation form of the second aspect, the sensor further comprises a first-in-first-out (FIFO) circuit. The maximum acceleration norm value is copied to a memory element of the FIFO circuit in response to the second interrupt signal being generated. The application processor is configured to access the memory element of the FIFO circuit to read the maximum acceleration norm value.

[0107] In a fifth implementation form of the system according to the second aspect itself or any previous implementation form of the second aspect, the delay is a first delay. The finite state machine circuit is further configured to generate a third interrupt signal after the second delay, the second delay being less than the first delay, the third interrupt signal indicating to the application processor that the maximum acceleration norm value is copied to the memory element of the FIFO circuit.

[0108] In a sixth implementation form of the system according to the second aspect itself or any previous implementation form of the second aspect, the finite state machine circuit is also configured to generate a first delay after the second interrupt signal is generated, and after the first delay, generate a fourth interrupt signal to indicate that the maximum acceleration norm value is reset and the sensor is monitoring the next impact event.

[0109] The third aspect relates to a method for detecting an impact event. The method includes: a sensor in a host device continuously measures acceleration values ​​along multiple directions; the sensor continuously calculates acceleration norm values ​​based on the acceleration values; the sensor compares the continuously calculated acceleration norm value with a first threshold value, and generates a first interrupt signal in response to the continuously calculated acceleration norm value being greater than the first threshold value; the sensor compares the continuously calculated acceleration norm value with a second threshold value, and generates a second interrupt signal in response to the continuously calculated acceleration norm value being less than or equal to the second threshold value after the first interrupt signal is generated, and the second threshold value is less than the first threshold value; the sensor calculates a maximum acceleration norm value between the first interrupt signal and the second interrupt signal; the maximum acceleration norm value is stored in a register of the sensor, wherein an application processor of the host device reads the maximum acceleration norm value after receiving the second interrupt signal from the sensor; and after a delay after the second interrupt signal is generated, the maximum acceleration norm value is reset.

[0110] In a first implementation form of the method according to the third aspect itself, the method also includes: calculating, by a sensor, a shock event duration corresponding to the generation of a first interrupt signal and a second interrupt signal; and storing the shock event duration in a register, wherein an application processor reads the shock event duration and the maximum acceleration norm value from the sensor.

[0111] In a second implementation form of the method according to the third aspect per se or any previous implementation form of the third aspect, the acceleration norm value is calculated using a Euclidean norm. The maximum acceleration norm value is calculated using a recursive function.

[0112] In a third implementation form of the method according to the third aspect itself or any previous implementation form of the third aspect, the method further comprises: in response to the second interrupt signal being generated, copying the maximum acceleration norm value to a memory element of a FIFO circuit in the sensor. The application processor accesses the memory element of the FIFO circuit to read the maximum acceleration norm value.

[0113] In a fourth implementation form of the method according to the third aspect itself or any previous implementation form of the third aspect, the delay is a first delay. The method also includes: generating a third interrupt signal after a second delay, the second delay being less than the first delay, the third interrupt signal indicating to the application processor that the maximum acceleration norm value is copied to the memory element of the FIFO circuit.

[0114] In a fifth implementation form of the method according to the third aspect itself or any previous implementation form of the third aspect, the method also includes: generating a first delay after the second interrupt signal is generated; and generating a fourth interrupt signal after the first delay to indicate to the application processor that the maximum acceleration norm value is reset and the sensor is monitoring the next impact event.

[0115] Although the description has been described in detail, it should be understood that various changes, substitutions and modifications can be made without departing from the spirit and scope of the present disclosure as defined by the appended claims. The same elements are represented by the same reference numerals in different figures. In addition, the scope of the present disclosure is not intended to be limited to the specific embodiments described herein, because those of ordinary skill in the art can easily understand from the present disclosure that the processes, machines, products, material compositions, devices, methods or steps currently existing or to be developed later can perform functions substantially the same as the corresponding embodiments described herein or achieve substantially the same results as them. Therefore, the appended claims are intended to include such processes, machines, products, material compositions, devices, methods or steps within their scope.

[0116] The specification and drawings are therefore to be regarded as illustrative only of the present disclosure as defined by the appended claims, and are intended to cover any and all modifications, permutations, combinations or equivalents within the scope of the present disclosure.

Claims

1. A sensor for detecting an impact event, the sensor comprising: a machine learning core configured to continuously calculate acceleration norm values ​​based on changes in velocity along one or more axes from an accelerometer; as well as A finite state machine circuit, wherein the finite state machine circuit is configured to: comparing the continuously calculated acceleration norm value with a first threshold, and generating a first interrupt signal in response to the continuously calculated acceleration norm value being greater than the first threshold, and comparing the continuously calculated acceleration norm value with a second threshold value, and generating a second interrupt signal in response to the continuously calculated acceleration norm value being less than or equal to the second threshold value after the first interrupt signal is generated, the second threshold value being less than the first threshold value, The machine learning core is further configured to calculate a maximum acceleration norm value between the first interrupt signal and the second interrupt signal, and store the maximum acceleration norm value in a register, wherein the application processor of the host device reads the maximum acceleration norm value after receiving the second interrupt signal from the sensor, and Wherein the maximum acceleration norm value is reset after a delay after the second interrupt signal is generated. 2 . The sensor of claim 1 , wherein the finite state machine circuit is further configured to calculate a shock event duration corresponding to the generation of the first interrupt signal and the generation of the second interrupt signal, and store the shock event duration in the register. 3 . The sensor of claim 2 , wherein the application processor reads the maximum acceleration norm value and the impact event duration from the sensor. 4 . The sensor of claim 1 , wherein the acceleration norm value is calculated using a Euclidean norm, and wherein the maximum acceleration norm value is calculated using a recursive function.

5. The sensor of claim 1 , further comprising a first-in-first-out (FIFO) circuit, wherein the maximum acceleration norm value is copied to a memory element of the FIFO circuit in response to the second interrupt signal being generated, and wherein the application processor accesses the memory element of the FIFO circuit to read the maximum acceleration norm value.

6. The sensor of claim 5 , wherein the delay is a first delay, wherein the finite state machine circuit is further configured to generate a third interrupt signal after a second delay, the second delay being less than the first delay, the third interrupt signal indicating to the application processor that the maximum acceleration norm value is copied to the memory element of the FIFO circuit.

7. The sensor of claim 6, wherein the finite state machine circuit is further configured to: After the second interrupt signal is generated, the first delay is generated, and After the first delay, a fourth interrupt signal is generated to indicate that the maximum acceleration norm value is reset and the sensor is monitoring for a next impact event.

8. A system comprising: A sensor, the sensor comprising: a machine learning core configured to continuously calculate acceleration norm values ​​based on changes in velocity along one or more axes from an accelerometer, and A finite state machine circuit, wherein the finite state machine circuit is configured to: comparing the continuously calculated acceleration norm value with a first threshold value, and generating a first interrupt signal in response to the continuously calculated acceleration norm value being greater than the first threshold value, and comparing the continuously calculated acceleration norm value with a second threshold value, and generating a second interrupt signal in response to the continuously calculated acceleration norm value being less than or equal to the second threshold value after the first interrupt signal is generated, the second threshold value being less than the first threshold value, The machine learning core is further configured to calculate a maximum acceleration norm value between the first interrupt signal and the second interrupt signal, and store the maximum acceleration norm value in a register, and wherein the maximum acceleration norm value is reset after a delay after the second interrupt signal is generated, and An application processor is coupled to the sensor, the application processor being configured to read the maximum acceleration norm value after receiving the second interrupt signal from the sensor.

9. The system of claim 8, wherein the sensor operates autonomously with respect to the application processor. 10 . The system of claim 8 , wherein the application processor is configured to calculate a shock event duration corresponding to a duration between receipt of the first interrupt signal and receipt of the second interrupt signal.

11. The system of claim 8, wherein the acceleration norm value n is calculated using the following equation: wherein x, y and z are different components of the acceleration vector, wherein α corresponds to a weighting factor for the x direction, β corresponds to a weighting factor for the y direction, and γ corresponds to a weighting factor for the z direction, and wherein the weighting factors are configurable.

12. The system of claim 8, wherein the sensor further comprises a first-in-first-out (FIFO) circuit, wherein the maximum acceleration norm value is copied into a memory element of the FIFO circuit in response to the second interrupt signal being generated, and wherein the application processor is configured to access the memory element of the FIFO circuit to read the maximum acceleration norm value.

13. The system of claim 12 , wherein the delay is a first delay, wherein the finite state machine circuit is further configured to generate a third interrupt signal after a second delay, the second delay being less than the first delay, the third interrupt signal indicating to the application processor that the maximum acceleration norm value is copied to the memory element of the FIFO circuit.

14. The system of claim 13, wherein the finite state machine circuit is further configured to: After the second interrupt signal is generated, the first delay is generated, and After the first delay, a fourth interrupt signal is generated to indicate that the maximum acceleration norm value is reset and the sensor is monitoring for a next impact event.

15. A method for detecting an impact event, the method comprising: The acceleration values ​​along multiple directions are continuously measured by sensors in the host device; By the sensor, continuously calculating an acceleration norm value based on the acceleration value; By the sensor, comparing the continuously calculated acceleration norm value with a first threshold value, and generating a first interrupt signal in response to the continuously calculated acceleration norm value being greater than the first threshold value; comparing, by the sensor, the continuously calculated acceleration norm value with a second threshold value, and generating a second interrupt signal in response to the continuously calculated acceleration norm value being less than or equal to the second threshold value after the first interrupt signal is generated, the second threshold value being less than the first threshold value; calculating, by the sensor, a maximum acceleration norm value between the first interrupt signal and the second interrupt signal; storing the maximum acceleration norm value in a register of the sensor, wherein an application processor of the host device reads the maximum acceleration norm value after receiving the second interrupt signal from the sensor; and The maximum acceleration norm value is reset after a delay after the second interrupt signal is generated.

16. The method according to claim 15, further comprising: calculating, by the sensor, a duration of an impact event corresponding to the generation of the first interrupt signal and the generation of the second interrupt signal; as well as The impact event duration is stored in the register, wherein the application processor reads the impact event duration and the maximum acceleration norm value from the sensor.

17. The method of claim 15, wherein the acceleration norm value is calculated using a Euclidean norm, and wherein the maximum acceleration norm value is calculated using a recursive function.

18. The method according to claim 15, further comprising: In response to the second interrupt signal being generated, the maximum acceleration norm value is copied to a memory element of a FIFO circuit in the sensor, and wherein the application processor accesses the memory element of the FIFO circuit to read the maximum acceleration norm value.

19. The method of claim 18, wherein the delay is a first delay, the method further comprising: A third interrupt signal is generated after a second delay, the second delay being less than the first delay, the third interrupt signal indicating to the application processor that the maximum acceleration norm value is copied into the memory element of the FIFO circuit.

20. The method according to claim 19, further comprising: generating the first delay after the second interrupt signal is generated; as well as After the first delay, a fourth interrupt signal is generated to indicate to the application processor that the maximum acceleration norm value is reset and the sensor is monitoring for a next impact event.

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